Assessing the Value of Online Interactions

Ministers also unveil the first 7 apprenticeship units

The post Apprenticeships purge: Team leader and chartered manager among 16 axed standards first appeared on FE Week.

Knowledge silos rarely show up as a single, dramatic event. They creep in quietly as teams grow, tools multiply, and work becomes more specialized—until one day, it’s clear that everyone is operating with a different version of the truth. Fortunately, you have the power to defeat this behemoth for good. But to eradicate your internal knowledge silos, you first need to understand what they are, why they develop, and the specific harm they’re causing your business.

In this guide, we’ll walk through what knowledge silos look like in practice, the warning signs that your organization is too siloed, and their tangible impact on performance, culture, and the customer experience. From there, we’ll dig into practical strategies to break them down so your teams can move faster, stay aligned, and make better decisions together.

What Is a Knowledge Silo?

A knowledge silo is a situation in which one individual or team holds information that’s not shared or distributed with others. Instead of communicating and collaborating across the organization, each team or department is working in isolation.

A 2024 study in Data Management cited that 68% of organizations now cite data or knowledge silos as a top concern, and that number is growing year over year. When left unchecked, silos can negatively impact operations and the customer experience by slowing decision-making, driving inconsistent messaging, and hiding critical insights from the people who need them most.

Types of silos include:

• Department silos. These occur when each different department or line of business has its own systems or tools for sharing information and fails to effectively communicate this information outside of these systems. Important context stays locked in team-specific platforms, and cross-functional work turns into a game of telephone.
• Buyer’s journey silos. This problem occurs when departments aren’t in communication regarding what stage a customer or prospect is in their buyer journey. Sales may end up trying to sell a customer a product they already have, or giving them content that is irrelevant to their current needs. Either way, the customer becomes confused and frustrated.
• Channel silos. These occur when there’s a disconnect between the teams and technologies supporting different customer channels, such as phone, chat, and social media. They can lead to brand inconsistencies and mixed messages for customers. 

Think of silos like a series of windowless rooms divided by soundproof, concrete walls. Each department is stationed within one of these rooms. They collect data and use it to drive decisions but cannot transmit those insights to other departments. So, when the time comes to solve more complex organizational problems or align on a common goal, chaos ensues. No one understands what other departments are responsible for, multiple individuals are unknowingly doing the same work, everyone relies on different data, and communications are unclear.

What Causes Knowledge Silos?

Knowledge silos typically develop silently due to inaction rather than deliberate isolation. Usually, they form when organizations fail to support the transfer of information across teams and departments. It’s not due to a single-point-of-failure, either. In most cases, silos are the byproduct of several avoidable mistakes, which include the following:

1. Poor Cross-Team Communication and Collaboration

When teams don’t have clear, consistent ways to share information, they fall back on communicating only within their own group. When teams don’t have clear, consistent ways to share information, they fall back on communicating only within their own group. In fact, a Harvard Business survey on collaboration found that roughly 67% of collaboration failures are linked directly to organizational silos, not to lack of effort or tools. Poor collaboration will lead to duplicate work, conflicting priorities, and different versions of the right answer across the organization.

2. Misaligned Incentives and KPI Structures

Silos also thrive when teams are rewarded for local wins instead of shared outcomes. If each department is measured on its own narrow KPIs, people naturally prioritize hitting their numbers over sharing information. In practice, this can look like sales hanging onto prospect insights, marketing guarding campaign performance data, or support keeping customer feedback locked in their own systems.

3. Inadequate Onboarding of New Hires

When new employees join a team, their onboarding experience lays the foundation for their performance in their new role. If information is poorly documented and difficult to access, they’ll rely only on the people around them—thus establishing the silo. As the team grows, so do the walls around the silo. 

As these employees ramp up, they unintentionally reinforce the same narrow information paths that kept them in the dark to begin with. Instead of all employees using the same well-documented and easily accessible knowledge base, each team will rely on its own disparate tools and methods.

4. Lack of a Clear Knowledge Management Strategy

When there’s no clear strategy for how knowledge should be captured, organized, and shared, information siloes quickly form across tools, teams, and channels. Documents, decisions, and insights end up buried in inboxes, chat threads, shared drives, and local folders, with no single source of truth or consistent structure. As a result, employees spend more time hunting for answers than actually applying what the organization already knows. 

Different teams will eventually develop their own versions of key information, reinforcing silos and making it harder to coordinate around shared goals. In practice, a missing or weak knowledge management strategy doesn’t just create clutter; it quietly hardcodes silos into how people work every day.

Why Are Knowledge Silos Harmful?

Knowledge silos don’t just create minor inconveniences; they quietly undermine how your organization thinks, operates, and grows. When information is fragmented across teams and tools, it becomes harder to make smart decisions, innovate, and deliver a consistent customer experience. This friction will show up everywhere: projects slow down, initiatives stall, and employees feel like they’re constantly flying blind. Left unaddressed, silos turn into a structural problem that impacts performance and culture.

Here are some reasons why knowledge silos are harmful to your organization. 

Slower, Less Informed Decision-Making

When information lives in silos, leaders and frontline employees rarely see the full picture before making a decision. Teams end up making decisions based on partial, outdated, or secondhand information instead of shared, verified data. That slows everything down: more status checks, more alignment meetings, and more rework when a missing detail surfaces later. These decisions will become more about who has the loudest voice or the most local context than what is actually best for the business.

Missed Opportunities for Innovation and Growth

Innovation almost always happens at the intersections between teams, disciplines, and perspectives. When knowledge is trapped in silos, those intersections never form. 

Marketing doesn’t see the nuanced feedback support is hearing from customers, the product doesn’t get visibility into emerging market patterns, and sales can’t easily feed real-world objections back into messaging or roadmap decisions. The result is a stream of missed chances to launch better offerings, refine processes, or enter new markets because the right people never see the right insights at the right time.

Employee Frustration

Knowledge silos make everyday work harder than it needs to be. Employees waste time hunting for answers, pinging the same go-to people, or recreating work because they can’t find a previous version. That constant friction sends a clear message: the organization doesn’t value their time or make it easy for them to do their best work. Frustrations will turn into disengagement and cynicism, especially when people see other teams hoarding information or making inconsistent decisions.

They Cost Time and Money

Knowledge silos can cost you time and money. When tools, systems, and repositories aren’t integrated or centralized, teams lose hours every month just trying to locate, verify, and reconcile information. 

In larger environments like contact centers, these inefficiencies compound quickly: every extra minute an agent spends searching for the right answer instead of resolving an issue turns into hard labor costs. As operational efficiency declines, overall employee engagement drops, and disengaged employees are more likely to make mistakes and deliver uneven service, leading to a poor customer experience. 

4 Signs Your Organization Is Too Siloed

When silos become the default way of working, the impact doesn’t stay hidden for long—it shows up in the way your teams operate and how your customers experience your brand. You will start to see patterns: projects dragging on for no clear reason, teams unintentionally stepping on each other’s toes, and employees feeling disconnected. These are symptoms of information getting trapped instead of being shared.

The following signs can help you diagnose whether siloed behavior has taken root in your organization and where it’s doing the most damage.

1. A Negative Customer Experience

A disjointed customer experience is one of the most obvious signs your company has become too siloed. For example, if customers complain that their experience with a product or service doesn’t live up to what a sales representative or marketing message promised, or if they receive conflicting information from different customer service representatives, silos may be at the root of the problem.

You might also notice longer resolution times because agents have to dig through multiple systems or ping other teams for answers. Over time, customers start to feel like they’re dealing with several different companies instead of one unified brand, and their trust and loyalty erode.

2. Frequent Task Duplication Across Departments

If you regularly discover that multiple teams are creating similar reports, building overlapping dashboards, or drafting nearly identical playbooks, you’re seeing silos in action. Each group works from its own limited view of what exists, so they reinvent the wheel rather than reusing or improving shared assets.

This duplication isn’t just inefficient—it also leads to conflicting versions of official documents, data, or information. When no one is sure which version to use, decisions slow down, and internal debates increase, all because the organization lacks a clear, shared knowledge base.

3. Low Employee Engagement

Siloed environments make it hard for employees to feel connected to a bigger mission. When people only see their team’s slice of the work and don’t understand how it fits into broader objectives, their motivation naturally dips. You may hear comments like “I have no idea what that other team does” or “No one tells us what’s going on.” That sense of disconnection can turn into frustration and disengagement: fewer ideas, less initiative, and a stronger tendency to do the bare minimum within a narrow job description.

4. Slow Cross-Department Approvals

If any project that spans more than one department stalls, that’s a strong indicator of silos. Simple approvals can require long email chains, multiple meetings, and repeated back-and-forth because each team is operating with different priorities and incomplete information.

Deadlines slip, momentum dies, and people start planning around delays as if they’re unavoidable. This will train teams to avoid cross-functional work altogether, reinforcing the very silos that caused the slowdown in the first place.

How to Break Down Knowledge Silos

Knowledge silos don’t disappear on their own; they have to be phased out of your organization. That means changing both behavior and infrastructure: how people share information, how work gets documented, and where knowledge actually lives. The goal isn’t to eliminate specialization, but to ensure insights can move freely across teams rather than get trapped within them.

Below are some methods to destroy knowledge silos, so your organization can prevent the damage of silos before they appear.

Build a Knowledge Sharing Culture 

Technology can support knowledge sharing, but it can’t replace culture. If people don’t feel safe, rewarded, and expected to share what they know, silos will always creep back in. Start by making knowledge sharing a visible norm: highlight examples in all-hands meetings, recognize people who document and share insights, and have leaders model the behavior by openly sharing their own thinking and decisions. The goal is to move from knowledge is power to shared knowledge is how we win.

Standardize How Insights Are Documented

Even when teams want to share, inconsistent documentation makes it hard to actually use what others have created. Establish simple, organization-wide standards for capturing insights, decisions, and processes through research summaries, meeting notes, and playbooks. Keep the standards lightweight but consistent, so employees know where to find the context, key takeaways, and next steps. The more predictable your documentation is, the easier it is for knowledge to travel beyond a single team.

Strategize Your Communication

Instead of sharing everything with everyone, be selective and intentional about what gets broadcast widely. Focus on communicating decisions, big shifts in priorities, and context that changes how people should work, not every minor update. Work by sharing decisions in writing, recapping key points from meetings, and proactively tagging or looping in adjacent teams that might be affected. 

This doesn’t mean flooding everyone with noise, it means deliberately broadcasting the most important information to the audiences who need it, rather than assuming it will trickle out on its own. When in doubt, share one step earlier and one layer wider than you think is necessary so critical insights don’t stay trapped inside a single team.

Connect Knowledge in a Single, Searchable Platform

If your knowledge lives in ten different tools, you’re setting the stage for silos. Centralize critical institutional knowledge into a single, searchable platform like Bloomfire that’s easy for everyone to access. Make it the default home for source of truth content, and train teams to publish final versions there rather than burying them in email threads or local folders. When people know exactly where to look and can find trusted information in seconds, they’re far less likely to reinvent the wheel or retreat into team-specific knowledge silos.

Prevent Knowledge Silos to Expand Your Company’s Success

Ultimately, breaking down silos isn’t just an operational clean-up effort; it’s a growth strategy. Organizations that treat knowledge as a shared asset are better equipped to adapt, innovate, and scale without losing alignment. If you invest in the culture, processes, and systems that keep information moving, you’re not just preventing silos; you’re building a company that can learn faster than the challenges it faces.

Click to expand the infographic below:

Note: This post was originally published in 2018 under the title “Guide to Overcome Silos in the Workplace.” It was most recently expanded and updated in February 2026.

The post Knowledge Silos in the Workplace: What They Are and How to Overcome Them appeared first on Bloomfire.

Government gears up to tell employers which apprenticeships 'aren't appropriate for public funding'

The post Management apprenticeships on the chopping block, minister confirms first appeared on FE Week.

[cover image by akshayapatra on Pixabay]

by Cindy Nebel

What is the spacing effect? Let’s start with a little quiz shall we?

Quiz

Which of the following involves spacing?

A.      A new hire has a week-long onboarding. They learn the company’s pricing tiers on Monday morning. They see the same pricing chart again in a short quiz on Wednesday and again in a role-play activity on Friday.

B.      Company A has traditionally had an 8-hour orientation on the first day of the month for all new hires. They are trying out a new system where they instead do 8 1-hour sessions spread out over the first two weeks of the month.

C.     An instructor switches from teaching Chapter 1 for a week and then Chapter 2 the next week to instead teaching one concept from each chapter and then coming back and teaching the next concept from each chapter.

D.     A music teacher lets students practice on their instruments for only half of the class before switching to reading music. The next class they do the same thing.

E.      Instead of studying all five chapters on Thursday night, a student studies Chapter 1 on Sunday, Chapter 2 on Monday, etc. in order to prepare for the exam on Friday.

F.      All of the above

Does this feel like a trick question to you? It might! Read on to find out the correct answer!

New Study

Today I’m reviewing a study that recently came out looking at how students choose to use spacing in their own study and how it relates to their performance (1). Importantly, the researchers recognized a limitation in the way students were being asked about their study habits in previous research on spacing. Here are common ways that students are asked about spacing their study habits:

Which of the following statements best describes when your studying occurred during the weeks leading up to this exam?

A.      The majority of my studying occurred 1-2 days before the exam.
B.      The majority of my studying occurred during the 7 days before the exam.
C.     The majority of my studying occurred more than a week before the exam.
D.     My studying was pretty evenly spread out across the weeks.

This question is aimed at looking at cramming behavior in particular and research DOES show that students tend to cram and do most their studying just before any given exam (2). However, what students are doing when they do spread out that study matters quite a lot too.

The true spacing effect, as measured in experimental research, is a spaced review effect. That is, the same material is reviewed multiple times, spread out over sessions. It is not that everything is viewed once, spread out over time.

In this study, they added a new question:

Please rate your agreement with the following statement: When studying different concepts for this exam, I made sure that I studied the very same concepts more than once.

This question is targeted at the review part of spaced review, not just the distance between study sessions.

Results

Unsurprisingly, most students crammed in this study, but there was actually quite a bit of variability as to whether their cramming sessions involved reviewing the material more than once.

When looking at the exam scores, the degree to which students spread out their study vs. crammed wasn’t related much at all, BUT the degree to which they reviewed concepts was! The more that concepts were reviewed, the better students performed, even when controlling for total study time. That means some students spent the same amount of time studying, but the ones who cycled through material instead of studying one thing at a time in big chunks did better on the exam.

Quiz Results

Based on the literature on the spacing effect and the study described here, the correct answer to the question at the top of this post is….

A and D only

In B, C, and E, new material is spaced out over time, but it isn’t reviewed. That’s not going to result in a spacing effect.

There might be some other benefits to spacing out the acquisition of new knowledge. As we take in more and more information, we can become cognitively fatigued and experience something called proactive interference. Taking a break and coming back fresh may help us to encode that information more effectively than trying to sit through that 8-hour training session. But this isn’t a spacing effect. It’s about the importance of taking breaks.

Bottom Line

I found this study to be an important one to share because there is often a conflation between spreading out new information and spacing out review of old information. In order to maximize student retention, we want to make sure we are revisiting taught information instead of talking about it once and moving along to the next topic. This can be done through structured review sessions, but also through delayed homework (think about assigning Chapter 1 homework while teaching Chapter 2 in class) or retrieval practice opportunities (think bell work that covers old material or cumulative low-stakes quizzes). While it might be beneficial to cover material in smaller chunks, spacing is about making sure you come back to those chunks over time.

---

References:

(1) Malain, E. D., & Hartwig, M. K. (2026). Self-reported spaced study: Associations with college students’ grades and self-regulation. Journal of Experimental Psychology: Applied. Advance online publication. https://dx.doi.org/10.1037/xap0000562

(2) Hartwig, M. K., & Dunlosky, J. (2012). Study strategies of college students: Are self-testing and scheduling related to achievement? Psychonomic Bulletin & Review, 19(1), 126–134. https://doi.org/10.3758/s13423-011- 0181-y

Saying no isn’t easy. A 2023 study found nearly six out of ten people struggle to turn down requests, and even more consider themselves people pleasers. According to Cornell University organizational psychologist Sunita Sah, social conditioning and group dynamics make resistance emotionally difficult. Sah’s expertise is in compliance — the often overwhelming tendency to relinquish autonomy to a…

Source

Deliberate Errors and Learning

“The stream of truth flows through the channels carved by its errors.”
Rabindranath Tagore

Teachers are trained to spot and correct mistakes. Errors are typically treated as signs of confusion, poor preparation, inattentiveness, or worse. But what if making mistakes — when done deliberately — could be an effective tool for learning? [Note: It’s not about productive failure!]

That’s the central claim of a recent study by Qiang, Ma, and Li (2025) – Learning from errors: Deliberate errors enhance learning – who found that deliberate errors, when used as a learning strategy, not only improve learner understanding but often outperform other methods like retrieval practice and, of course, rereading. In this post, I’ll do my best to walk you, the reader, through what deliberate errors are, why they work, how they relate to core theories of learning, and how teachers can apply them in the classroom.

A deliberate error is a plausible mistake a student makes on purpose — knowing it is incorrect — and then must correct. For example, a learner might write “Bats are birds” (they aren’t, they’re mammals) to deliberately encode a common misconception and then revise it. The key is that the student already knows the correct answer; they strategically generate the  error to highlight contrast and deepen processing.

This deceptively simple act engages the brain’s error-monitoring and correction mechanisms, forcing the learner to attend more carefully to what distinguishes correct from incorrect. In cognitive psychology, such an activity is called a generative learning strategy (Fiorella & Mayer, 2016) . It requires the learner to construct knowledge – here a self-explanation – and not just receive it.

The question that now arises is why such deliberate errors (might) work. Qiang and colleagues propose several theoretical explanations for the power of deliberate errors. One is based on the Knowledge Revision Components (KReC) framework (Kenedeou, 2024), which describes how learners update prior knowledge when presented with conflicting information. A deliberately made and immediately corrected error triggers a semantic conflict — a contradiction that demands resolution — which in turn strengthens memory encoding.

The strategy also aligns with the theory of self-explanation from Micki Chi and her colleagues (1989): the idea that learners gain deeper understanding by explaining concepts to themselves. Generating an error and correcting it pushes the learner to ask, “Why is this wrong?” and “What makes the correct answer right?” These prompts force deeper cognitive engagement. BTW: Craig Barton has a website devoted to this idea of Diagnostic Questions (DQs) and also has been publishing weekly examples  in his Eedi Newsletter recently.

Finally, the approach embodies the principle of desirable difficulties (Bjork, 1994) — learning conditions that feel harder in the moment but lead to stronger retention in the long term. Deliberate errors introduce complexity and discomfort, which paradoxically make learning more effective.

The Research

Across three experiments with over 470 undergraduate students, Qiang et al. tested three learning conditions:

1. Restudy: rereading and copying definitions (open-book)
2. Retrieval practice: recalling definitions from memory (closed-book)
3. Deliberate errors: writing an incorrect version of a definition, followed by a correction (open-book)

In Experiment 1, learners were tested immediately after studying. Both retrieval practice and deliberate erring produced better recall than restudy, with no significant difference between them.

In Experiment 2, learners were tested after one week. Here, deliberate errors led to significantly better performance than both retrieval practice and restudy. Even in Experiment 3, where retrieval practice was boosted with feedback and extra trials, deliberate errors retained their long-term advantage.

Perhaps most revealing was a consistent pattern: students underestimated the value of deliberate errors. They perceived them as less effective than restudy or retrieval practice — a classic example of a metacognitive illusion⁶.

The findings align closely with the results we would expect from three established cognitive theories, namely:

• Generative learning, which emphasises that learners benefit from generating connections, inferences, and elaborations.
• Self-explanation, which improves learning by prompting learners to clarify, justify, and connect concepts.
• Desirable difficulties, which suggest that mental effort during learning enhances memory.

The 64,000 dollar question: How can you bring this strategy into your classroom?

If you intend to use this in the classroom, you first and foremost need to understand where it fits. Deliberate erring works after instruction, not before. In this way it is diametrically opposed to productive failure. Students need to already know the correct answer to create and correct a meaningful error.

Second, you need to design tasks that invite deliberate erring. Ask students to invent a believable but incorrect answer to a concept and then correct it.

Third, you also can choose to use it in group activities where you present deliberate errors for peer correction and discussion.

Finally, your feedback is really important. You need to talk explicitly about the why. Learners often avoid effortful strategies (pejoratively, the calculating student or as I dubbed them, the discipulus economicus) because they feel less effective. Show them the research. Let them reflect on what actually helps them remember. And let them experience it in their exams!

Yes, we can and do learn from our mistakes, especially when they’re deliberate.

Deliberate errors challenge our traditional beliefs about mistakes in learning. They show that error is not simply a sign of failure; that it can be a tool for learning. When used strategically, they help learners discriminate between similar ideas, activate related knowledge, and solidify what they know.

What Qiang, Ma, and Li offer is not simply a new learning strategy, but a new mindset (not in the Dweckian sense!): Mistakes can be designed and can be deliberate. In doing so, they bring together three very powerful ideas in cognitive science. Learning is deeper when it’s generative. It’s stronger when it’s explained. And it’s more durable when it’s difficult.

So let’s also teach learners how to use errors purposefully, reflectively, and productively. Because sometimes, the best way to get something right… might be to get it wrong first.

References

Fiorella, L., & Mayer, R. E. (2016). Learning as a generative activity. Cambridge University Press.

Kendeou, P. (2024). A theory of knowledge revision: The development of the KReC framework. Educational Psychology Review, 36(2), 44. https://doi.org/10.1007/s10648-024-09885-y

Chi, M. T. H. et al. (1989). Self-explanations: How students study and use examples. Cognitive Science, 13(2), 145–182. https://onlinelibrary.wiley.com/doi/epdf/10.1207/s15516709cog1302_1

Bjork, R.A. (1994). Memory and metamemory considerations in the training of human beings. In J. Metcalfe and A. Shimamura (Eds.), Metacognition: Knowing about knowing (pp. 185-205). MIT Press. https://www.researchgate.net/publication/305433736_Memory_and_Meta-memory_Considerations_in_the_Training_of_Human_Beings

“The stream of truth flows through the channels carved by its errors.”
Rabindranath Tagore

Teachers are trained to spot and correct mistakes. Errors are typically treated as signs of confusion, poor preparation, inattentiveness, or worse. But what if making mistakes — when done deliberately — could be one an effective tool for learning? [Note: It’s not about productive failure!]

That’s the central claim of a recent study by Qiang, Ma, and Li (2025) – Learning from errors: Deliberate errors enhance learning – who found that deliberate errors, when used as a learning strategy, not only improve learner understanding but often outperform other methods like retrieval practice and, of course, rereading. In this post, I’ll do my best to walk you, the reader, through what deliberate errors are, why they work, how they relate to core theories of learning, and how teachers can apply them in the classroom.

A deliberate error is a plausible mistake a student makes on purpose — knowing it is incorrect — and then must correct. For example, a learner might write “Bats are birds” (they aren’t, they’re mammals) to deliberately encode a common misconception and then revise it. The key is that the student already knows the correct answer; they strategically generate the  error to highlight contrast and deepen processing.

This deceptively simple act engages the brain’s error-monitoring and correction mechanisms, forcing the learner to attend more carefully to what distinguishes correct from incorrect. In cognitive psychology, such an activity is called a generative learning strategy (Fiorella & Mayer, 2016) . It requires the learner to construct knowledge – here a self-explanation – and not just receive it.

The question that now arises is why such deliberate errors (might) work. Qiang and colleagues propose several theoretical explanations for the power of deliberate errors. One is based on the Knowledge Revision Components (KReC) framework (Kenedeou, 2024), which describes how learners update prior knowledge when presented with conflicting information. A deliberately made and immediately corrected error triggers a semantic conflict — a contradiction that demands resolution — which in turn strengthens memory encoding.

The strategy also aligns with the theory of self-explanation from Micki Chi and her colleagues (1989): the idea that learners gain deeper understanding by explaining concepts to themselves. Generating an error and correcting it pushes the learner to ask, “Why is this wrong?” and “What makes the correct answer right?” These prompts force deeper cognitive engagement. BTW: Craig Barton has a website devoted to this idea of Diagnostic Questions (DQs) and also has been publishing weekly examples  in his Eedi Newsletter recently.

Finally, the approach embodies the principle of desirable difficulties (Bjork, 1994) — learning conditions that feel harder in the moment but lead to stronger retention in the long term. Deliberate errors introduce complexity and discomfort, which paradoxically make learning more effective.

The Research

Across three experiments with over 470 undergraduate students, Qiang et al. tested three learning conditions:

1. Restudy: rereading and copying definitions (open-book)
2. Retrieval practice: recalling definitions from memory (closed-book)
3. Deliberate errors: writing an incorrect version of a definition, followed by a correction (open-book)

In Experiment 1, learners were tested immediately after studying. Both retrieval practice and deliberate erring produced better recall than restudy, with no significant difference between them.

In Experiment 2, learners were tested after one week. Here, deliberate errors led to significantly better performance than both retrieval practice and restudy. Even in Experiment 3, where retrieval practice was boosted with feedback and extra trials, deliberate errors retained their long-term advantage.

Perhaps most revealing was a consistent pattern: students underestimated the value of deliberate errors. They perceived them as less effective than restudy or retrieval practice — a classic example of a metacognitive illusion⁶.

The findings align closely with the results we would expect from three established cognitive theories, namely:

• Generative learning, which emphasises that learners benefit from generating connections, inferences, and elaborations.
• Self-explanation, which improves learning by prompting learners to clarify, justify, and connect concepts.
• Desirable difficulties, which suggest that mental effort during learning enhances memory.

The 64,000 dollar question is: How can you bring this strategy into your classroom?

If you intend to use this in the classroom, you first and foremost need to understand where it fits. Deliberate erring works after instruction, not before. In this way it is diametrically opposed to productive failure. Students need to already know the correct answer to create and correct a meaningful error.

Second, you need to design tasks that invite deliberate erring. Ask students to invent a believable but incorrect answer to a concept and then correct it.

Third, you also can choose to use it in group activities where you present deliberate errors for peer correction and discussion.

Finally, your feedback is really important. You need to talk explicitly about the why. Learners often avoid effortful strategies (pejoratively, the calculating student or as I dubbed them, the discipulus economicus) because they feel less effective. Show them the research. Let them reflect on what actually helps them remember. And let them experience it in their exams!

Yes, we can and do learn from our mistakes, especially when they’re deliberate.

Deliberate errors challenge our traditional beliefs about mistakes in learning. They show that error is not simply a sign of failure; that it can be a tool for learning. When used strategically, they help learners discriminate between similar ideas, activate related knowledge, and solidify what they know.

What Qiang, Ma, and Li offer is not simply a new learning strategy, but a new mindset (not in the Dweckian sense!): Mistakes can be designed and can be deliberate. In doing so, they bring together three very powerful ideas in cognitive science. Learning is deeper when it’s generative. It’s stronger when it’s explained. And it’s more durable when it’s difficult.

So let’s also teach learners how to use errors purposefully, reflectively, and productively. Because sometimes, the best way to get something right… might be to get it wrong first.

References

Fiorella, L., & Mayer, R. E. (2016). Learning as a generative activity. Cambridge University Press.

Kendeou, P. (2024). A theory of knowledge revision: The development of the KReC framework. Educational Psychology Review, 36(2), 44. https://doi.org/10.1007/s10648-024-09885-y

Chi, M. T. H. et al. (1989). Self-explanations: How students study and use examples. Cognitive Science, 13(2), 145–182. https://onlinelibrary.wiley.com/doi/epdf/10.1207/s15516709cog1302_1

Bjork, R.A. (1994). Memory and metamemory considerations in the training of human beings. In J. Metcalfe and A. Shimamura (Eds.), Metacognition: Knowing about knowing (pp. 185-205). MIT Press. https://www.researchgate.net/publication/305433736_Memory_and_Meta-memory_Considerations_in_the_Training_of_Human_Beings

“The stream of truth flows through the channels carved by its errors.”
Rabindranath Tagore

Teachers are trained to spot and correct mistakes. Errors are typically treated as signs of confusion, poor preparation, inattentiveness, or worse. But what if making mistakes — when done deliberately — could be one an effective tool for learning? [Note: It’s not about productive failure!]

That’s the central claim of a recent study by Qiang, Ma, and Li (2025) – Learning from errors: Deliberate errors enhance learning – who found that deliberate errors, when used as a learning strategy, not only improve learner understanding but often outperform other methods like retrieval practice and, of course, rereading. In this post, I’ll do my best to walk you, the reader, through what deliberate errors are, why they work, how they relate to core theories of learning, and how teachers can apply them in the classroom.

A deliberate error is a plausible mistake a student makes on purpose — knowing it is incorrect — and then must correct. For example, a learner might write “Bats are birds” (they aren’t, they’re mammals) to deliberately encode a common misconception and then revise it. The key is that the student already knows the correct answer; they strategically generate the  error to highlight contrast and deepen processing.

This deceptively simple act engages the brain’s error-monitoring and correction mechanisms, forcing the learner to attend more carefully to what distinguishes correct from incorrect. In cognitive psychology, such an activity is called a generative learning strategy (Fiorella & Mayer, 2016) . It requires the learner to construct knowledge – here a self-explanation – and not just receive it.

The question that now arises is why such deliberate errors (might) work. Qiang and colleagues propose several theoretical explanations for the power of deliberate errors. One is based on the Knowledge Revision Components (KReC) framework (Kenedeou, 2024), which describes how learners update prior knowledge when presented with conflicting information. A deliberately made and immediately corrected error triggers a semantic conflict — a contradiction that demands resolution — which in turn strengthens memory encoding.

The strategy also aligns with the theory of self-explanation from Micki Chi and her colleagues (1989): the idea that learners gain deeper understanding by explaining concepts to themselves. Generating an error and correcting it pushes the learner to ask, “Why is this wrong?” and “What makes the correct answer right?” These prompts force deeper cognitive engagement. BTW: Craig Barton has a website devoted to this idea of Diagnostic Questions (DQs) and also has been publishing weekly examples  in his Eedi Newsletter recently.

Finally, the approach embodies the principle of desirable difficulties (Bjork, 1994) — learning conditions that feel harder in the moment but lead to stronger retention in the long term. Deliberate errors introduce complexity and discomfort, which paradoxically make learning more effective.

The Research

Across three experiments with over 470 undergraduate students, Qiang et al. tested three learning conditions:

1. Restudy: rereading and copying definitions (open-book)
2. Retrieval practice: recalling definitions from memory (closed-book)
3. Deliberate errors: writing an incorrect version of a definition, followed by a correction (open-book)

In Experiment 1, learners were tested immediately after studying. Both retrieval practice and deliberate erring produced better recall than restudy, with no significant difference between them.

In Experiment 2, learners were tested after one week. Here, deliberate errors led to significantly better performance than both retrieval practice and restudy. Even in Experiment 3, where retrieval practice was boosted with feedback and extra trials, deliberate errors retained their long-term advantage.

Perhaps most revealing was a consistent pattern: students underestimated the value of deliberate errors. They perceived them as less effective than restudy or retrieval practice — a classic example of a metacognitive illusion⁶.

The findings align closely with the results we would expect from three established cognitive theories, namely:

• Generative learning, which emphasises that learners benefit from generating connections, inferences, and elaborations.
• Self-explanation, which improves learning by prompting learners to clarify, justify, and connect concepts.
• Desirable difficulties, which suggest that mental effort during learning enhances memory.

The 64,000 dollar question is: How can you bring this strategy into your classroom?

If you intend to use this in the classroom, you first and foremost need to understand where it fits. Deliberate erring works after instruction, not before. In this way it is diametrically opposed to productive failure. Students need to already know the correct answer to create and correct a meaningful error.

Second, you need to design tasks that invite deliberate erring. Ask students to invent a believable but incorrect answer to a concept and then correct it.

Third, you also can choose to use it in group activities where you present deliberate errors for peer correction and discussion.

Finally, your feedback is really important. You need to talk explicitly about the why. Learners often avoid effortful strategies (pejoratively, the calculating student or as I dubbed them, the discipulus economicus) because they feel less effective. Show them the research. Let them reflect on what actually helps them remember. And let them experience it in their exams!

Yes, we can and do learn from our mistakes, especially when they’re deliberate.

Deliberate errors challenge our traditional beliefs about mistakes in learning. They show that error is not simply a sign of failure; that it can be a tool for learning. When used strategically, they help learners discriminate between similar ideas, activate related knowledge, and solidify what they know.

What Qiang, Ma, and Li offer is not simply a new learning strategy, but a new mindset (not in the Dweckian sense!): Mistakes can be designed and can be deliberate. In doing so, they bring together three very powerful ideas in cognitive science. Learning is deeper when it’s generative. It’s stronger when it’s explained. And it’s more durable when it’s difficult.

So let’s also teach learners how to use errors purposefully, reflectively, and productively. Because sometimes, the best way to get something right… might be to get it wrong first.

References

Bjork, R.A. (1994). Memory and metamemory considerations in the training of human beings. In J. Metcalfe and A. Shimamura (Eds.), Metacognition: Knowing about knowing (pp. 185-205). MIT Press. https://www.researchgate.net/publication/305433736_Memory_and_Meta-memory_Considerations_in_the_Training_of_Human_Beings

Chi, M. T. H. et al. (1989). Self-explanations: How students study and use examples. Cognitive Science, 13(2), 145–182. https://onlinelibrary.wiley.com/doi/epdf/10.1207/s15516709cog1302_1

Fiorella, L., & Mayer, R. E. (2016). Learning as a generative activity. Cambridge University Press.

Kendeou, P. (2024). A theory of knowledge revision: The development of the KReC framework. Educational Psychology Review, 36(2), 44. https://doi.org/10.1007/s10648-024-09885-y

Qiang, M., Ma, Y., & Li, X. (2025). Learning from errors: Deliberate errors enhance learning. Contemporary Educational Psychology, 73, 102215. https://doi.org/10.1016/j.cedpsych.2023.102215

Organizations often struggle with selecting the right tools for information management, a dilemma comparable to choosing the best saw for a DIY project: while a circular saw might suffice, a jigsaw could offer greater efficiency and a superior outcome. This highlights the core distinction between content management vs. knowledge management. 

Many businesses utilize content management solutions, while others opt for knowledge management solutions. Some even integrate both to manage their information assets and optimize business processes effectively. 

Below, we break down the key differences between these systems. We also provide examples of both, so you can decide which would be the most beneficial for your business processes when looking into knowledge management vs. content management. 

Content Management vs. Knowledge Management Key Differences

Content management and knowledge management both serve as frameworks to preserve information, which is essential for businesses. This enables companies to develop a knowledge transfer plan, preventing the loss of valuable knowledge when employees depart. Instead, they maintain a referenceable database for tackling problems that have been solved before, and they have a learning center that new hires can use to get up to speed quickly. 

The key differences between content management and knowledge management lie in the types of assets they incorporate and the ways those assets are deployed. Content management is about organizing the books, while knowledge management is about extracting and applying the wisdom within those books.

The differences between content management and knowledge management can be categorized as follows.

• Objectives: Content management is primarily concerned with managing digital content to ensure it is easily accessible and relevant. Knowledge management focuses on capturing and utilizing organizational knowledge to drive innovation and decision-making.
• Processes: Content management involves tasks such as content creation, storage, organization, and distribution. Knowledge management entails collecting, sharing, and applying knowledge within the organization.
• Outcomes: The outcome of effective content management is streamlined content workflows and improved access to information. Knowledge management, however, aims to foster a culture of continuous learning and enhance organizational agility.
• Audience: Content management systems primarily serve content creators, editors, and website administrators. In contrast, knowledge management systems cater to a broader audience, encompassing all employees to foster collaboration and information sharing.
• Scope: The coverage of CMS includes organizing and retrieving digital assets, such as web pages, documents, and multimedia, which are crucial for industries like media and e-commerce. Meanwhile, knowledge management has a broader scope in both digital and non-digital information, including tacit knowledge and expertise. 

To ensure both systems function as intended, understanding them as separate business tools can guide company leaders to assess their value.

In the following sections, you will learn the individual definitions and core characteristics of each system. These sections will explain knowledge management and content management as distinct, yet sometimes complementary, ways to organize an organization’s intellectual assets.

What Is Content Management?

Content management refers to the systematic handling of digital content throughout its lifecycle. This includes everything from creating and storing content to its distribution and archiving. The primary objective of content management is to ensure that information is easily accessible, up-to-date, and relevant to the target audience.

Content management tools function as the overarching repository where companies store information. It manages projects, websites, web pages, and documents. The focus here is on creating systematic, consumable content for a specific medium. 

That said, many types of CMS have been increasingly popular in organizations. According to Storyblok’s State of CMS report in 2024, only 19% of companies rely on a single content management system (CMS), while 47% use two to three, and 27% utilize four or more.   

For example, a team of content creators within the marketing department might plan, write, and publish blog posts designed to educate prospective customers. They will likely utilize a content management system (CMS) such as WordPress or HubSpot to manage and create this content. 

Importance of a Content Management System in Organizations

Content management systems are commonly used to manage websites and other digital content. This type of software enables users to create, publish, and edit content through a single, central interface. The interface is typically intuitive, allowing users from all departments to use it, rather than just those employees with programming or IT knowledge.  

A CMS can be a critical tool for managing websites. With a CMS, companies can easily update web pages, blog posts, and e-commerce pages, so your website is always up-to-date with the most recent information. 

Advanced CMS platforms also offer opportunities for customer personalization and targeted marketing, allowing current and prospective clients to see the most relevant content, ultimately leading to a better customer experience. 

What Is Knowledge Management?

Knowledge management (KM) is more multidimensional than content management, as it enables the search for, capture, update, and maintenance of relevant information within a single platform. While a CMS is primarily used for publishing external content, a KM platform can house both internal and external content. 

Using a KM platform like Bloomfire, subject matter experts can curate or update information in real-time, sparking conversations and collaboration among peers and across departments. And while knowledge management systems can contain explicitly documented content (such as process documents and how-to guides), they can also capture implicit knowledge by enabling employees to comment on content and publish questions and answers.  

When comparing content management vs. knowledge management, you could argue that both systems can store a 200-page training document. Still, only knowledge management systems can help capture the interactions and institutional knowledge surrounding that document.

Importance of a Knowledge Management System

What is a knowledge management system? Beyond simply storing documents, knowledge management frameworks and systems empower companies to preserve institutional knowledge and subject matter expertise. This benefits businesses in several ways, including:

• Faster onboarding process: With access to one centralized source of information, new employees can get up to speed faster.
• Consistent customer experience: A knowledge management system empowers employees to provide fast, accurate information to customers, delivering a better customer experience. Customers can resolve issues quickly without contacting customer support multiple times, improving the customer service experience. 
• Knowledge preservation: By documenting in-house expertise through a knowledge management system, businesses can ensure that they don’t lose valuable knowledge when employees decide to leave to pursue other opportunities or retire. 
• Increased efficiency and productivity: When employees know exactly where to find the knowledge they need to do their jobs, they can work faster and with fewer errors. They spend less time searching for information, allowing them to focus more on their core responsibilities. 
• Increased Enterprise Intelligence: Companies with robust knowledge management systems can identify subject matter experts and experienced, senior-level employees. They can also easily document their knowledge so the entire company can benefit.

A knowledge management system will only be helpful if employees use it regularly to capture and curate information. Otherwise, the system can become stagnant, inhibiting your company’s attempts to create efficiencies, help employees succeed, and drive change. 

Many companies overcome the challenge of stagnation by selecting a knowledge management system that integrates with the tools their employees use daily. These tools include Slack, Salesforce, and Zendesk. 

When a KM system seamlessly integrates with tools that are part of an employee’s daily workflow, the employee is more likely to adopt (and continue to use) the knowledge management system. This also helps prevent employees from waiting to document knowledge until “later,” which too often means that the knowledge is never actually preserved. 

Knowledge Management vs. Content Management: Difference in Tools

The tools used for content management and knowledge management differ significantly, reflecting the unique requirements of each system. Selecting the right tools, whether it’s knowledge base software vs. content management systems, is crucial for maximizing the effectiveness of each approach.

For instance, content management systems like WordPress, Drupal, or Joomla are designed to handle creating, editing, and publishing digital content, often for public consumption. They excel at managing website pages, blog posts, images, and other media, focusing on workflow, version control, and presentation. 

On the other hand, knowledge management systems such as Bloomfire or dedicated knowledge bases are built to organize, store, and retrieve internal organizational knowledge. Their features often include advanced search capabilities, collaborative document editing, wikis, and discussion forums, all aimed at fostering knowledge sharing and collective intelligence within an organization.

Content Management Tools

Content management tools enable individuals without extensive coding knowledge to build and maintain websites and other online platforms. These tools—whether they’re comprehensive content management systems, specialized digital asset managers, or insightful web analytics platforms—all serve a similar purpose: to empower users to create, manage, and optimize their digital presence effectively. 

• Complete content management systems (CMS): Platforms like WordPress, Joomla, and Drupal are popular CMS options that provide features for creating, editing, and organizing digital content.
• Digital asset management (DAM): Tools like Adobe Experience Manager and Bynder help manage and store multimedia content, ensuring easy access and retrieval.
• Web analytics tools, such as Google Analytics and Adobe Analytics, are used to monitor user engagement and optimize content for improved performance.

Some organizations configure their CMS platform to serve as both a company intranet and a dual-purpose platform for knowledge and content management. While this is possible, CMS platforms are not specifically designed for knowledge management and may pose a set of challenges related to scalability, information retention, and user adoption. Content management tools are essential to organizational success, but are not always the best tool for every job.

Knowledge Management Tools

Knowledge management tools involve diverse platforms that collectively empower organizations to cultivate a robust and available knowledge ecosystem. Organizations with optimized KMS reduce barriers to accessing information by nearly 59%. Leveraging them allows for streamlined information flow, enhanced collaboration, and more informed decision-making across all levels of an enterprise.

• Knowledge bases: Platforms like Bloomfire help document and share knowledge within an organization, providing a centralized repository for information.
• Collaborative platforms, such as Slack and Microsoft Teams, facilitate communication and collaboration among team members, promoting knowledge sharing and interaction.
• Social networking tools: Platforms like Yammer and Workplace by Facebook enable informal knowledge exchange and networking within organizations.

Various KM tools incorporate CMS functionalities for creating and managing articles within a knowledge base. Similarly, some CMS platforms can be configured to serve as internal intranets and support basic knowledge sharing and collaboration. However, dedicated KM tools are designed with the broader objectives of knowledge capture, collaboration, and leveraging organizational intelligence in mind.

Content Management vs. Knowledge Management: Differences in Applications

When it comes to real-world applications, content management revolves around the creation, storage, organization, versioning, publishing, and archiving of explicit content. Meanwhile, the emphasis in knowledge management is on fostering organizational learning and ensuring that collective expertise is readily accessible and actionable.

To better illustrate the differences between content management and knowledge management, let’s look at two examples.

Example 1: Using a CMS

Let’s say that a professional services firm is building a resource center for its website to share educational articles, blog posts, and other content for current and prospective clients. Company leaders plan to use a content management framework to complete this project. 

They have an editor who will serve as a project manager and several content creators who will write and review content. The editor and content creators utilize project management software to track their progress. When they complete new content, they upload it to their content management system and use it to publish information on the website. They can continue to update existing content and add new resources to the CMS platform as needed.

Example 2: Using a KMS

The same professional services firm from above requires its customer service representatives to quickly locate the necessary information to assist clients over the phone. The CMS platform isn’t designed to surface the specific knowledge they need quickly, so the company uses an internal knowledge management platform. 

Designated knowledge or community managers may add customer-facing resources, policies, processes, and other formal documentation to the platform. Still, they may also encourage customer service representatives and subject matter experts to ask and answer questions on the platform so the company’s knowledge base grows over time. 

Multichannel access to a wide variety of content forms, including videos, audio files, documents, and step-by-step procedures, provides all employees with a better understanding of—and easy, streamlined access to—company knowledge. 

Do Businesses Need Both Content and Knowledge Management Systems?

When used for their intended purposes, content management systems and knowledge management systems fulfill distinct needs and serve different end-users. Generally, content management systems are used to create, publish, and manage various forms of digital content designed to be consumed by external users, including current and potential customers. 

On the other hand, knowledge management systems can house both internal and external content, providing employees and customers with company knowledge. This may include information such as company policies, procedures, how-to guides, best practices, tips, and more. 

With that in mind, businesses can benefit from implementing both content management and knowledge management systems. You will primarily use your content management system to manage web content and publish across multiple channels. In contrast, you will use your knowledge management system to source internal knowledge and curate content that empowers your employees to achieve business goals such as improved customer service, boosted productivity, and increased efficiency. 

Ultimately, while the two systems both involve forms of content creation, that information is meant for different channels, so companies can and should use the two systems simultaneously for distributing knowledge and creating a great experience for both customers and employees.  

A knowledge management strategy ensures that organizational knowledge is captured, preserved, and made searchable and accessible, enabling better decision-making and performance. With a CMS, you can develop additional educational and customer-facing content to further enhance your KMS.

Frequently Asked Questions

What is the difference between knowledge management and information management?

Information management involves the collection and storage of data, while knowledge management focuses on leveraging insights and expertise to drive innovation and decision-making. Information management is about getting the correct data, and knowledge management is about making the most of what you know.

What is website knowledge management?

Website knowledge management specifically applies KM principles to a website, providing self-service information, like FAQs, to enhance user experience and reduce support needs. It differs from content management, which handles the digital content lifecycle, and the broader knowledge management that captures and leverages all organizational knowledge.

What is the difference between knowledge and content?

When distinguishing “content vs knowledge,” content refers to the raw, explicit information like documents, images, and videos. Knowledge, however, covers a broader spectrum, including explicit content and the implicit and tacit understanding, experiences, and insights that reside within individuals and an organization.

Driving Change Through Knowledge and Content Management

Content management vs. knowledge management is an ongoing conversation, but at the core, the two systems should be interconnected. However, for these two tools to work together, designing and implementing a comprehensive knowledge management strategy is essential. 

With well-organized content, whether it’s created for your customer-facing website or to preserve the knowledge of your employees, you can streamline your work processes and make progress toward a variety of business goals.

Note: This blog post was originally published in June 2017. It was most recently expanded and updated in June 2025.

The post Content Management vs. Knowledge Management: What’s the Difference? appeared first on Bloomfire.

A robust knowledge base helps you unlock the full potential of your organizational knowledge, facilitating better decision-making and collaboration. Efficiently managing and organizing this knowledge can greatly enhance your organization’s productivity and innovation. This is also where knowledge management comes into play, providing the frameworks and strategies to transform raw data into actionable insights.

In this comprehensive guide, we will cover the steps to organizing your knowledge base, from setting your goals to choosing the right platform and structuring your content. We will also explore best practices, common pitfalls, and strategies for continuously improving your knowledge management system.

Step 1: Define Your Knowledge Base Goals

Identifying your specific goals and objectives marks the first step to answering the question of “how to organize a knowledge base.” Without a clear understanding of what you aim to achieve, your efforts to organize your knowledge base may lack direction and impact. 37% of projects, including knowledge base implementations, fail due to unclear project objectives and milestones.

Start by identifying the primary objectives of your knowledge base. Are you looking to improve customer support, streamline internal communication, or enhance training programs?

Once you have established your primary objectives, consider the specific outcomes you wish to achieve. For instance, if your goal is to enhance customer support, you might aim to reduce response times or increase the rate of first-contact resolutions. If your focus is internal, aim to facilitate cross-departmental collaboration or improve onboarding processes. Defining these specific outcomes will provide a clear roadmap for developing your knowledge base.

It is also essential to align your knowledge management goals with your organization’s overall strategic objectives. This ensures that your efforts in knowledge management contribute directly to your broader business goals. Regularly revisit and refine your goals to ensure they remain relevant and aligned with your organization’s evolving needs.

Step 2: Identify and Collect Existing Information

With your goals clearly defined, the crucial next step involves identifying and gathering your organization’s existing information. This means performing a comprehensive audit of your current knowledge assets. Understanding what information is already available will help you prevent duplication of effort and ensure your knowledge base is both thorough and up-to-date.

Here’s how to approach this process:

• Categorize information: Begin by classifying the various types of information your organization utilizes and generates. This could encompass product manuals, troubleshooting guides, FAQs, and standard operating procedures.
• Gather resources: Once categories are set, collect all pertinent documents and data. This may require coordinating with different departments or teams to access their files and resources.
• Assess relevance and accuracy: As you collect information, evaluate its relevance and accuracy. Obsolete or incorrect information can compromise your knowledge base’s effectiveness, so verify that all content is current and meets your organizational standards.

Taking these steps not only streamlines the development process for your knowledge base but also guarantees that it becomes a dependable resource for all users. Doing so allows for a more informed and efficient work environment.

Step 3: Choose Your Knowledge Base Platform

Choosing the ideal knowledge platform is crucial for the success of your knowledge base. The platform you select should align with your goals and support the types of content you plan to include. With numerous options available for building a knowledge base, ranging from open-source solutions to enterprise-level software, it’s essential to evaluate each one based on your specific needs.

When selecting a knowledge base platform, consider the following critical factors:

• Usability: The user experience should be intuitive, allowing users to find the information they need easily. This includes a clean interface, effective search functionality, and straightforward navigation.
• Scalability: The platform should be scalable and capable of accommodating future growth in both content volume and the number of users. This means it can handle increasing data and user loads without significant performance degradation.
• Integration capabilities: Look for seamless integration with existing systems, such as CRM (Customer Relationship Management) or help desk software. This enhances functionality, streamlines workflows, and avoids information silos.
• Security features: The platform must offer robust security features to protect sensitive information. This includes access controls, encryption, and compliance with relevant data privacy regulations.
• Search and retrieval: Beyond basic search, evaluate advanced search options, filtering capabilities, and how effectively users can retrieve specific information from an extensive knowledge base.
• Content creation and management: Consider the ease of creating, editing, organizing, and publishing knowledge base documents. Look for features like version control, content templates, and collaborative editing tools.
• Analytics and reporting: The platform should provide analytics and reporting features to track usage, identify popular content, and pinpoint knowledge gaps. This data is crucial for continuous improvement.
• Customization: The ability to tailor the platform’s appearance and functionality to align with your organization’s branding and specific needs is crucial for a personalized user experience.
• Cost-Effectiveness: Evaluate the total cost of ownership, including licensing fees, implementation costs, maintenance, and potential training expenses.

To make an informed decision, involve key stakeholders in the selection process. This might include representatives from IT, customer support, and other departments that will frequently use the knowledge base. Their input can provide valuable insights into the features and functionalities that are most important for your organization. Once a platform is chosen, set it up in a way that aligns with your information architecture and organizational needs.

Step 4: Structure Your Content (Information Architecture)

Information architecture involves organizing content logically and intuitively, making it easy for users to navigate and find what they need. This step is critical in ensuring that your knowledge base is user-friendly and efficient. 

A report by McKinsey found that employees spend, on average, 1.8 hours every day – or 9.3 hours per week–searching for information. This highlights the substantial time lost due to disorganized or hard-to-find internal resources.

Begin by defining a clear hierarchy for your content. This might involve grouping related topics into categories and subcategories. Use logical naming conventions and consider the user’s perspective when organizing content to ensure clarity and ease of use. 

For a business organization, you might define a content hierarchy with a main category, such as “Employee Resources,” which could then have subcategories like “HR Policies,” “IT Support,” and “Training Materials.” Within the “HR Policies” section, you will find specific documents, such as “Leave Request Forms” and “Code of Conduct,” all organized logically for easy employee access.

Think about how users search for information and structure your content to match these patterns. A well-organized hierarchy simplifies navigation and enhances the overall user experience.

Step 5: Create and Format Your Content

Creating and formatting content is where the knowledge base truly comes to life. This step involves writing clear, concise, and accurate documentation that meets the needs of your users. High-quality content is essential for ensuring that your knowledge base is a valuable resource for your organization.

When creating content, such as articles for your knowledge base, focus on clarity and user comprehension. Here are the key steps to follow:

• Prioritize simple and direct language: Use straightforward vocabulary and sentence structures. Steer clear of jargon unless it’s essential and your target audience readily understands it.
• Break down complex information: Divide intricate topics into smaller, digestible sections. Use headings, bullet points, and numbered lists to significantly improve readability and allow users to scan for relevant information quickly.
• Incorporate visual aids: Enhance understanding and engagement by including images, diagrams, screenshots, and videos. Visuals can convey complex processes or concepts more effectively than text alone.
• Consider diverse formats and media: Don’t limit yourself to just text. Think about how multimedia elements can provide additional context and clarity. For instance, a written troubleshooting guide could be significantly enhanced by a short video demonstration of the steps.
• Maintain consistency: Ensure a consistent tone, style, and terminology throughout your content to maintain a cohesive narrative. This helps build a coherent and professional knowledge base.
• Focus on the user’s needs: Always write with your audience in mind. What questions are they trying to answer? What problems are they trying to solve? Tailor your content to directly address these needs.
• Include calls to action (where applicable): If the content requires a user to act, clearly state what that action is. This could be “Contact Support,” “Download this form,” or “Click here for more information.”
• Regularly review and update: Knowledge is constantly evolving. Schedule regular reviews of your content to ensure it remains accurate, relevant, and up-to-date.

To streamline the content creation process, assign roles and responsibilities to team members. 

Our Value of Enterprise Intelligence Report shows that on average, companies typically have only one content contributor for every seven employees who access a knowledge management system. This ratio highlights a significant imbalance, underscoring the potential for a small group to bear a substantial burden in maintaining current and comprehensive information.

Encourage collaboration and peer reviews to ensure accuracy and comprehensiveness of the content. Remember, your knowledge base is a living document that should evolve with your organization.

Step 6: Establish a Review and Update Process

Develop a schedule for regular content reviews. This might involve quarterly audits or more frequent checks for high-priority information. Assign responsibility for content review to specific individuals or teams, ensuring accountability and consistency throughout the process. 

During reviews, verify the accuracy of information, update outdated content, and remove any redundant or irrelevant material. In addition to scheduled reviews, establish a process for ad-hoc updates. This may involve a system for reporting inaccuracies or a protocol for incorporating user feedback.

Step 7: Promote and Train Users

A knowledge base is only effective if users know how to access and utilize it. Promote your knowledge base through internal communications, highlighting its benefits and available resources.

Consider offering training sessions or workshops to familiarize users with the knowledge base. These might include demonstrations of key features, navigation tips, and best practices for finding information. Tailor training to different user groups, such as employees, customer support teams, or external clients, to ensure that everyone can effectively use the resource.

In addition to formal training, provide ongoing support and resources to ensure continued learning and growth. This might include user guides, FAQs, and contact information for assistance. Encourage feedback and address any challenges users may encounter.

Step 8: Monitor and Iterate

Regular monitoring involves tracking usage metrics, gathering feedback, and identifying areas for improvement. This step is essential for maintaining a knowledge base that meets the evolving needs of your organization.

Utilize analytics tools to track how your knowledge base is being used. Key metrics might include the number of searches, popular topics, and user engagement levels. Analyzing these metrics can provide valuable insights into user behavior and help identify content gaps or areas for improvement. Use this data to prioritize updates and enhancements.

Encourage user feedback through surveys or feedback forms. This direct input can reveal user challenges, preferences, and suggestions for improvement. Act on feedback by making necessary changes and enhancements. Iterative improvement ensures that your knowledge base remains relevant, user-friendly, and aligned with organizational goals.

Best Practices in Designing Your Knowledge Base

An internal knowledge base, when well-implemented, has a significant impact on internal efficiency.  Companies that effectively leverage knowledge bases can improve internal team productivity by an average of 35% by streamlining access to information.

Designing a knowledge base goes far beyond simply compiling articles; it’s about crafting a dynamic and user-centric information hub that directly contributes to organizational success. Following best practices in its creation and ongoing management is paramount for enhancing its usability and overall effectiveness. 

Here are some key considerations to keep in mind for maximizing your knowledge base’s value:

• User-centric design: Focus on the needs and preferences of your users. Design the layout and navigation with their experience in mind.
• Consistency: Maintain a consistent style and format throughout your knowledge base. This includes language, tone, and visual elements.
• Accessibility: Ensure your knowledge base is accessible to all users, including those with disabilities. This might involve providing alternative text for images and ensuring compatibility with screen readers.
• Search optimization: Optimize your content for searchability. Use relevant keywords and meta tags to improve search engine performance within your knowledge base.
• Feedback mechanisms: Implement mechanisms for user feedback and suggestions. This can help identify areas for improvement and enhance user satisfaction.

Incorporating these best practices means you can create a knowledge base that’s not just informative but also incredibly user-friendly and highly effective. You can transform your knowledge base into a dynamic asset, ensuring your team and customers can easily find the information they need, when they need it.

Common Mistakes to Avoid in Knowledge Base Documentation

When establishing a knowledge base, it’s crucial to steer clear of common pitfalls that can severely hamper its effectiveness and adoption. Overlooking these mistakes can lead to a resource that is underutilized, unreliable, or even detrimental to organizational efficiency. 

Gartner reports that 47% of digital workers struggle to find the necessary information for their jobs. This highlights the significant impact that design flaws and maintenance neglect can have on productivity and employee satisfaction.

Here are some critical mistakes to watch out for:

• Overloading with information: Avoid overwhelming users with too much information. Prioritize clarity and conciseness, focusing on essential content.
• Neglecting updates: Failing to regularly update your knowledge base can lead to outdated and incorrect information. Establish a process for continuous review and revision.
• Ignoring user feedback: User feedback is a valuable resource for improvement. Ignoring it can result in a knowledge base that doesn’t meet user needs.
• Lack of training and promotion: Without proper training and promotion, users may not fully utilize your knowledge base. Ensure that users are aware of its existence and know how to use it effectively.
• Poor navigation: Complicated navigation can hinder the user experience. Design a clear and intuitive knowledge base structure for easy information retrieval.

To forge a practical and user-friendly knowledge base, remember these common missteps. Addressing these pitfalls during development and ongoing management dramatically increases the likelihood of success. Neglecting them can lead to a resource that is underutilized, unreliable, and ultimately, a drain on organizational resources rather than a benefit.

Frequently Asked Questions: How to Organize a Knowledge Base

How do I create a knowledge base?

Creating a knowledge base involves several key steps, starting with defining your content strategy and understanding your audience’s needs. Next, choose a knowledge base builder, like Bloomfire. Then, organize and structure your information logically, employing clear hierarchies and user-friendly navigation. Finally, populate the knowledge base with well-written, accurate, and easily understandable content, utilizing various formats to enhance comprehension and engagement.

How often should I update my knowledge base?

You should update your knowledge base regularly, with the frequency depending on the dynamism of your information and user feedback. Aim for a proactive approach, reviewing and revising content whenever there are changes in products, services, policies, or common user queries. At a minimum, conduct a comprehensive audit and update at least annually to ensure all information remains accurate and relevant.

Can I incorporate multimedia elements into my knowledge base?

Yes, you can incorporate multimedia elements into your knowledge base to enhance understanding and engagement. For example, Bloomfire allows you to integrate various media types, such as videos demonstrating software functionalities, interactive diagrams, or audio files for quick tutorials. This rich media approach makes information more accessible and caters to different learning preferences, significantly improving the user experience.

Enhancing Your Organization with a Well-Structured Knowledge Base

Organizing your knowledge base enables you to build a powerful tool that significantly enhances organizational efficiency. Adhering to the steps in this guide helps you create a comprehensive resource, one that fully supports your company’s goals and meets user needs. Every aspect of knowledge management, from setting clear objectives to continuously monitoring and improving content, is crucial for your success.

The post How to Organize a Knowledge Base: A Step-by-Step Guide appeared first on Bloomfire.

How does limited student engagement in virtual classrooms affect students’ academic performance? A stressor–strain–outcome perspective
Sheikh Mohammad Rabby, Asma Azad Akhi, Md. Shamim Talukder, Sumon Das
On the Horizon, Vol. ahead-of-print, No. ahead-of-print, pp.-

This study aims to investigate the impact of limited student engagement in virtual classroom environments on their academic performance, examining key stressors including infrastructural hindrance, instructors’ incompetency and distractions.

A sample of 304 students from various Bangladeshi universities participated in this study. Using the partial least squares-structural equation modeling approach, the research assessed the proposed model and hypotheses.

The research uncovers that stressors, specifically infrastructural hindrance, instructors’ incompetency and distractions, significantly impede student engagement within virtual classrooms. Furthermore, limited student engagement within online learning environments results in poor academic performance.

This study holds implications for educational service providers, offering insights into enhancing the effectiveness of online classroom platforms. The findings can foster improved strategies that positively impact students’ academic performance.

The application of the stressor–strain–outcome framework to elucidate the complex interplay between stressors, limited student engagement and academic performance within virtual classrooms during a global pandemic contributes original insights to the field of educational research.

Information is arguably an organization’s most valuable asset in today’s data-driven world. However, without proper management, this asset can quickly become a liability. Microsoft Fabric, a revolutionary unified analytics platform, integrates everything from data engineering and data science to data warehousing and business intelligence into a single, SaaS-based environment. It provides powerful tools to store, process, analyze, and visualize vast data. But with great power comes great responsibility. To maintain trust, ensure security, uphold data quality, and meet ever-increasing compliance demands, implementing a robust data governance framework within Fabric isn’t just recommended—it’s essential.

Effective data governance ensures that data remains accurate, secure, consistent, and usable throughout its entire lifecycle, aligning technical capabilities with strategic business goals and stringent regulatory requirements like GDPR, HIPAA, or CCPA. Within the Fabric ecosystem, this translates to leveraging its built-in governance features and its seamless integration with Microsoft Purview, Microsoft’s comprehensive data governance and compliance suite. The goal is to effectively manage and protect sensitive information while empowering users, from data engineers and analysts to business users and compliance officers, to confidently discover, access, and derive value from data within well-defined, secure guardrails.

A well-designed governance plan in Fabric strikes a critical balance between enabling user productivity and innovation and enforcing necessary controls for compliance and risk mitigation. It’s about establishing clear policies, defining roles and responsibilities, and implementing consistent processes so that, as the adage goes, “the right people can take the right actions with the right data at the right time”. This guide provides a practical, step-by-step approach to implementing such a framework within Microsoft Fabric, leveraging its native capabilities and Purview integration to build a governed, trustworthy data estate.

The Critical Importance of Data Governance

Data governance is more than just an IT buzzword or a compliance checkbox; it is a fundamental strategic imperative for any organization looking to leverage its data assets effectively and responsibly. The need for robust governance becomes even more pronounced in the context of a powerful, unified platform like Microsoft Fabric, which brings together diverse data workloads and user personas. Implementing strong data governance practices yields numerous critical benefits:

• Ensuring Data Quality and Consistency: Governance establishes standards and processes for creation, maintenance, and usage, leading to more accurate, reliable, and consistent data across the organization. This is crucial for trustworthy analytics and informed decision-making. Poor data quality can lead to flawed insights, operational inefficiencies, and loss of credibility.
• Enhancing Data Security and Protection: A core function of governance is to protect sensitive data from unauthorized access, breaches, or misuse. By defining access controls, implementing sensitivity labeling (using tools like Microsoft Purview Information Protection), and enforcing security policies, organizations can safeguard confidential information, protect intellectual property, and maintain customer privacy.
• Meeting Regulatory Compliance Requirements: Organizations operate under a complex web of industry regulations and data privacy laws (such as GDPR, CCPA, HIPAA, SOX, etc.). Data governance provides the framework, controls, and audit trails necessary to demonstrate compliance, avoid hefty fines, and mitigate legal risks. Features like data lineage and auditing in Fabric, often powered by Purview, are essential.
• Improving Data Discoverability and Usability: A well-governed data estate makes it easier for users to find the data they need. Features like the OneLake data hub, data catalogs, business glossaries, endorsements (certifying or promoting assets), and descriptive metadata help users quickly locate relevant, trustworthy data, fostering reuse and reducing redundant data preparation efforts.
• Building Trust and Confidence: When users know that data is well-managed, secure, and accurate, they have greater confidence in the insights derived from it. This trust is foundational for fostering a data-driven culture where decisions are based on reliable evidence.
• Optimizing Operational Efficiency: Governance helps streamline data-related processes, reduce data duplication, clarify ownership, and improve team collaboration. This leads to increased efficiency, reduced costs for managing poor-quality or redundant data, and faster time-to-insight.
• Enabling Scalability and Innovation: While governance involves controls, it also provides the necessary structure to manage data effectively as volumes and complexity grow. A solid governance foundation allows organizations to innovate confidently, knowing their data practices are sound and scalable.

Data governance transforms data from a potential risk into a reliable, strategic asset, enabling organizations to maximize their value while minimizing associated risks within the Microsoft Fabric environment.

An Overview of Microsoft Fabric

Understanding the platform itself is helpful before diving into the specifics of governance implementation. Microsoft Fabric represents a significant evolution in the analytics landscape, offering an end-to-end, unified platform delivered as a Software-as-a-Service (SaaS) solution. It aims to simplify analytics for organizations by combining disparate data tools and services into a single, cohesive environment built around a central data lake called OneLake.

Fabric integrates various data and analytics workloads, often referred to as “experiences,” which traditionally required separate, usually complex, integrations:

• Data Factory: Provides data integration capabilities for ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) processes, enabling data movement and transformation at scale.
• Synapse Data Engineering: A Spark-based large-scale data transformation and preparation platform primarily uses notebooks.
• Synapse Data Science: Provides an end-to-end workflow for data scientists to build, deploy, and manage machine learning models.
• Synapse Data Warehousing: Delivers a next-generation SQL engine for traditional data warehousing workloads, offering high performance over open data formats.
• Synapse Real-Time Analytics: This technology enables the real-time analysis of data streaming from various sources, such as IoT devices and logs.
• Power BI: The well-established business intelligence and visualization service, fully integrated for reporting and analytics.
• Data Activator: A no-code experience for monitoring data and triggering actions based on detected patterns or conditions.

Shortcuts allow your organization to easily share data between users and applications without unnecessarily moving and duplicating information. When teams work independently in separate workspaces, shortcuts enable you to combine data across different business groups and domains into a virtual data product to fit a user’s specific needs.

A shortcut is a reference to data stored in other file locations. These file locations can be within the same workspace or across different workspaces, within OneLake or external to OneLake in ADLS, S3, or Dataverse, with more target locations coming soon. No matter the location, shortcuts make files and folders look like you have stored locally. For more information on how to use shortcuts, see OneLake shortcuts.

Underpinning all these experiences is OneLake, Fabric’s built-in, tenant-wide data lake. OneLake eliminates data silos by providing a single, unified storage system for all data within Fabric, regardless of which experience created or uses it. It’s built on Azure Data Lake Storage Gen2. Still, it adds shortcuts (allowing data to be referenced without moving or duplicating it) and a unified namespace, simplifying data management and access.

This unified architecture has profound implications for governance. By centralizing data storage (OneLake) and providing a familiar administrative interface (Fabric Admin Portal), Fabric facilitates the application of consistent governance policies, security controls, and monitoring across the entire analytics lifecycle. Features like sensitivity labels and lineage can often propagate automatically across different Fabric items, simplifying the task of governing a complex data estate. Understanding this integrated nature is key to effectively implementing governance within the platform.

https://youtube.com/watch?v=X_c7gLfJz_Q%3Fstart%3D12%26feature%3Doembed

Understanding Microsoft Purview: The Governance Foundation

While Microsoft Fabric provides the unified analytics platform, Microsoft Purview is the overarching data governance, risk, and compliance solution that integrates deeply with Fabric to manage and protect the entire data estate. Understanding Purview’s role is crucial for implementing effective governance in Fabric.

Microsoft Purview is a family of solutions designed to help organizations govern, protect, and manage data across their entire landscape, including Microsoft 365, on-premises systems, multi-cloud environments, and SaaS applications like Fabric. Its key capabilities relevant to Fabric governance include:

• Unified Data Catalog: Purview automatically discovers and catalogs Fabric items (like lakehouses, warehouses, datasets, reports) alongside other data assets. It creates an up-to-date map of the data estate, enabling users to easily find and understand data through search, browsing, and business glossary terms.
• Data Classification and Sensitivity Labels: Through integration with Microsoft Purview Information Protection, Purview allows organizations to define sensitivity labels (e.g., Confidential, PII) and apply them consistently across Fabric items. This classification helps identify sensitive data and drives protection policies.

• End-to-End Data Lineage: Purview provides visualization of data lineage, showing how data flows and transforms from its source through various Fabric processes (e.g., Data Factory pipelines, notebooks) down to Power BI reports. This is vital for impact analysis, troubleshooting, and demonstrating compliance.
• Data Loss Prevention (DLP): Purview DLP policies can be configured (currently primarily for Power BI semantic models within Fabric) to detect sensitive information based on classifications or patterns (like credit card numbers) and prevent its unauthorized sharing or exfiltration, providing alerts and policy tips.
• Auditing: All user and administrative activities within Fabric are logged and made available through Microsoft Purview Audit, providing a comprehensive trail for security monitoring and compliance investigations.
• Purview Hub in Fabric: This centralized page within the Fabric experience provides administrators and governance stakeholders with insights into their Fabric data estate, including sensitivity labeling coverage, endorsement status, and a gateway to the broader Purview governance portal.

Purview is the central governance plane that overlays Fabric (and other data sources), providing the tools to define policies, classify data, track lineage, enforce protection, and consistently monitor activities. The seamless integration ensures that as data moves and transforms within Fabric, the governance context (like sensitivity labels and lineage) is maintained, enabling organizations to build a truly governed and trustworthy analytics environment.

https://learn.microsoft.com/en-us/purview/data-governance-overview

https://youtube.com/watch?v=JN1A4fD9NdM%3Ffeature%3Doembed

Step-by-Step Process for Implementing Data Governance in Microsoft Fabric

Implementing data governance in Microsoft Fabric is a phased process that involves defining policies, configuring technical controls, assigning responsibilities, and establishing ongoing monitoring. Here’s a practical step-by-step guide:

Step 1: Define Your Governance Policies and Framework

Before configuring any tools, establish the foundation – your governance framework. This involves defining the rules, standards, and responsibilities that will guide data handling within Fabric.

• Identify Stakeholders and Requirements: Assemble a cross-functional team including representatives from IT, data management, legal, compliance, and key business units. Collaboratively identify all applicable external regulations (e.g., GDPR, HIPAA, or CCPA) and internal business requirements (e.g., data quality standards, retention policies, ethical use guidelines). Understanding these requirements is crucial for tailoring your policies.
• Develop Data Classification Policies: Define clear data sensitivity levels (e.g., Public, Internal, Confidential, Highly Restricted). Map these levels to Microsoft Purview Information Protection sensitivity labels. Establish clear policies detailing how data in each classification level must be handled regarding access, sharing, encryption, retention, and disposal. For example, it mandates that all data classified as “Highly Restricted” must be encrypted and access restricted to specific roles. https://learn.microsoft.com/en-us/purview/sensitivity-labels
• Configure Tenant Settings via Admin Portal: Fabric administrators should configure tenant-wide governance settings in the Fabric Admin Portal. This includes defining who can create workspaces, setting default sharing behaviors, enabling auditing, configuring capacity settings, and potentially restricting specific Fabric experiences. Many settings can be delegated to domain or capacity admins, where appropriate, for more granular control. Consider licensing requirements for advanced Purview features like automated labeling or DLP. https://learn.microsoft.com/en-us/fabric/admin/about-tenant-settings
• Document and Communicate: Document all governance policies, standards, and procedures. Make this documentation easily accessible to all Fabric users. Communicate the policies effectively, explaining their rationale and clarifying user responsibilities. Assign clear accountability for policy enforcement, often involving data stewards, data owners, and workspace administrators.

Step 2: Establish Roles and Access Controls (RBAC)

With policies defined, implement Role-Based Access Control (RBAC) to enforce them.

• Utilize Workspace Roles: Assign users or (preferably) security groups to Fabric workspace roles (Admin, Member, Contributor, Viewer) based on the principle of least privilege. Understand the permissions associated with each role to ensure users only have the access necessary for their jobs. https://learn.microsoft.com/en-us/fabric/fundamentals/roles-workspaces
• Leverage Security Groups: Manage access primarily through Microsoft Entra ID (formerly Azure AD) security groups rather than individual user assignments. This simplifies administration as team memberships change.
• Assign Admin Roles: Carefully assign higher-level administrative roles: Fabric Administrator (tenant-wide), Domain Administrator (for specific business areas), and Capacity Administrator (for managing compute resources)—delegate responsibilities where appropriate to distribute the governance workload. https://learn.microsoft.com/en-us/fabric/admin/roles
• Implement Granular Security: For sensitive data within datasets, lakehouses, or warehouses, implement Row-Level Security (RLS) and Object-Level Security (OLS) (e.g., column-level or table/view permissions) to restrict access at a finer grain than workspace roles allow. https://learn.microsoft.com/en-us/fabric/data-warehouse/row-level-security, https://learn.microsoft.com/en-us/fabric/security/service-admin-row-level-security and https://learn.microsoft.com/en-us/fabric/data-warehouse/column-level-security
• Establish Access Review Processes: Implement procedures for requesting, approving, and periodically reviewing access permissions, especially for sensitive data or privileged roles. Maintain logs of approvals for audit purposes.

Step 3: Configure Workspaces and Domains

Organize your Fabric environment logically to support governance.

• Structure Domains: Group workspaces into logical domains, typically aligned with business units or subject areas (e.g., Finance, Marketing, Product Analytics). This facilitates delegated administration and helps users discover relevant data. https://learn.microsoft.com/en-us/fabric/governance/domains
• Organize Workspaces: Within domains, organize workspaces based on purpose (e.g., project, team) or environment (Development, Test, Production). Use clear naming conventions and descriptions. Assign workspaces to the appropriate domain. https://learn.microsoft.com/en-us/fabric/fundamentals/workspaces
• Apply Workspace Settings: Configure settings within each workspace, such as contact lists, license modes (Pro, PPU, Fabric capacity), and connections to resources like Git for version control, aligning them with your governance policies.
• Consider Lifecycle Management: Use separate workspaces and potentially Fabric deployment pipelines to manage content promotion from development through testing to production, ensuring only validated assets reach end-users. https://learn.microsoft.com/en-us/fabric/cicd/deployment-pipelines/understand-the-deployment-process?tabs=new-ui

Step 4: Implement Data Protection and Security Measures

Actively protect your data assets using built-in and integrated tools.

• Apply Sensitivity Labels: Implement the data classification policy by applying Microsoft Purview Information Protection sensitivity labels to Fabric items (datasets, reports, lakehouses, etc.). Use a combination of manual labeling by users, default labeling on workspaces or items, and automated labeling based on sensitive information types detected by Purview scanners. Ensure label inheritance policies are configured appropriately. https://learn.microsoft.com/en-us/power-bi/enterprise/service-security-enable-data-sensitivity-labels
• Configure Data Loss Prevention (DLP) Policies: Define and enable Microsoft Purview DLP policies specifically for Power BI (and potentially other Fabric endpoints as capabilities expand) to detect and prevent the inappropriate sharing or exfiltration of sensitive data identified by sensitivity labels. (Note: Requires specific Purview licensing.) https://learn.microsoft.com/en-us/fabric/governance/data-loss-prevention-configure
• Leverage Encryption: Understand and utilize Fabric’s encryption capabilities, including encryption at rest (often managed by the platform) and potentially customer-managed keys (CMK) for enhanced control over encryption if required. https://learn.microsoft.com/en-us/fabric/security/security-scenario

Step 5: Enable Monitoring and Auditing

Visibility into data usage and governance activities is crucial.

• Utilize the Monitoring Hub to monitor the status and history of Fabric activities like pipeline runs, Spark jobs, and dataflow refreshes. https://learn.microsoft.com/en-us/fabric/admin/monitoring-hub
• Analyze Capacity Metrics: Use the Fabric Capacity Metrics app to monitor resource utilization, performance, and potential throttling, helping optimize costs and ensure service levels. https://learn.microsoft.com/en-us/fabric/enterprise/metrics-app
• Leverage the Purview Hub: Use the integrated Purview Hub within Fabric as a central point for administrators and data owners to view governance insights, manage metadata, and track compliance status. https://learn.microsoft.com/en-us/fabric/governance/use-microsoft-purview-hub?tabs=overview
• Enable and Review Audit Logs: Ensure Fabric auditing is enabled and integrated with the Microsoft Purview compliance portal. Regularly review audit logs to track user activities, access patterns, policy changes, and potential security incidents. https://learn.microsoft.com/en-us/fabric/admin/track-user-activities
• Use Admin Monitoring Workspace: Fabric administrators can use the dedicated monitoring workspace to gain insights into tenant-wide activities, usage patterns, and adoption. https://learn.microsoft.com/en-us/fabric/admin/monitoring-workspace

Step 6: Foster Data Discovery, Trust, and Reuse

Governance should also empower users by making trustworthy data easily accessible.

• Promote the OneLake Data Hub: Encourage users to utilize the OneLake Data Hub to discover existing data items across the tenant, filtering by domain, keywords, or endorsement status. https://learn.microsoft.com/en-us/fabric/governance/onelake-catalog-overview and https://learn.microsoft.com/en-us/fabric/governance/onelake-catalog-govern
• Implement Endorsement: Establish a straightforward process for promoting and certifying high-quality, reliable data assets (datasets, dataflows, reports). Clearly define the criteria for certification and authorize specific reviewers. This builds user trust in endorsed assets. https://learn.microsoft.com/en-us/fabric/governance/endorsement-overview
• Utilize Tags: Encourage using relevant tags on fabric items to improve organization and searchability beyond standard metadata. https://learn.microsoft.com/en-us/fabric/governance/tags-overview
• Leverage Lineage and Impact Analysis: Use Fabric’s lineage view to understand data origins, transformations, and dependencies. Before changing upstream items, perform an impact analysis to understand potential effects on downstream reports or processes. https://learn.microsoft.com/en-us/fabric/governance/lineage
• Build a Business Glossary: Integrate with Microsoft Purview Data Catalog to build and maintain a business glossary, consistently defining key terms and metrics across the organization and linking them to relevant Fabric assets. https://learn.microsoft.com/en-us/purview/legacy/how-to-create-manage-glossary-term

Step 7: Monitor, Iterate, and Optimize

Data governance is not a one-time project but an ongoing process.

• Regularly Review and Audit: Periodically review governance policies, access controls, label usage, and audit logs to ensure effectiveness and identify areas for improvement. https://learn.microsoft.com/en-us/fabric/governance/governance-compliance-overview
• Gather Feedback: Solicit feedback from users and stakeholders on the governance processes and tools.
• Adapt and Update: Update policies and configurations based on audit findings, user feedback, changing regulations, and evolving business needs. Stay informed about new Fabric and Purview governance features.

By following these steps, organizations can establish a comprehensive and practical data governance framework within Microsoft Fabric, enabling them to harness the full power of the platform while maintaining control, security, and compliance.

Real-World Examples: Data Governance in Action

The principles and steps outlined above are not just theoretical; organizations are actively implementing robust data governance frameworks using Microsoft Fabric and Purview to overcome challenges and drive value. Let’s look at a couple of examples:

1. Microsoft’s Internal Transformation:

Microsoft itself faced significant hurdles with its vast and complex data estate. Data was siloed across various business units and managed inconsistently, making it difficult to gain a unified enterprise view. Governance was often perceived as a bottleneck, hindering the pace of digital transformation. Microsoft embarked on its data transformation journey, leveraging its tools to address this.

Their strategy involved building an enterprise data platform centered around Microsoft Fabric as the unifying analytics foundation and Microsoft Purview for governance. Fabric helped break down silos by providing a common platform (including OneLake) for data integration and analytics across diverse sources. Purview was then layered on top to enable responsible data democratization. This meant implementing controls like a shared data catalog and consistent policies, not to restrict access arbitrarily, but to enable broader, secure access to trustworthy data. A key cultural shift was viewing governance as an accelerator for transformation, facilitated by the unified data strategy and strong leadership alignment. The outcome is a more agile, regulated, and business-focused data environment that fuels faster decision-making and innovation.

2. Leading Financial Institution:

A leading bank operating in a highly regulated industry revolutionized its data governance with Microsoft Purview. While specific challenges aren’t detailed in the summary, typical banking concerns include operational efficiency, stringent compliance requirements (like GDPR), data security, and preventing sensitive data loss.

By implementing Purview, the bank achieved significant improvements. Operationally, automated data discovery and a centralized view allowed business users to find information faster and reduced manual effort in reporting. From a compliance perspective, Purview provided centralized metrics for monitoring the compliance posture and automated processes for classifying and tagging data according to regulations, strengthening overall security. Furthermore, implementing Data Loss Prevention (DLP) rules based on data sensitivity helped safeguard critical information and prevent unauthorized access or sharing. Purview acted as a unified platform, enhancing efficiency, visibility, security, and control over the bank’s data assets.

These examples illustrate how organizations, facing everyday challenges like data silos, compliance pressures, and the need for agility, are successfully using Microsoft Fabric and Purview to establish effective data governance. They highlight the importance of a unified data strategy, the role of tools in automating and centralizing controls, and the cultural shift towards viewing governance as an enabler of business value.

Conclusion

Microsoft Fabric offers a robust, unified platform for end-to-end analytics, but realizing its full potential requires a deliberate and comprehensive approach to data governance. As we’ve explored, implementing governance in Fabric is not merely about restricting access; it’s about establishing a framework that ensures data quality, security, compliance, and usability, fostering trust and enabling confident, data-driven decision-making across the organization.

The real-world examples, from Microsoft’s internal transformation to implementations in regulated industries like finance, demonstrate that these are not just theoretical concepts. Organizations are actively leveraging Fabric’s unified foundation and Purview’s comprehensive governance capabilities to overcome tangible challenges like data silos, inconsistent management, compliance burdens, and operational inefficiencies.

By integrating Fabric’s built-in features—such as the Admin Portal, domains, workspaces, RBAC, endorsement, and lineage—with the advanced capabilities of Microsoft Purview—including Information Protection sensitivity labels, Data Loss Prevention, auditing, and the unified data catalog—organizations can create a robust governance posture tailored to their specific needs.

The outlined step-by-step process provides a roadmap, but the journey requires more than technical implementation. Success hinges on several key factors, reinforced by real-world experience:

Key Recommendations for Success:

1. Strategic Alignment and Collaboration: As seen in Microsoft’s case, define clear governance objectives that are aligned with business goals before configuring tools. Data governance requires a cultural shift and strong leadership alignment. It’s a team effort involving IT, data, legal, compliance, and business units.
2. Leverage the Unified Platform (Fabric + Purview): Treat Fabric and Purview as an integrated solution. Use Fabric to unify the data estate and Purview to apply consistent governance controls across it, enabling responsible democratization and breaking down silos.
3. Prioritize Automation for Efficiency and Consistency: Automate governance tasks like sensitivity labeling, policy enforcement (DLP), and monitoring wherever possible. As the banking case study demonstrated, this reduces manual effort, ensures consistency, improves responsiveness, and boosts operational efficiency.
4. Focus on User Empowerment and Education: Balance control with usability. Provide clear documentation, training, and tools (like the OneLake Data Hub and Purview catalog) to help users understand policies, find trustworthy data, and comply with requirements – turning governance into an accelerator, not a blocker.
5. Implement Incrementally and Iterate: Data governance is an ongoing journey. Start with a pilot or focus on critical assets first. Monitor effectiveness, gather feedback, and continuously refine your approach based on evolving needs, regulations, and platform capabilities.

By taking a structured, collaborative, and tool-aware approach, informed by others’ successes, organizations can build a foundation of trust and control within Microsoft Fabric, transforming governance from a perceived burden into a strategic enabler that unlocks the actual value of their data.

Should you have any questions or need assistance about Microsoft Fabric or Microsoft Purview, please don’t hesitate to contact me using the provided link: https://lawrence.eti.br/contact/

That’s it for today!

Sources

• Implementing Data Governance in Power BI (Lawrence ETI Blog) – https://lawrence.eti.br/2023/01/29/implementing-data-governance-in-power-bi-a-step-by-step-guide/
• Microsoft Learn: Governance and compliance in Microsoft Fabric – https://learn.microsoft.com/en-us/fabric/governance/governance-compliance-overview
• Microsoft Learn: Govern your Fabric data with the OneLake catalog – https://learn.microsoft.com/en-us/fabric/governance/onelake-catalog-govern
• Microsoft Learn: What is Microsoft Fabric? – https://learn.microsoft.com/en-us/fabric/fundamentals/microsoft-fabric-overview
• Microsoft Learn: OneLake, the OneDrive for data – https://learn.microsoft.com/en-us/fabric/onelake/onelake-overview
• Microsoft Learn: Information protection in Microsoft Fabric – https://learn.microsoft.com/en-us/fabric/governance/information-protection
• Microsoft Learn: What is the admin portal? – https://learn.microsoft.com/en-us/fabric/admin/admin-overview
• Microsoft Learn: Workspace roles – https://learn.microsoft.com/en-us/fabric/get-started/roles-workspaces
• Microsoft Learn: Admin roles – https://learn.microsoft.com/en-us/fabric/admin/roles
• Microsoft Learn: Security overview – https://learn.microsoft.com/en-us/fabric/security/security-overview#data-security
• Microsoft Learn: Domains – https://learn.microsoft.com/en-us/fabric/governance/domains
• Microsoft Learn: Introduction to deployment pipelines – https://learn.microsoft.com/en-us/fabric/cicd/deployment-pipelines/intro-deployment-pipelines
• Microsoft Learn: Learn about data loss prevention policies for Power BI – https://learn.microsoft.com/en-us/purview/dlp-powerbi-learn-about
• Microsoft Learn: Encryption – https://learn.microsoft.com/en-us/fabric/security/security-encryption
• Microsoft Learn: Monitoring hub – https://learn.microsoft.com/en-us/fabric/admin/monitoring-hub
• Microsoft Learn: Microsoft Fabric Capacity Metrics – https://learn.microsoft.com/en-us/fabric/enterprise/metrics-app
• Microsoft Learn: Use the Microsoft Purview hub – https://learn.microsoft.com/en-us/fabric/governance/use-microsoft-purview-hub
• Microsoft Learn: Track user activities in Microsoft Fabric – https://learn.microsoft.com/en-us/fabric/admin/track-user-activities
• Microsoft Learn: Admin monitoring – https://learn.microsoft.com/en-us/fabric/admin/admin-monitoring
• Microsoft Learn: OneLake data hub – https://learn.microsoft.com/en-us/fabric/governance/onelake-catalog-overview
• Microsoft Learn: Endorsement overview – https://learn.microsoft.com/en-us/fabric/governance/endorsement-overview
• Microsoft Learn: Tags – https://learn.microsoft.com/en-us/fabric/governance/tags
• Microsoft Learn: Lineage – https://learn.microsoft.com/en-us/fabric/governance/lineage
• Microsoft Learn: Create and manage glossary terms – https://learn.microsoft.com/en-us/purview/how-to-create-edit-glossary-terms
• YASH Technologies Blog: Microsoft Fabric Data Governance: What You Need Today – https://www.yash.com/blog/comprehensive-guide-to-data-governance-in-microsoft-fabric/
• Microsoft Inside Track: Transforming Data Governance at Microsoft with Purview and Fabric – https://www.microsoft.com/insidetrack/blog/transforming-data-governance-at-microsoft-with-microsoft-purview-and-microsoft-fabric/
• Kanerika Case Study: Achieving Data Governance Excellence with Microsoft Purview – https://kanerika.com/case-studies/revolutionizing-data-governance-for-a-leading-bank-with-microsoft-purview/

About the Author

Lawrence Teixeira

Chief Transformation Officer @ Licks Attorneys | MBA in AI, Data-Driven Strategy

 

Reference:

Teixeira, L (2025). Implementing Data Governance in Microsoft Fabric: A Step-by-Step Guide. Available at: Implementing Data Governance in Microsoft Fabric: A Step-by-Step Guide – Tech News & Insights [Accessed: 10th May 2025].

The post Implementing Data Governance in Microsoft Fabric: A Step-by-Step Guide appeared first on ESPC Conference, 2025.

Summary: Unlearning old ideas and ways of working is a critical yet challenging skill for adult learners. Neuroscience reveals that the complexity of our brains makes unlearning harder. L&D functions are presented with a unique opportunity: to support people as they overcome the cognitive, emotional and social barriers associated with unlearning.

——

In the medical field, many educators say that half of what students learn will eventually be proven false. That’s a daunting statistic! And yet, with the expediency at which new research, data and information is uncovered today, professionals across all industries are facing this reality. As knowledge must be constantly updated and replaced, one pressing challenge becomes clear for the L&D function: how can we help learners become better unlearners?

Let’s take a look at the neuroscience behind unlearning, and explore how Learning & Development can address these barriers over time.

The Neuroscience Behind Unlearning:

When we first learn something new, the brain builds neural pathways. Think of these pathways like following a hiking trail– you’ll have a much easier time walking through the woods if you follow the well-traveled path. And the more this path is used over time, the deeper and wider the trail becomes. This repetitive use of neural pathways leads to something called neural efficiency, where the brain uses established pathways to save mental energy.

But what happens when you find a faster path, cutting through the woods? Most people will still take the well-traveled path— it’s still the easier path. Your brain, too, will continue to prioritize the less demanding and most frequently used pathways. Therefore, the challenge of unlearning is not just to forge new neural pathways, but to inhibit dominant neural networks simultaneously. WDHB Studio’s Research Associate, Kübra Simsek, says about the unlearning process, “When we forget something, it results from synapses associated with certain circuits being weakened or less activated. This occurs through an inhibition process, where less frequently used neural connections are reduced in strength. However, they do not disappear completely.”

Why is Unlearning So Hard?

Beyond neural pathways, here are a few other reasons why unlearning can be a challenge. 

1. Ego Protection

Much of what we learn consists of mental models– frameworks that help us understand the world based on socialization and lived experience. These mental models can become so engrained, they impact behavior, personality and even self-perception. To let go of old knowledge, therefore, can feel like a threat to the identity and perception of reality. Releasing these anchoring beliefs can often feel unsettling for learners— if not downright unsafe.

2. Cognitive Biases

Cognitive biases are errors in thinking that occur when your brain attempts to simplify how it processes new information. These biases are meant to help you reach speedy conclusions with less brain power, utilizing “rules of thumb” that seem to be true most of the time. A common example of cognitive bias is confirmation bias— the process of paying greater attention to information that aligns with currently held beliefs.

Cognitive biases fail us when they preclude alternate approaches in favor of familiar solutions. In simpler terms— you’ll never see that new path through the woods if you only pay attention to the familiar road ahead of you.

3. Social Reinforcement

Cultural norms within organizations often reinforce existing mental models, making it difficult to challenge established ways of thinking. When a company deeply values tradition or consistency, employees may hesitate to ask whether certain approaches are still effective. Without intentional reflection, teams can become resistant to questioning the status quo, even when change is necessary for growth. L&D can play a crucial role in creating structured opportunities for teams to reassess and adapt their mental models in a way that feels constructive rather than disruptive.

4. Emotional Attachment

When you have an emotionally charged experience, your emotional nervous system (aka your limbic system) activates to help create stronger neural pathways. This is because the brain prioritizes information that protects us in times of perceived danger. These emotional memories become “sticky” learnings that take 40% more cognitive effort to override.

How can L&D support the unlearning process?

While unlearning is challenging, it isn’t impossible. With targeted efforts by L&D teams, we can make the most of the brain’s neuroplasticity while integrating new mental models.

1. Create Safe Communities of Learning

Unlearning requires vulnerability. If people fear judgment, failure or repercussions for shifting their thinking, they will cling even tighter to their existing mental models. This is why psychological safety is a prerequisite for unlearning— without it, learners may struggle to challenge their own deeply held beliefs or step into the discomfort of change.

L&D teams can foster safety by creating structured spaces for open dialogue, curiosity, and respectful dissent. When learners see their peers openly grappling with new ideas— and when facilitators model this behavior— it normalizes the process of letting go and embracing the unknown. Techniques like peer coaching, reflective discussions and structured debates can encourage learners to challenge outdated perspectives without fear of criticism. We talk more about building learning communities in our latest research report, “The Future of L&D 2024.”

2. Catalyze an Innovation Mindset

If outdated mental models hold organizations back, then the ability to unlearn is directly tied to innovation. Many organizations claim to value innovation, yet they inadvertently reinforce rigid thinking patterns that make true change difficult.

One way L&D can drive this shift is by encouraging learners to adopt what psychologist Carol Dweck calls a growth mindset. Organizations that celebrate curiosity and experimentation create conditions where employees feel empowered to challenge their assumptions. Do you have a growth mindset? Take this short quiz to find out!.

L&D can support a growth mindset in the long term by designing learning experiences that push participants out of their comfort zones. Reverse mentoring, scenario planning and design thinking workshops all encourage people to question assumptions and reframe problems in new ways. The key is to create a culture where unlearning is not seen as a loss, but as a necessary step toward innovation and growth.

3. Experiential Learning:

If emotions create stickier learnings, then experiential learning is the key to building new neural pathways fast. Hearing impactful stories from inspiring people triggers a powerful emotional response, creating memorable “moments” in learning that participants will remember 10 years down the road.

Additionally, learning experiences often leverage embodied cognition— the idea that we store information in our bodies, which impacts our thoughts and actions. According to Dr. Andrew Huberman’s research, pairing physical movement with cognitive restructuring creates 68% faster unlearning compared to traditional learning methods. With engaging, hands-on activities and moments of reflection with peers, the possibilities for deeper processing and reflection go far beyond what you typically witness in the classroom setting.

Conclusion

Unlearning is fundamentally different from forgetting and relearning. In reality, unlearning is a complicated process that requires both neural inhibition and replacement. Neural inhibition can be a huge challenge for learners, especially when we consider the social and emotional impact knowledge can have on self-perception and lived experience. The role of L&D, then, is not just to teach new skills, but to create the conditions in which people feel safe to question, explore and ultimately replace their old ways of thinking.

By cultivating communities of learning, fostering innovation in the workplace and utilizing experiential learning, Learning & Development teams can uniquely position themselves to support employees as they create new neural pathways. In the end, the most successful learners are not just those who acquire new knowledge, but those who master the art of unlearning and relearning, time and time again.

Further Reading

https://www.sciencedirect.com/science/article/abs/pii/S0010945218300273

https://www.linkedin.com/pulse/how-challenge-your-mental-models-think-differently-kate-gerasimova

https://blog.cengage.com/the-neuroscience-of-unlearning/

https://partably.com/unlearning/

https://pmc.ncbi.nlm.nih.gov/articles/PMC5657245

https://www.verywellmind.com/what-is-a-cognitive-bias-2794963

If you work in a membership organisation, you already know the pain. Members need answers—about CPD requirements, industry standards, event bookings, or how to renew their membership. But where do they go? For many, it’s straight to your inbox or a phone call to your team. Despite all the carefully curated content, FAQs, and [...]

The post 7 Essential Learning Content Management System Features to Look for first appeared on Xyleme.

Training managers to ensure employees’ engagement and well-being in a remote working environment
Christina Nizamidou
Development and Learning in Organizations, Vol. ahead-of-print, No. ahead-of-print, pp.-

Given the massive increases in remote work, a new need has emerged for managers’ training focused on employees’ engagement and well-being. This viewpoint provides recommendations for this training.

Based on a literature review on the effects of remote work on employees’ engagement and emotions and on the experiences of those in my professional and personal circles who work in a remote environment, this viewpoint argues for the need to train managers to ensure employees’ engagement and well-being in this new mode of remote work.

In this viewpoint, I provide several solutions that managers can use to address the effects of remote work, aiming to maximize individual and organizational performance.

From an originality perspective, the article combines contradictory research findings about employees’ engagement and emotions in a remote working environment, demonstrating my unbiased position. Based on them, it generates substantial solutions for managers to tackle the negative impacts of remote work on their employees’ well-being and engagement.

Will Box become the content and context enabler of the agentic AI food chain? Box CEO Aaron Levie is betting on it.

With an investor day on tap, Levie laid out Box's AI and genAI master plan, which has been advancing over the last 18 months or so. With the rise of agentic AI, Box's vision is playing out.

The company reported in line fourth quarter earnings with a light first quarter outlook--largely due to a currency exchange headwind due to operations in Japan. Box reported fourth quarter earnings of $1.12 a share, on revenue of $279.5 million, up 6%. Non-GAAP earnings were 42 cents a share. For fiscal 2025, Box earnings were $1.36 a share on revenue of $1.09 billion.

As for the outlook, Box projected first quarter revenue of $274 million to $275 million with non-GAAP earnings between 25 cents a share and 26 cents a share. For fiscal 2026, Box projected revenue between $1.15 billion to $1.16 billion with non-GAAP earnings between $1.13 a share and $1.17 a share.

Box recently launched an Enterprise Advanced version of its platform along with a credit system as customers consume AI agents going forward. Box is also benefiting as systems integrators and services providers include the company in AI implementations.

• Box launches new AI features, Box AI Units
• Box acquires Alphamoon Technology, aims to integrate LLMs, OCR into Box AI
• AI agents bring consumption models to SaaS: Goldilocks or headache?

"We are seeing companies start to adopt Enterprise Advanced to power intelligent metadata extraction from documents, automate workflows and dashboards with Box Apps, gain access to Forms, Doc Gen, and Archive, and create custom AI agents with the AI Studio," said Levie, who cited legal and public sector use cases in prepared remarks.

He added:

"AI Agents are entering the workforce and will augment and accelerate our work; our unstructured data is enabling intelligence that we can now use to gain new insights about business; and we can begin automating any workflow in the enterprise, especially the long tail of work that we couldn't automate before."

With Box adding context and insights to previously untapped unstructured data, it could become a cog in agentic AI workflows. Levie said that Box is using its own platform to boost productivity, give employees the ability to get HR and sales information, and use Box AI to write sales pitches, conduct code reviews and give the company more insights.

"Any employee can gain the same level of expertise as the most knowledgeable employee just by asking questions of the existing enterprise data that's already there; the hidden information inside of contracts, invoices, financial documents, and customer data turns into business critical insights that drive better execution; Agentic AI can automate workflows that were expensive and time consuming; and by understanding what's in our content, we can better secure and protect it at scale," said Levie.

Levie said that Box has multiple product announcements in fiscal 2026 focused on extracting data from documents, no-code Box Apps, workflow automation and AI platform advances for Box AI Agents. Levie said that Box will continue to add models to its platform.

• Agentic AI: Three themes to watch for 2025
• Agentic AI without process optimization, orchestration will flop

"We believe we're an asset in this environment. In dynamic times, you want to have more leverage from your technology, you want to be able to retire more legacy systems, and you want to automate more workflows," said Levie.

Indeed, Box appears to be landing more high-level CxO interest as part of broader enterprise transformation plans.

"We were usually in core infrastructure within the CIO organization. But the Chief Data Officer realizes now that once you have AI on unstructured data, they can treat that as another data type to pull business insights from," said Levie. "We're having more conversations with company CTOs that are trying to deliver better experiences, and they know that if they can get data from within their unstructured information, they could go and automate a better client-facing experience."

ISO 9001 as a catalyst: linking staff training to university performance
Shahzaf Iqbal
The TQM Journal, Vol. ahead-of-print, No. ahead-of-print, pp.-

This study investigates the impact of staff training on university performance (UP), focusing particularly on the mediating role of ISO 9001 implementation in the higher education (HE) sector.

Employing a quantitative research design, data were collected via online surveys using purposive sampling techniques from academic and administrative staff affiliated with ISO 9001-certified universities. Subsequent analysis utilized SmartPLS-4 software.

The findings indicate that staff training significantly influences both ISO 9001 standard implementation and UP. Additionally, the mediating effect of ISO 9001 in the relationship between staff training and UP was found to be positive and significant.

Limitations of the study include its cross-sectional design, reliance on purposive sampling and exclusive focus on academic and administrative staff from seven ISO-certified universities in Pakistan, potentially limiting generalizability. Nonetheless, the study enriches the discourse on quality management in HE by emphasizing the role of staff training in fostering knowledge creation and enhancing staff competencies within organizational learning theory (OLT), and by integrating ISO 9001 into the dynamic capability theory (DCT) framework.

The study provides practical insights for policymakers, administrators and quality managers, emphasizing the importance of staff training, resource allocation for compliance and continuous improvement efforts to effectively implement ISO 9001 requirements and enhance overall UP.

This study contributes by introducing ISO 9001 adoption as a mediator between staff training and UP while integrating OLT and DCT theories within the university context.

Industry 5.0 – the human centric solution and personalization: challenges and opportunities for libraries
Murtaza Ashiq
Library Hi Tech News, Vol. ahead-of-print, No. ahead-of-print, pp.-

Industry 5.0 is considering a paradigm shift from mass production to mass personalization and a human-centric model of services using modern technologies. This study aims to examine the Industry 5.0 and its applications in various sectors. It also pinpoints the possible challenges and opportunities for libraries promoting library services, resources, infrastructure and spaces to attract the modern library users.

Relevant literature on the topic was searched using keywords including Industry 5.0, modern technologies, artificial intelligence, sustainability and green shift and libraries. The researcher also used his experience and the initiatives of the Punjab University Library, which is the oldest and largest university in Pakistan, to promote personalization and user-centered approaches.

Findings revealed challenges and opportunities for libraries. Using Industry 5.0, Libraries have the potential to understand users’ preferences, browsing history and information behavior, and accordingly design their services, resources, infrastructure and spaces. Libraries can serve their modern users through personalization and human centric approach such as content recommendations, alert and notification services, customized research assistant, inclusive and accessibility services, personalized event services and personalization through human books.

Professional library associations, library schools, library directors and libraries need to proactive learn and apply Industry 5.0 while establishing library services, resources, infrastructure and spaces. Library schools need to design their curriculum considering the Industry 5.0 technologies, tools, and platforms.

This study provides a pen picture of the Industry 5.0 and its impact on libraries. Potential challenges and opportunities are highlighted. These all initiatives required a visionary library leadership to take initiatives toward human-centered personalized services and attract the modern library users.

Open knowledge
Development and Learning in Organizations, Vol. 39, No. 1, pp.67-68

This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies.

This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context.

Knowledge hiding within a firm is damaging and undermines attempts to gain competitive advantage. Knowledge sharing is the opposite of this and boosts competitive advantage.

The briefing saves busy executives, strategists and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format.

Digital transformation in SMEs: global maps and trends of research
Dina Elsa, Henny Indrawati, Caska Caska
Strategy & Leadership, Vol. 53, No. 2, pp.132-159

This paper reviews the literature on digital transformation in SMEs. The general purpose of the paper is to provide an overview of the evolution of digital transformation research in SMEs globally and propose possible future research directions to advance digital transformation research in SMEs.

This study used a systematic review of the literature by conducting bibliometric analysis and content analysis. The research protocol included 51 articles collected in the Scopus database in the bibliometric analysis. The Scopus database was searched using Publish or Perish, while Excel and Mendeley assistance were used for quantitative investigation of the sample and bibliographic management. A bibliometric analysis was conducted by combining two software applications, Biblioshiny R Studio and VOSviewer.

Digital transformation (DT) research on SMEs has increased significantly especially after 2015. Using bibliometric analysis and science mapping, seven main research themes were found, namely digital technologies, dynamic capability, digitalization, small and medium enterprises, big data, manufacturing sector and innovation. Seven future DT research trends were also found, namely digital technology adoption, dynamic capability, adaptive leadership, digital literacy, sustainable innovation, managerial readiness and external support.

Compared to existing reviews, we adopt a broader approach and one that does not focus on specific aspects of DT, but adopts an integrated and holistic approach that provides a comprehensive overview of the DT literature in SMEs. In addition to quantitative analysis through bibliometrics, this study also integrated content analysis to determine future research opportunities and directions.

This paper is based on imported bibliographic data from Scopus. The findings of bibliometric analysis may be affected by the use of certain databases. Therefore, the results depend on the selected databases which may lead to different results. Although the literature review procedure was applied, it is possible that there were missed articles related to the topic discussed. The use of different indicators and depiction methods will also lead to different results. Therefore, future researchers should optimize these aspects.

Gaming the system
Development and Learning in Organizations, Vol. 39, No. 2, pp.51-52

This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies.

This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context.

Gamification is a powerful tool that can encourage increased engagement and productivity in the workplace, yet without careful consideration and calibration of rewards, it can be open to exploitation or detrimental to the wellbeing of employees.

The briefing saves busy executives, strategists and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format.

Navigating data visualization: service landscapes and librarian roles in academic libraries
Guan Wang, A. Noorhidawati, A.M.K. Yanti Idaya, Ye Yuan
The Electronic Library, Vol. 43, No. 2, pp.194-210

Under the dual influence of external data environments and growing internal demands, data visualization (DV) has become a crucial component of library services, introducing new trends in library practices. This study aims to: i) evaluate data visualization services (DVS) in Chinese academic libraries and ii) analyse the evolving roles and responsibilities of librarians within this context.

This exploratory study is based on an environmental scan of websites from academic libraries in China’s top-tier universities. Using purposive sampling, data were collected through semi-structured interviews from 24 experienced librarians across 16 academic libraries and analysed thematically using NVivo.

The findings reveal that libraries’ DVS practices are evolving to accommodate a broader user base, with librarians’ roles becoming more multidimensional, including stewards, trainers, collaborators and advocates. Emerging responsibilities, such as enriching DV resources, guiding users in data interpretation, developing users’ DV skills and promoting data-driven innovation, have become more prominent.

This research provides valuable insights into the evolving roles of librarians in the age of open data, while offering practical guidance and inspiration for libraries and librarians keen to implement DVS to support such initiatives. It assists libraries in better meeting user needs and promotes data-driven innovation.

To the best of the authors’ knowledge, this is the first study to examine the practice of DVS in Chinese academic libraries. The findings affirm the importance of DVS and offer practicable strategies to strengthen its implementation, which pave the way for future studies on library services and librarian competencies in DV-related fields.

This post provides resources to anyone interested in developing a university business curriculum. It includes elements such as:
> Scheme of Work
> Learning Outcomes
> Learning Resources
> Assessment Models

You can download the PDF version of the presentation video below. Please do contact me with any questions. I am happy to consult you or your organisation to apply this model. – Dr Anthony ‘Skip’ Basiel abasiel@gmail.com or WhatsApp +44(0)7771998799

This video is 21 minutes long.

Design really does matter — layout and page composition can make a critical difference in helping your customers get the most out of your documentation. Here are some formatting tips to help improve your knowledge base articles. Read the full article

Richard Chaplin | LinkedIn, Founder of the Managing Partners’ Forum |, hosted the 101^st “Retuning your firm” webinar this morning. Guests included: Jeremy Beard | LinkedIn, John Rowland | LinkedIn, Alastair Beddow | LinkedIn and Alex Hamilton | LinkedIn. It was a masterclass in how to pack a 45 minute webinar with valuable insights. The theme was the accelerating rate of change. Managing Partners’ Forum – Political, Practice and Marketing Trends

Political update – John Rowland

John Rowland, the award-winning public affairs advisor at H/Advisors Cicero provided a rapid update on the fast-moving political scene

• Polls – with four years to the next election
  • Reform at 27% are the party polling best in the country – over several polls over several weeks “Gliding upwards”
  • Labour at 25% are descending gently (at the election they were at 33-34%)
  • Conservatives at 21% are drifting downwards – sustainable for Badenoch?

• Reform hasn’t said a lot but are creating a mood – we are at the ceiling of Reform support which is not capable of forming a majority government but could form strong alliance. But they are setting the agenda – and professionalising their operation

• The Government has its Spring Statement on 26^th March – Rachel Reeves says it’s not a budget but might look like a Budget. There are public concerns about the economy and cost of living crisis and the fiscal gap (tax receipts lower, rates higher, growth not there yet)

• The UK is not the “problem child” it was – there’s a focus on Germany and US economy. The UK may turn the corner on investor confidence – although Government action is needed to unpick damage done to business confidence

• A more subtle issue is the Government exerts more control on the regulators to prioritise growth. Several heads of regulatory bodies have gone (CMA, Financial Ombudsmen and NHS England)

• Starmer handled the trip to Washington well (as did President Macron of France). He announced an increase in defence spending (funded from overseas aid) before he went and handed over a letter from the King for an historic second State Visit. He appeared to be supported on the Chagos Islands deal. UK’s position outside the EU and the trade surplus with the US may be helpful in avoiding tariffs

• More of a concern is the 5m UK people who are not working

Practice update – Alastair Beddow

Alastair Beddow, Managing Director at Meridian West , ran through the results of Strategy and Marketing Benchmark 2025 (conducted with the Managing Partners’ Forum and ICON.

He shared four headlines:

1. Despite a drop of economic activity and political uncertainty – the majority of firms anticipate continued growth this year

2. However, concerns about fee pressures and margin erosion mean that efficiency and commerciality dominate the management agenda

3. CMOs are planning for above inflation budget increases but they are under pressure to manage a diverse range of MBD investment priorities

4. Six in 10 firms are increasing their spend on client listening as this becomes more embedded in business as usual across firms with insights gained being used to inform future strategy

Then he looked at data supporting those trends.

Marketing/BD update – Alastair Beddow

With regards to the marketing and business development topics dominating the agenda, the following activities were highest up the priority list for the marketing and business development team

• 36% Improvements to client service and experience (CX- Client Experience)
• 34% Providing training and support to fee-earners
• 30% Enhancing team productivity and engagement
• 28% Investing in automation and artificial intelligence (AI)
• 25% Research into growth markets and segments
• 23% Establishing/improving account management
• 22% Creating thought leadership and insight campaigns
• 20% Obtaining feedback from clients (client listening)
• 19% Refreshing or relaunching the firm’s brand
• 13% Marketing via social and digital channels
• 13% Personalising more content to individual clients
•  8%  Improving project management on client engagements
•  6%   Launching new products and services

He added:

• CX remains a clear focus for CMOs and BD leaders
• Firms anticipate a 6.5% increase in marketing/BD budgets this year
• 57% firms saw their team headcount increase over the last year
• A 5.5% increase in MBD team headcount
• Six in 10 firms are increasing their spend on client listening (four in 10 have a structured approach to client listening)
• Two thirds say their firm’s Managing Partner is a key CX champion

Technology disruption (but not AI)

Alex Hamilton, CEO of Radiant Law, was a partner in a top US law firm.  Whilst technically a law firm, his tech-enabled, timesheet-free and fixed-price business welcomes market concerns about fee pressures as he has created great relationships with clients improved lawyer productivity.

“We’re not really using AI and not terribly excited by AI. We do things differently. We improve lawyer productivity and perform at a much higher level by using deterministic tech. We have a focus on continuous improvement and a lean approach. There’s a lot of waste in law”.

He reflected that client stakeholders want different things. The legal team wants stuff off their desk and to show they are adding business value. The business people want faster contracting. The legal and business people talk completely different languages – so who do we listen to?

Others points discussed

• Decline in the number of people entering the accountancy market – will there be enough to support growth ambitions? Alastair reported that clients are concerned about capacity issues and the challenge of firms delivering great service.
• Richard noted management’s focus on efficiency whilst at the same time, MBD teams are looking for growth. Alex suggested that it was not a clash between marketing and productivity as they are bringing down the budget in marketing but doing more – industrialising how we do that
• John indicated that marketing was not an area where they are looking to trim – they see it as a core function of the business. The market has become more competitive – and marketing has a role to play in cutting through that. Having been through a sale, rebrand and acquisition, people need to know who we are
• Alastair noted that firms are looking at new markets, service diversification and international expansion – and must learn the lessons of past economic recessions where marketing was slashed. Meanwhile, client satisfaction has decreased, there are capacity challenges, there’s people churn and firms have grown quickly with new services and markets – so it hard to be consistent. Ways of working have changed and more people are pushing back.
• Jeremy commented on the need to have a strong management team around you – both operational heads as well as partners to ensure you continue to act for the right clients. . Richard asks Jeremy – video of how you start and end the day – how did you cope?

The next Re-Tuning your firm | Managing Partners’ Forum is on Friday 28^th March – Live at Institute of Directors which is where Summit | Managing Partners’ Forum takes place

Other trends data

Brexit impact

Five key impacts of Brexit five years on – BBC News (31^st January 2025)

1. Negative trade impact
2. Big fall in EU immigration but increases in net migration from the rest of the world
3. The end of Freedom of movement affected tourists and business travellers. The impact of the new electronic Entry Exit System (EES) and European Travel Information and Authorization System (ETIAS) is unclear
4. According to the latest government count there were 6,901 individual pieces of retained EU law
5. The UK’s gross public sector contribution to the EU Budget in 2019-20, the final financial year before Brexit, was £18.3bn, equivalent to around £352m per week. It has not paid since December 2020. The net fiscal benefit to the UK from not paying into the EU Budget is closer to £9bn per year

Professional Services Trends

Navigating the Professional Services Landscape: Top Business Trends to Watch | Kantata Software (July 2024) Originally published in Forbes

1. Prioritising exceptional client experiences
2. Cultivating existing talent and removing barriers to success
3. Tackling rising technical debt
4. Staying ahead of emerging business trends

CIM marketing Trends for 2025

Top marketing trends for 2025 | CIM Content Hub (10^th January 2025 – paywall)

From 10 industry experts from Uber Advertising, Tripadvisor, McKinsey & Company, Global and others

1. Commerce media’s role in personalised advertising
2. Leveraging data-driven partnerships for smarter advertising
3. The expanding role of the CMO
4. Generative AI reshaping audio advertising
5. Growth of programmatic out-of-home advertising
6. AI-driven hyper-personalised content
7. Embracing composable architecture for marketing flexibility
8. The end of Google’s market dominance
9. Remix culture: a game changer for brand building
10. A shift towards fandoms and peer trust

Related trends articles

Private client commercial conversations March 2024

International Marketing Benchmark 2024 from Meridian West February 2024

Book review: The Strategy Book by Max McKeown November 2023

Managing Partners’ Forum – Highlights September 2023

Professional services benchmark and accountancy profession September 2023

Context and curiosity drive commerciality and pricing September 2023

know about commercial awareness by Christopher Stoakes August 2023

Managing Partners’ Forum Strategy Summit July 2023

Key Account Management (KAM) – Research companies June 2023

Annual International Marketing Benchmark – PM Forum and Meridian West January 2023

Be more strategic – PESTLE, Positioning and Plans December 2022

Nine Marketing and Business Development trends in 2021 December 2021

Law Society Research Report – Future Worlds 2050 July 2021

Law firm analysis – MHA Legal Benchmarking Annual Report 2020 January 2021

Digital and soft skills – Microsoft on skills needed for an inclusive economy September 2020

Emerging marketing and business development roles September 2020

Marketing basics – Marketing audits with onions and pestles August 2020

 

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Creating a knowledge base is only half of the battle; you also need to keep it up to date. Help your customers get the most out of your content with regular knowledge base maintenance.Read the full article