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12 Dec 21:14

Your Search Diet - How Not to Over EAT

by jennyhalasz@slideshare.net(jennyhalasz)

Like being healthy in life, being healthy in business requires balance and a full understanding of the elements involved.
03 Dec 10:05

Liens vagabonds : Netflix, gagnant contesté de toute part !

by Méta Media

A retenir cette semaine :

#Netflix : après un premier Lion d'or décerné au film Roma à la Mostra de Venise en septembre dernier, Netflix décroche cette fois trois Oscars, mais pas le meilleur film. Une distinction qui fait débat dans le monde du cinéma. Quelle légitimité a la plateforme ? Dans un article de la Harvard Business Review, les auteurs estiment que « Netflix n'est pas une firme qui vend tel ou tel film à de nombreux clients. Elle vend de nombreux films à telle ou telle personne, sous forme de forfaits ». On est bien loin de l'économie du cinéma

Sur le terrain de la SVOD, Netflix fait face à un nouveau concurrent en Europe : la BBC s'allie à son concurrent privé ITV en lançant une offre à £5 par mois pour investir ce marché en plein essor. Il est vrai que les derniers chiffres de croissance de l'entreprise de Los Gatos ont de quoi impressionner. Rien qu'en France, l'application Netflix a été téléchargée plus de 10 millions de fois en 2018, juste devant le Royaume-Uni. En terme de revenus, c'est l'Allemagne qui rapporte le plus avec plus de 35 millions de dollars générés. Pourtant, la plateforme pourrait gagner encore plus. Netflix perdrait ainsi 192 millions de dollars par mois à cause du partage de comptes. En attendant, un nouveau service extérieur joignant l’utile à l’agréable est possible sur la plateforme : il y est désormais possible d'apprendre une langue étrangère.

Aussi cette semaine :

Après l'annonce du Président Abdelaziz Bouteflika qu'il souhaiterait briguer un cinquième mandat, l'Algérie fait face à un fort mouvement de contestation. Alors que des journalistes étaient venus couvrir un rassemblement contre la censure, certains d'entre eux ont été interpellés, et incarcérés. Pierre Haski, Président de Reporters Sans Frontières fait état d'une « vraie bataille de l'information ».

Les difficultés économiques des médias s'étendent désormais aux agences de presse : après l’AFP, Reuters va supprimer 25 postes de journalistes en France

Les Etats-Unis commencent à penser qu’il faudrait renforcer l’arsenal législatif pour protéger la vie privée.

3 CHIFFRES

LE GRAPHIQUE DE LA SEMAINE

Infographic: Netflix's Growing European Audience | Statista
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11 Nov 14:34

How Crypto Payments Help to Avoid Commissions and Save Money, Explained

by Cointelegraph By Elijah Bradley

As cryptocurrencies become more and more mainstream, using them instead of fiat or banking transfers is sometimes more profitable

11 Nov 14:00

Gartner: Blockchain Tech Used by Enterprises at Risk of Becoming Obsolete Within 18 Months

by Cointelegraph By Thomas Simms

Research firm Gartner has warned 90% of the blockchain technology used by enterprises will need to be replaced within the next 18 months

11 Nov 14:00

Internet Authority: History of Centralized Companies Being Hostile Toward Crypto

by Cointelegraph By Henry Linver

Centralized companies can still adversely impact crypto — the community reacts: “There is a solution to protect ourselves against potential abuse”

09 Sep 20:32

Building a Direct-to-Consumer Strategy Without Alienating Your Distributors

by Ned Calder
Three Images/Getty Images

Companies increasingly use digital technologies to circumvent distributors and enter into direct relationships with their end-users. These relationships can create efficient new sales channels and powerful feedback mechanisms or unlock entirely new business models. But they also risk alienating the longstanding partners that companies count on for their core business.

The auto industry is a case in point. Porsche’s Passport program allows consumers to subscribe via a phone app to a range of vehicles for a fixed monthly fee. Your chosen Porsche is delivered to your house with insurance and maintenance as well as unlimited miles and flips to other models included. But if you’re a Porsche dealer, how do you like this idea? Now consider that similar subscription services are being offered by Volvo, Lincoln, BMW, and Mercedes, with more to follow.

These direct-to-consumer offers threaten the very livelihood of dealerships, who historically have owned the customer relationship. And many dealers are pushing back. The California New Car Dealers Association lobbied for a law that required subscriptions to go through dealers. Volvo’s program has elicited so much criticism that dealers have mobilized the Indiana state legislature to outlaw the business model.

This is but one example of the digital Catch-22, the dilemma that most manufacturers and service companies face when creating new distribution channels. As a result, many B2B companies remain stuck in a stalemate. Writing in the Sloan Management Review, Boston College professor Gerald Kane noted that 87% of executives surveyed indicated that digital technologies will disrupt their industries to a great or moderate extent. Yet fewer than half felt that their companies were doing enough to address this disruption.

We frequently find that executive teams understand the potential of a reinvented distribution strategy; however, they are unclear on how to proceed. While the opportunity is compelling, so is the potential to upset existing distribution partners and thereby damage the core business. Disgruntled distribution partners may retaliate in ways such as switching to rivals, favoring competing products, or even lobbying for legislative remedies.

How can companies position for the future without putting their current business in jeopardy? Here are three strategies for developing digital distribution approaches that minimize risk:

Embrace Stealth

In the past, companies looking to test new business models could quietly enter a new geography free from restrictive distribution contracts that limit their ability to go direct in their traditional geographies. But that is harder to do in the digital age, as customers and partners anywhere can easily see what you’re doing online.

Alternatively, the company can operate in stealth mode by targeting customer segments that have been poorly served or ignored by traditional distributors.

Recently, Verizon quietly launched a startup called Visible which offers no-contract mobile phone service subscriptions for a $40 flat fee and is only available for purchase through an app. This model competes mainly with smaller-brand, low-end providers and may not be seen as a direct threat by Verizon’s massive distribution network of company-owned, partner, and authorized reseller stores that are selling higher-margin services.

Sometimes, an entirely new product provides the right entry point. Starting in 2011, Mercedes chose to develop direct distribution capabilities for electric bicycle sales under its Smart brand.

Mercedes’ strategy preserves its traditional distribution network for its major lines of vehicles, while enabling the company to build the capabilities and infrastructure needed to support a reinvented distribution strategy — selling to consumers rather than through traditional dealerships.

Create Hooks

Distribution partners willingness to retaliate can be minimized if companies are able to create hooks that compel and reduce their negotiating leverage. There are many ways to build hooks, including bundling products, monopolizing a category, or developing features that are indispensable to a subset of customers.

For example, Cree Inc. made a splash when it introduced affordable consumer LED lightbulbs in the early 2010s. For several years the company was both a cost and product feature leader in the category. This enabled Cree to command significant shelf space in Home Depot, while simultaneously building a direct-to-consumer business. During this period, Home Depot was compelled to carry Cree products. This dual distribution strategy resonated with both consumers and investors — as Cree’s stock price tripled from 2011 to 2013.

In 2012, with the launch of the Surface product line, Microsoft began directly competing with the manufacturers and OEMs who had been its distribution partners for decades. Microsoft was able to do so largely due to its monopolization of the desktop operating system market. Traditional Microsoft partners such as Acer, Lenovo, HP, and Dell were already hooked on Windows and had little choice but to accept Microsoft’s direct-to-consumer strategy.

In fact, many of Microsoft’s partners, at least publicly, were supportive of the Surface. In 2012, Acer’s founder, Stan Stinh, indicated that he believed the Surface was only intended to stimulate market demand and that “once the purpose [was] realized, Microsoft [would] offer more models.” Today, the Surface product line has a greater share than Acer does in the U.S. market for personal computers.

Minimize Pain

Supporting downstream partners’ business can also reduce the risk of retaliation.

The heavy equipment manufacturer Caterpillar, for example, introduced a vehicle management platform that provides customers with insights on vehicle utilization, health, and location. The platform is sold directly to customers — frequently removing downstream partners from the sales process. Ultimately, though, the platform benefits partners because it alerts customers when they need to get their equipment serviced by these local partners — a key revenue stream for Caterpillar’s distributors.

UnitedHealth Group, one of the largest health insurers in the U.S., is on the verge of becoming the nation’s largest employer of physicians. But under its subsidiary Optum, UnitedHealth Group has pursued an aggressive M&A strategy to build its direct-to-consumer capabilities while being careful to not upset traditional healthcare providers. For example, Optum has continued to accept over 80 types of health insurances across its facilities and has avoided restricting United insurance customers to Optum-owned providers. Optum’s deliberate strategy has caught the industry’s attention, but to date has avoided direct retaliatory actions by incumbent healthcare providers.

Digital represents a significant opportunity for many B2B companies, but also risk. Failure to act enables competitors and new entrants, while action risks retaliation from existing partners. To break this stalemate, leadership should align on the imperative to act, acknowledge the risks of action, and identify the right strategy with which to move ahead. Your long-term partners are more likely to stand by you if they see your direct-to-consumer move not as an act of aggression but as a plan for growth.

09 Sep 20:31

Why CRM Projects Fail and How to Make Them More Successful

by Scott Edinger
PM Images/Getty Images

In 2017, CIO magazine reported that around one-third of all customer relationship management (CRM) projects fail. That was actually an average of a dozen analyst reports. The numbers ranged from 18% to 69%. Those failures can mean a lot of things — over-budget, data integrity issues, technology limitations, and so forth. But in my work with clients, when I ask executives if the CRM system is helping their business to grow, the failure rate is closer to 90%.

The primary reason they miss the mark in helping companies increase revenue is that CRM systems are too often used for inspection — to report on progress, improve accuracy of forecasts, provide visibility, predict project delivery dates, and provide a range of other business intelligence — rather than creating improvement in the sales process. Front-line sales professionals and managers rarely find the majority of these capabilities useful in winning more business for the company.

CRMs today also serve a lot of masters, from executives in the C-suite, technology, marketing, finance, and, oh yeah, sales. They try to address more objectives than are reasonable for any software system. I recently led a working session for a team of executives looking to select a CRM provider. By the time everyone weighed in on their must-haves, we had identified 23 unique objectives. With such a diluted focus, it’s virtually impossible to succeed.

I saw this clearly at another client where there was a wide range of answers to the question, “Was the CRM implementation a success?” The EVP of marketing was pleased she could now track the assignment of every single lead. The CIO was unhappy about data integrity issues that arose from the integration of more than 20 discreet databases. The EVP of sales liked the easy-access dashboard to report on metrics and the forecast. Sales management was less positive but acknowledged that it helped them monitor activity. And the sales team — well, they mostly hated it. They had to enter a lot of information that added little value (for them), and provided no help in selling more. Because the sales team had so little incentive to keep up with the data entry requirements, the quality of the data in the system became less and less reliable over the following year. The result? Incomplete or inaccurate information from the CRM was exported into Excel spreadsheets for further manipulation by each level of management.

If you want your CRM implementation to increase revenue (which it only will if it enables your sales organization to increase sales), I recommend doing the following:

Re-think your CRM as a tool to increase revenue. Period. That is why you bought this system and spent millions, sometimes tens of millions, on its deployment. Broadcast this message loud and clear from the CEO and sales leadership. Your sales team needs to understand that they drive the execution of your strategy every time they interact with a client or prospect. Your implementation of a CRM system is not about the technology, and it is not to fulfill an administrative reporting requirement, which is how too many sales teams view them. The CRM is a tool to help them sell more, access support resources during sales cycles, and manage their territory or “book of business.” If the sales team recognizes the value of this tool, you’ll get all the metric and forecast information you desire. If not, you’ll be back to modifying guesses in Excel spreadsheets.

Integrate your marketing efforts with sales activity. Historically, these two functions collaborate on CRM implementation so poorly it’s almost a cliché. Marketing blames sales for not following up on all the leads produced. Sales points out that marketing doesn’t understand field reality and truly qualified leads. Overcoming these interdepartmental squabbles requires a collaborative effort by both teams throughout the sales process. Early in the sales cycle, marketing and sales have roles to play in identifying and qualifying opportunities to actively pursue. As sales cycles develop, they should have a shared understanding of what constitutes a qualified lead, as well your ideal customer profile — both in terms of the company and level of buyer. This helps filter out business you shouldn’t pursue. Later in the sales cycle, marketing works with sales to create materials that can be customized to client objectives and case studies, instead of the generic collateral sales teams often see as low value. Finally, working together on win/loss analysis provides an active feedback loop for joint planning and addressing future needs. This kind of integration, using your CRM as the glue, will improve marketing’s efforts to create gravity with prospects, and sales’ ability to accelerate sales cycles. It’s an advantage for the business if you can use at least some of the same metrics to evaluate the success of both departments.

Managers provide coaching to improve, not reporting to inspect. The pivotal role in driving CRM success is not individual sales people. It’s sales management. They will determine how the sales team uses and experiences the CRM. If they use it solely to check on the amount of activity, call volume, or other measures of efficiency, it’s of low value to the sales team and likely be rejected or filled with fictional data. Instead use it as a tool to jointly create strategies for major opportunities, and help the sales team to maximize opportunities by coaching them throughout the sales process. I’ve written in the past about the high value of coaching and the fact that it’s rarely done well. But CRM can be a powerful mechanism to support coaching for individual sales calls, as well as opportunity, account, and territory management.

CRM is an important tool, but it is just a tool. When the laptops are shut down for the day, it’s your sales team that is responsible for bringing value to clients and driving revenue. Implement your CRM with that in mind and you’ll be pleased with your ROI.

09 Sep 20:28

How to Choose Your First AI Project

by Andrew Ng

Pick a quick win to build internal support.

09 Sep 20:24

5G’s Potential, and Why Businesses Should Start Preparing for It

by Omar Abbosh

The technology will allow for a range of new products and services.

09 Sep 20:18

Do Your Data Scientists Know the ‘Why’ Behind Their Work?

by Thomas C. Redman

If not, is it their fault — or yours?

15 Aug 13:32

MusicBrainz Server update, 2019-08-08

by yvanzo

This summery release brings one main new feature: collaborative collections! As an editor, you can now share your collections with others. This is mainly intended for community projects, but it can also be a good way to, say, have a shared “Music we have at home” collection with your family, or collect artists with funny names with your friends. You decide how to use it!

To add collaborators to your collections, edit the collection and enter the editors you’d want as collaborators in the appropriate section (suggestion: ask first whether they’re interested, then add them!). Once they’ve been added as collaborators, they’ll be able to add and remove entities from the collection in the same way as you, but they won’t be able to change the title / description: that’s still only for the collection owner to change.

CDs collection shared as a cloak for everyone to see

The release also comes with a bunch of small improvements and bug fixes, including a couple about collections, and continues migrating to React.

Thanks to Ge0rg3 and sothotalker for their contributed code. Also, thanks to chaban, chiark, cyberskull, Dmitry, hibiscuskazeneko, jesus2099, Lotheric, mfmeulenbelt, psychoadept and everyone who tested the beta version, reported issues, or updated the website translations.

The git tag is v-2019-08-08.

Bug

  • [MBS-8867] – Guess Case normalizes “C’mon” as “C’Mon”
  • [MBS-9512] – Changing recording name to empty string should not be allowed
  • [MBS-10100] – ISE without “non-required” attributes for admin/attributes/Language/create
  • [MBS-10133] – Error message when sending an empty query to the WS is unclear
  • [MBS-10212] – SoundCloud URL with trailing slash is not displayed with user name in artist sidebar
  • [MBS-10218] – Regression: Cover Art tab not selected / highlit on release page
  • [MBS-10233] – Regression: ISE when trying to cancel a “add release annotation” edit

Improvement

  • [MBS-8569] – Don’t display ended legal names in the overview page for artists
  • [MBS-9381] – Show user’s own private collections in the list of collections for an entity
  • [MBS-10135] – Support WikiaParoles as its own site rather than LyricWiki
  • [MBS-10139] – Clarify why recording lengths can’t be edited when non standalone
  • [MBS-10210] – Only allow allowed frequencies in language admin form
  • [MBS-10215] – Make ISO number required for script admin form
  • [MBS-10217] – Explain what renaming artist credits does when editing artist
  • [MBS-10219] – Add Muziekweb to other DBs whitelist, with sidebar display
  • [MBS-10222] – Pull legal name alias instead of legal name artist for the relationship Artist-Artist “perform as/legal name”
  • [MBS-10224] – Don’t show the same legal name string multiple times in artist overview
  • [MBS-10246] – Don’t assume all event collections are attendance lists
  • [MBS-10272] – Convert the header / navbar to Bootstrap

New Feature

  • [MBS-8915] – Allow editors to choose delimiter in track parser
  • [MBS-9428] – Allow multiple users to share one collection

React Conversion Task

  • [MBS-9914] – Convert the area public pages to React
  • [MBS-10047] – Convert /oauth2/ pages to React

Other Task

  • [MBS-10131] – Update LyricWiki domain to lyrics.fandom.com

31 Jul 22:16

Blockchain Governance: How Boundaries Can Help the Blockchain to Scale

by Jenny Scribani

Blockchain Governance: How Boundaries Can Help the Blockchain to Scale

How Boundaries Can Help the Blockchain to Scale

The blockchain offers a long overdue upgrade for our changing economy.

However, the world isn’t quite ready for broadscale blockchain adoption. The technology is still in its relative infancy, and to reach its true potential the blockchain must be able to successfully replace existing systems while also operating at meaningful scale.

Today’s infographic comes to us from eXeBlock Technology, and it explores how good blockchain governance can help solve the pressing challenges around blockchain adoption and implementation, including the ever-present issue of scalability.

So You Say You Want A Blockchain

While it’s relatively easy to implement a blockchain in an organization, it’s far more difficult to decide just how that network should operate. For a blockchain to generate and hold any real competitive advantage, there are a few key questions to consider:

Scalability
How big can you grow before sacrificing efficiency? As the blockchain grows, so do the number of nodes to process transactions. This creates a bottleneck and slows down the system.

Privacy
What are your privacy needs? The attraction of the blockchain lies in its ability to decentralize information and make it transparent, but this creates a challenge for corporations who use the blockchain to handle sensitive or proprietary information.

Interoperability
Will your blockchain play nicely with other blockchains? There are a number of blockchain configurations – and to date, no cross-industry standards. This means your blockchain might not collaborate smoothly with another blockchain, particularly if the security standards are mismatched.

How Can Blockchain Governance Help?

Blockchain governance is concerned with solving these problems by:

  • Reducing scalability obstacles by finding ways for blockchains to reach consensus faster without sacrificing decentralization
  • Providing a foundation for shared standards, so organizations can collaborate without risking the privacy of their data
  • Providing a framework for adaptability – a playbook for the blockchain to rely on when inevitable problems and security issues crop up

Think of governance as a constitution to help the blockchain run smoothly: it improves efficiency, encourages collaboration, and outlines a course of action when the system falters.

Types of Blockchains

There are four different types of blockchains, each with unique characteristics:

Federated

  • Operates under the leadership of a group, and access is limited to only members of the group
  • Due to limited membership, they are faster, can scale higher, and offer more transaction privacy

Permissioned/private

  • Access might be public or restricted, but only a few users are given permission to view and verify transactions
  • Ideal for database management or auditing services, where data privacy is an issue
  • Compliance can be automated, as the organization has control over the code

Permissionless/public

  • Open-source and available to the public
  • Transactions are transparent to anyone on the network with a block viewer, but anonymous.
  • The ultimate democracy – this fully distributed ledger disrupts current business models by removing the middleman
  • Minimal costs involved: no need to maintain servers or system admins

Hybrid

  • A public blockchain, which hosts a private network with restricted participation
  • The private network generates blocks of hashed data stored on the public blockchain, but without sacrificing data privacy
  • Flexible control over what data is kept private and what is shared on the public ledger
  • Hybrid blockchains offer the benefits of decentralisation and scalability, without requiring consensus from every single node on the network

Within each of these systems, blockchain governance outlines different standards for privacy and security. Governance determines how consensus is reached, and how many nodes are required. It establishes who has access to what information, and how that data is encrypted. Governance sets up the foundations for blockchains to scale according to the needs of the organization.

Blockchain governance exists to smooth the transition to widespread adoption, providing organizations with dynamic solutions to make their blockchain suit their needs without sacrificing the security of decentralization.

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31 Jul 22:14

How Artificial Intelligence is Transforming Clinical Trial Recruitment

by Iman Ghosh

How Artificial Intelligence is Transforming Clinical Trial Recruitment

How AI is Transforming Clinical Trial Recruitment

The medical world is shifting underneath our feet.

To keep up with the rising demands of empowered patients, physicians and pharma businesses regularly test innovative treatments and medicines during rigorous clinical trials.

But one misguided move can trigger a domino effect, such as when the wrong patients are selected for a clinical trial.

Today’s infographic comes to us from Publicis Health, and it highlights why the current model of clinical trial recruitment urgently needs to change.

The Cost of Clinical Trials

Clinical trials help to determine if a new treatment, drug, or device is safe for the larger patient population.

Patients are at the heart of these clinical trials, and poor patient recruitment has dire consequences:

  • 50% of sites enroll one or no patients in studies
  • 85% of clinical trials fail to retain enough patients
  • 80% of all clinical trials fail to finish on time

A single trial can cost anywhere from $44 million to $115 million. But here’s the kicker – according to a CenterWatch survey, delays can cost a trial between $600,000 and $8 million per day.

For these reasons, it’s crucial for pharma trial sponsors to find the right fit for clinical trials from the start.

A 360° View

The healthcare industry is moving towards a people-based marketing approach, to discover and engage the right patient one-on-one.

Advanced technology and connected patient data work in tandem with millions of real-time consumer behaviors, creating a rich and accurate profile of the perfect patient match.

The use of artificial intelligence, machine learning, and predictive analytics unearth further insights, weighting those patients with the behavioral tendencies most suited for the trial:

Omni-channel targeting
Actively reaching out to patients, wherever they are.

Predictive analytics
Continually refining media channels and messaging to further patient interest.

Ongoing communications
Nurturing relationships with patients, starting with the initial outreach.

Transforming Value

Applying a people-based approach to patient recruitment has a myriad of benefits, many of which live on long after the original trial’s completion.

  Advantage Value added
Recruitment - Accurate insight generation
- Real time optimization
- Faster and improved quality
- More efficient
- Increased conversion
- Reduced costs
Engagement - Behavioral-based messaging
- Personalized trial participation experiences
- Precise engagement at scale
- Drives patient adherence and retention during a trial
Long-term benefits of data collected - Develops patient profiles for future trials
- Guides the planning of the patient demographic
- Informs drug launch activities
- Accelerates recruitment and reduces start-up costs
- Speeds up commercialization of new drugs
- Supports disease awareness and educational campaigns

As clinical trials are successfully completed on time – allowing new drugs to reach the market faster than before – patients will benefit from easier access to groundbreaking treatments.

This is part five of a seven part series. Stay tuned by subscribing to Visual Capitalist for free, as we wrap up with the final two transformative forces shaping the future of healthcare.

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31 Jul 21:19

Data Analysis 9: Data Regression - Computerphile

by Computerphile

Real life doesn't fit into neat categories - Dr Mike Pound on some different ways to regress your data. This is part 9 of the Data Analysis Learning Playlist: https://www.youtube.com/playlist?list=PLzH6n4zXuckpfMu_4Ff8E7Z1behQks5ba

This Learning Playlist was designed by Dr Mercedes Torres-Torres & Dr Michael Pound of the University of Nottingham Computer Science Department. Find out more about Computer Science at Nottingham here: https://bit.ly/2IqwtNg

This series was made possible by sponsorship from by Google.

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This video was filmed and edited by Sean Riley.

Computer Science at the University of Nottingham: https://bit.ly/nottscomputer

Computerphile is a sister project to Brady Haran's Numberphile. More at http://www.bradyharan.com
31 Jul 21:19

Data Analysis 6: Principal Component Analysis (PCA) - Computerphile

by Computerphile

PCA - Principle Component Analysis - finally explained in an accessible way, thanks to Dr Mike Pound. This is part 6 of the Data Analysis Learning Playlist: https://www.youtube.com/playlist?list=PLzH6n4zXuckpfMu_4Ff8E7Z1behQks5ba

This Learning Playlist was designed by Dr Mercedes Torres-Torres & Dr Michael Pound of the University of Nottingham Computer Science Department. Find out more about Computer Science at Nottingham here: https://bit.ly/2IqwtNg

This series was made possible by sponsorship from by Google.

The music dataset can be found here: https://github.com/mdeff/fma

https://www.facebook.com/computerphile
https://twitter.com/computer_phile

This video was filmed and edited by Sean Riley.

Computer Science at the University of Nottingham: https://bit.ly/nottscomputer

Computerphile is a sister project to Brady Haran's Numberphile. More at http://www.bradyharan.com
20 Jul 09:44

Flash - L’ADN, votre nouveau support de stockage de données

L’ADN est en bonne voie pour devenir le support de stockage de données de demain. C’est notamment la startup Catalog qui travaille sur le sujet et pour démontrer leur innovation, ils ont copié l’intégralité du Wikipédia anglophone dans de l’ADN.


N’hésitez pas à vous abonner au podcast, et surtout à le partager autour de vous ! 

Site : www.anti-brouillard.fr 

Instagram : www.instagram.com/antibrouillard/

Twitter : www.twitter.com/Anti_brouillard 

Facebook : www.facebook.com/anti.brouillard.podcast/ 

Email : anti.brouillard.podcast@gmail.com

Et mon LinkedIn perso : www.linkedin.com/in/fabienroques/ 

___

Producteur et hôte : Fabien Roques

Crédit musique : Music by Joakim Karud youtube.com/joakimkarud

Crédit logo : Axel Delbrayère - http://delbrayere.com/  

06 Jul 18:46

Visualizing the Healthtech Revolution

by Iman Ghosh

Imagine being a patient in the early 19th century, when all ailments were considered “humors” to be ejected from the body. To restore balance, various techniques such as diets, natural herbs, or bloodletting with leeches were used – the only “technology” available at the time.

Even when the basic structure of modern medicine came into place, the average life expectancy was just 34 years old in 1913. A patient from that era would surely be amazed by the leaps and bounds that healthcare has undergone since then, thanks to the influence of technology.

The Healthtech Revolution

Today’s infographic dives into some of the technological advances that are pushing the boundaries of modern healthcare, and what this could mean for the sector.

The HealthTech Revolution

What is Healthtech?

Healthcare technology, or healthtech, is the use of technology to better treat patients. Many such inventions have been credited for saving countless human lives since the 1800s.

Medicines, devices, procedures, and even organizational systems contribute to expanding life expectancy and improvements in quality of life.

From Fiction to Reality

Breakthroughs such as robotic arms, 3D bio-printed organs, and virtual reality for pain relief are being developed in the medical sector, drawing influences from the big screen.

Technologies that were once the staple of science fiction movies are rapidly becoming realities.

— Jeroen Tas, Chief Innovation Officer, Philips

But there’s a less tactile application of technology from science fiction that will arguably have an even bigger impact on healthcare: artificial intelligence (AI).

By recognizing patterns in behavior and creating their own logic, machine learning algorithms are set to transform various aspects of healthcare ranging from the automation of mundane tasks to the creation of entirely new drugs.

Healthcare at our Fingertips

Healthcare is also getting more mobile and connected, putting the Internet of Things (IoT) and mobile health (mHealth) at center stage as sources of potential disruption.

These technologies can help in everything from offering patients a convenient way to book appointments and pay bills online, to allowing doctors to use electronic health records to access and share information.

Wearable devices and smartphone apps are spiking in adoption as they unlock the option to monitor and manage individual health anytime, anywhere. This is creating an explosion in personal health data, which consumers are willing to share with their doctor if it will benefit them or others.

The Coming Healthtech Boom

Artificial intelligence, IoT, and mHealth are contributing to rapidly expanding healthtech sector, and each are expected to experience rapid growth by 2025:

Healthcare segment Current* Projected (2025E) CAGR
Artificial Intelligence $2.1 billion $36.1 billion 50.2%
Global IoT $120.2 billion $543.3 billion 20.2%
Global mHealth $4.16 billion $111.8 billion 44.2%
*2018E for AI, 2017 for IoT, 2016 for mHealth.

While healthtech won’t replace your doctor anytime soon, but it will certainly change your experience with healthcare – both on the front-end and behind the scenes.

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06 Jul 18:28

The 15 Biggest Data Breaches in the Last 15 Years

by Jeff Desjardins

There’s no doubt that data breaches are a primary concern for people on the technological side of any modern business.

However, it’s increasingly the case that C-suite executives are catching wind of the potential business ramifications that these breaches can trigger.

In 2013, for example, the hacking of Yahoo not only compromised three billion email accounts – it also nearly jeopardized Verizon’s bid to acquire the company for $4.8 billion. At the end of the day, experts say that the breach knocked $350 million off of the sale price of Yahoo.

Counting Down the Breaches

Today’s infographic comes to us from Hosting Tribunal, and it highlights the biggest data breaches over the last 15 years.

The 15 Biggest Data Breaches in the Last 15 Years

Did you know that a whopping 14,717,618,286 records have been stolen since 2013?

It’s part of a much larger problem, and some experts anticipate that by 2021 the cost of cybercrime to the global economy will eclipse $6 trillion – a potential impact that would even supersede the size of the current Japanese economy ($4.9 trillion).

The 15 Biggest Data Breaches

Here are the most notable breaches that have occurred over the last 15 years, in ascending chronological order:

Year Company Impact
2004 AOL 92 million screen names and email addresses stolen
2013 Yahoo All 3 billion accounts compromised
2013 Target 110 million compromised accounts, incl. 40 million payment credentials
2014 eBay 145 million compromised accounts
2015 Anthem Inc 80 million company records were hacked, including Social Security numbers
2016 LinkedIn 117 million emails and passwords leaked
2016 MySpace 360 million compromised accounts
2016 Three 133,827 compromised accounts, including payment methods
2017 Equifax 143 million accounts exposed, including 209k credit card numbers
2016 Uber 57 million compromised accounts
2018 Marriott 500 million compromised accounts
2018 Cathay Pacific 9.4 million compromised accounts, including 860k passport numbers
2018 Facebook 50 million compromised accounts
2018 Quora 100 million compromised accounts
2018 Blank Media 7.6 million compromised accounts

Most of these breaches led to millions, or even billions, of records being compromised.

And while the motives behind cyberattacks can vary from case to case, the business impact of hacks at this scale should make any executive tremble.

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30 May 21:40

McDonald's Turns to AI for a Better Burger

by Claire Carroll

While Burger King may still be poking fun at AI following their robot commercials last year, other burger titans embrace AI as the next step in their evolution. This year, Wendy’s announced that it’s adding $25 million to its digital budget, while McDonald's took things to the next level when they purchased a marketing AI startup for over $300 million, their largest acquisition this century. Burger joints are leveling up their technology stack to drive seamless user experiences.

29 May 22:41

Hollywood is quietly using AI to help decide which movies to make

by James Vincent

AI will tell you who to cast and predict how much money you’ll make

The film world is full of intriguing what-ifs. Will Smith famously turned down the role of Neo in The Matrix. Nicolas Cage was cast as the lead in Tim Burton’s Superman Lives, but he only had time to try on the costume before the film was canned. Actors and directors are forever glancing off projects that never get made or that get made by someone else, and fans are left wondering what might have been.

For the people who make money from movies, that isn’t good enough.

If casting Alicia Vikander instead of Gal Gadot is the difference between a flop and smash hit, they want to know. If a movie that bombs in the US would have set box office records across Europe, they want to know. And now, artificial intelligence can tell them.

artificial intelligence turns filmmaking into fantasy football

Los Angeles-based startup Cinelytic is one of the many companies promising that AI will be a wise producer. It licenses historical data about movie performances over the years, then cross-references it with information about films’ themes and key talent, using machine learning to tease out hidden patterns in the data. Its software lets customers play fantasy football with their movie, inputting a script and a cast, then swapping one actor for another to see how this affects a film’s projected box office.

Say you have a summer blockbuster in the works with Emma Watson in the lead role, says Cinelytic co-founder and CEO Tobias Queisser. You could use Cinelytic’s software to see how changing her for Jennifer Lawrence might change the film’s box office performance.

“You can compare them separately, compare them in the package. Model out both scenarios with Emma Watson and Jennifer Lawrence, and see, for this particular film … which has better implications for different territories,” Queisser tells The Verge.

Cinelytic isn’t the only company hoping to apply AI to the business of film. In recent years, a bevy of firms has sprung up promising similar insights. Belgium’s ScriptBook, founded in 2015, says its algorithms can predict a movie’s success just by analyzing its script. Israeli startup Vault, founded the same year, promises clients that it can predict which demographics will watch their films by tracking (among other things) how its trailers are received online. Another company called Pilot offers similar analyses, promising it can forecast box office revenues up to 18 months before a film’s launch with “unrivaled accuracy.”

The water is so warm, even established companies are jumping in. Last November, 20th Century Fox explained how it used AI to detect objects and scenes within a trailer and then predict which “micro-segment” of an audience would find the film most appealing.

Looking at the research, 20th Century Fox’s methods seem a little hit or miss. (Analyzing the trailer for 2017’s Logan, the company’s AI software came up with the following, unhelpful tags: “facial_hair,” “car,” “beard,” and — the most popular category of all — “tree.”) But Queisser says the introduction of this technology is overdue.

“On a film set now, it’s robots, it’s drones, it’s super high-tech, but the business side hasn’t evolved in 20 years.”

“On a film set now, it’s robots, it’s drones, it’s super high-tech, but the business side hasn’t evolved in 20 years,” he says. “People use Excel and Word, fairly simplistic business methods. The data is very siloed, and there’s hardly any analytics.”

That’s why Cinelytic’s key talent comes from outside Hollywood. Queisser used to be in finance, an industry that’s embraced machine learning for everything from high-speed trading to calculating credit risk. His co-founder and CTO, Dev Sen, comes from a similarly tech-heavy background: he used to build risk assessment models for NASA.

“Hundreds of billions of dollars of decisions were based on [Sen’s work],” says Queisser. The implication: surely the film industry can trust him as well.

But are they right to? That’s a harder question to answer. Cinelytic and other companies The Verge spoke to declined to make any predictions about the success of upcoming movies, and academic research on this topic is slim. But ScriptBook did share forecasts it made for movies released in 2017 and 2018, which suggest the company’s algorithms are doing a pretty good job. In a sample of 50 films, including Hereditary, Ready Player One, and A Quiet Place, just under half made a profit, giving the industry a 44 percent accuracy rate. ScriptBook’s algorithms, by comparison, correctly guessed whether a film would make money 86 percent of the time. “So that’s twice the accuracy rate of what the industry achieved,” ScriptBook data scientist Michiel Ruelens tells The Verge.

An academic paper published on this topic in 2016 similarly claimed that reliable predictions about a movie’s profitability can be made using basic information like a film’s themes and stars. But Kang Zhao, who co-authored the paper along with his colleague Michael Lash, cautions that these sorts of statistical approaches have their flaws.

One is that the predictions made by machines are frequently just blindingly obvious. You don’t need a sophisticated and expensive AI software to tell you that a star like Leonardo DiCaprio or Tom Cruise will improve the chances of your film being a hit, for example.

Algorithms are also fundamentally conservative. Because they learn by analyzing what’s worked in the past, they’re unable to account for cultural shifts or changes in taste that will happen in the future. This is a challenge throughout the AI industry, and it can contribute to problems like AI bias. (See, for example, Amazon’s scrapped AI recruiting tool that penalized female candidates because it learned to associate engineering prowess with the job’s current male-dominated intake.)

Because AI learns from past data, it can’t predict future cultural shifts

Zhao offers a more benign example of algorithmic shortsightedness: the 2016 action fantasy film Warcraft, which was based on the MMORPG World of Warcraft. Because such game-to-movie adaptations are rare, he says, it’s difficult to predict how such a film would perform. The film did badly in the US, taking in only $24 million in its opening weekend. But it was a huge hit in China, becoming the highest grossing foreign language film in the country’s history.

Who saw that coming? Not the algorithms.

Warcraft
AI didn’t predict the success of ‘Warcraft.’ (In fairness, neither did the humans.)

There are similar stories in ScriptBook’s predictions for 2017 / 2018 movies. The company’s software correctly greenlit Jordan Peele’s horror hit Get Out, but it underestimated how popular it would be at the box office, predicting $56 million in revenue instead of the actual $176 million it made. The algorithms also rejected The Disaster Artist, the tragicomic story of Tommy Wiseau’s cult classic The Room, starring James Franco. ScriptBook said the film would make just $10 million, but it instead took in $21 million — a modest profit on a $10 million film.

As Zhao puts it: “We are capturing only what can be captured by data.” To account for other nuances (like the way The Disaster Artist traded on the memeiness of The Room), you have to have humans in the loop.

Andrea Scarso, a director at the UK-based Ingenious Group, agrees. His company uses Cinelytic’s software to guide investments it makes in films, and Scarso says the software works best as a supplementary tool.

“Sometimes it validates our thinking, and sometimes it does the opposite.”

“Sometimes it validates our thinking, and sometimes it does the opposite: suggesting something we didn’t consider for a certain type of project,” he tells The Verge. Scarso says that using AI to play around with a film’s blueprint — swapping out actors, upping the budget, and seeing how that affects a film’s performance — “opens up a conversation about different approaches,” but it’s never the final arbiter.

“I don’t think it’s ever changed our mind,” he says of the software. But it has plenty of uses all the same. “You can see how, sometimes, just one or two different elements around the same project could have a massive impact on the commercial performance. Having something like Cinelytic, together with our own analytics, proves that [suggestions] we’re making aren’t just our own mad ideas.”

But if these tools are so useful, why aren’t they more widely used? ScriptBook’s Ruelens suggests one un-Hollywood characteristic might be to blame: bashfulness. People are embarrassed. In an industry where personal charisma, aesthetic taste, and gut instinct count for so much, turning to the cold-blooded calculation of a machine looks like a cry for help or an admission that you lack creativity and don’t care about a project’s artistic value.

Ruelens says ScriptBook’s customers include some of the “biggest Hollywood studios,” but nondisclosure agreements (NDAs) prevent him from naming any. “People don’t want to be associated with these AIs yet because the general consensus is that AI is bad,” says Ruelens. “Everyone wants to use it. They just don’t want us to say that they’re using it.” Queisser says similar agreements stop him from discussing clients, but that current customers include “large indie companies.”

Hollywood is unlikely to accept AI having the final say anytime soon

Some in the business push back against the claim that Hollywood is embracing AI to vet potential films, at least when it comes to actually approving or rejecting a pitch. Alan Xie, CEO of Pilot Movies, a company that offers machine learning analytics to the film industry, tells The Verge that he’s “never spoken to an American studio executive who believes in [AI] script analysis, let alone [has] integrated it into their decision-making process.”

Xie says it’s possible studios simply don’t want to talk about using such software, but he says script analysis, specifically, is an imprecise tool. The amount of marketing spend and social media buzz, he says, are a much more reliable predictor of box office success. “Internally at Pilot, we’ve developed box office forecast models that rely on script features, and they’ve performed substantially worse than models that rely on real-time social media data,” he says.

Despite skepticism about specific applications, the tide might be turning. Ruelens and investment director Scarso say a single factor has convinced Hollywood to stop dismissing big data: Netflix.

The streaming behemoth has always bragged about its data-driven approach to programming. It surveils the actions of millions of subscribers in great detail and knows a surprising amount about them — from which thumbnail will best convince someone to click on a movie to the choices they make in Choose Your Own Adventure-style tales like Black Mirror: Bandersnatch. “We have one big global algorithm, which is super-helpful because it leverages all the tastes of all consumers around the world,” said Netflix’s head of product innovation, Todd Yellin, in 2016.

Netflix regularly changes the thumbnails on TV shows and films to see what appeals to different viewers.

It’s impossible to say whether Netflix’s boasts are justified, but the company claims its recommendation algorithm alone is worth $1 billion a year. (It surely doesn’t hurt that such talk puts fear into the competition.) Combined with its huge investments into original content, it’s enough to make even the most die-hard Hollywood producer reach for a fortifying algorithm.

Ruelens says the transformation has been noticeable. “When we started out four years ago, we had meetings with big companies in Hollywood. They were all super skeptical. They said ‘We have [decades] of expertise in the industry. How can this machine tell us what to do?’” Now, things have changed, he says. The companies did their own validation studies, they waited to see which predictions the software got right, and, slowly, they learned to trust the algorithms.

“They’re starting to accept our technology,” says Ruelens. “It just took time for them to see.”

29 May 05:19

Forget the Rules, Listen to the Data

by community-noreply@hitachivantara.com

 

Rule-based fraud detection software is being replaced or augmented by machine-learning algorithms that do a better job of recognizing fraud patterns that can be correlated across several data sources. DataOps is required to engineer and prepare the data so that the machine learning algorithms can be efficient and effective.

 

Fraud detection software developed in the past have traditionally been based on rules -based models. A 2016 CyberSource report claimed that over 90% of online fraud detection platforms use transaction rules to detect suspicious transactions which are then directed to a human for review. We’ve all received that phone call from our credit card company asking if we made a purchase in some foreign city.

 

This traditional approach of using rules or logic statement to query transactions is still used by many banks and payment gateways today and the bad guys are having a field day. In the past 10 years the incidents of fraud have escalated thanks to new technologies, like mobile, that have been adopted by banks to better serve their customers. These new technologies open up new risks such as phishing, identity theft, card skimming, viruses and Trojans, spyware and adware, social engineering, website cloning and cyber stalking and vishing (If you have a mobile phone, you’ve likely had to contend with the increasing number and sophistication of vishing scams). Criminal gangs use malware and phishing emails as a means to compromise customers’ security and personal details to commit fraud. Fraudsters can easily game a rules-based system. Rule based systems are also prone to false positives which can drive away good customers. Rules based systems become unwieldy as more exceptions and changes are added and are overwhelmed by today’s sheer volume and variety of new data sources.

 

For this reason, many financial institutions are converting their fraud detection systems to machine learning and advanced analytics and letting the data detect fraudulent activity.Today’s analytic tools with modern compute and storage systems can analyze huge volumes of data in real time, integrate and visualize an intricate network of unstructured data and structured data, and generate meaningful insights, and provide real-time fraud detection.

 

However, in the rush to do this, many of these systems have been poorly architected to address the total analytics pipeline. This is where DataOps comes into play. A Big Data Analytics pipeline– from ingestion of data to embedding analytics consists of three steps

 

  1. Data Engineering: The first step is flexible data on-boarding that accelerates time to value. This requires a product that can ETL (Extract Transform Load) the data from the acquisition application which may be a transactional data base or sensor data and load it using a data format that can be processed by an analytics platform. Regulated data also needs to show lineage, a history of where the data came from and what has been done with it. This will require another product for data governance.
  2. Data Preparation: Data integrationthat is intuitive and powerful. Data typically goes through transforms to put it into an appropriate format, this can be called data engineering and preparation. This is colloquially called data wrangling. The data wrangling part requires another set of products.
  3. Analytics: Integrated analytics to drive business insights. This will require analytic products that may be specific to the data scientist or analyst depending on their preference for analytic models and programming languages.

 

A data pipeline that is architected around so many piece parts will be costly, hard to manage and very brittle as data moves from product to product. 

 

Hitachi Vantara’s Pentaho Business Analytics can address DataOps for the entire Big Data Analytics pipeline with one flexible orchestration platform that can integrate different products and enable teams of data scientists, engineers, and analysts to train, tune, test and deploy predictive models.

 

Pentaho is open source-based and has a library of PDI (Pentaho Data Integration) connectors that can ingest structured and unstructured data including MQTT (Message Queue Telemetry Transport) data flows from sensors. A variety of data sources, processing engines, and targets like Spark, Cloudera, Hortonworks, MAPR, Cassandra, GreenPlum, Microsoft and Google Cloud are supported.  It also has a data science pack that allows you to operationalize models trained in Python, Scala, R, Spark, and Weka.  It also supports deep learning through a TensorFlow step.  And since it is open, it can interface with products like Tableau, etc. if they are preferred by the user. Pentaho provides an Intuitive drag-and-drop interface to simplify the creation of analytic data pipelines. For a complete list of the PDI connectors, data sources and targets, languages, and analytics, see the Pentaho Data Sheet.

 

Pentaho enables the DataOps team to streamline the data engineering, data preparation and analytics process and enable more citizen data scientists that Gartner defines in “Citizen Data Science Augments Data Discovery and Simplifies Data Science” . This is a person who creates or generates models that use advanced diagnostic analytics or predictive and prescriptive capabilities, but whose primary job function is outside the field of statistics and analytics. Pentaho’s approach to DataOps has made it easier for non-specialists to create robust analytics data pipelines. It enables analytic and BI tools to extend their reach to incorporate easier accessibility to both data and analytics. Citizen data scientists are “power users” who can perform both simple and moderately sophisticated analytical tasks that would previously have required more expertise. They do not replace the data science experts, as they do not have the specific, advanced data science expertise to do so, but they certainly bring their individual expertise around the business problems and innovations that are relevant.

 

In fraud detection the data and scenarios are changing faster than a rules based system can keep track of, leading to a rise in false positive and false negative rates which is making these systems no longer useful. The increasing volume of data can mire down a rules based system, while machine learning gets smarter as it processes more data.  Machine Learning can solve this problem since it is probabilistic and uses statistical models rather than deterministic rules. The machine learning models need to be trained using historic data. The creation of rules is replaced by the engineering of features which are input variables related to trends in historic data. In a world where data sources, compute platforms, and use cases are changing rapidly, unexpected changes in data structure and semantics (known as data drift) require a DataOps platform like Pentaho Machine Learning Orchestration to ensure the efficiency and effectiveness of Machine learning.

 

You can visit our website for a hands on demo for building a data pipeline with Pentaho and see how easy Pentaho makes it to “listen to the Data.

26 May 17:25

Little Lamp To Learn Longer Leaps

by Roger Cheng

Reinforcement learning is a subset of machine learning where the machine is scored on their performance (“evaluation function”). Over the course of a training session, behavior that improved final score is positively reinforced gradually building towards an optimal solution. [Dheera Venkatraman] thought it would be fun to use reinforcement learning for making a little robot lamp move. But before that can happen, he had to build the hardware and prove its basic functionality with a manual test script.

Inspired by the hopping logo of Pixar Animation Studios, this particular form of locomotion has a few counterparts in the natural world. But hoppers of the natural world don’t take the shape of a Luxo lamp, making this project an interesting challenge. [Dheera] published all of his OpenSCAD files for this 3D-printed lamp so others could join in the fun. Inside the lamp head is a LED ring to illuminate where we expect a light bulb, while also leaving room in the center for a camera. Mechanical articulation servos are driven by a PCA9685 I2C PWM driver board, and he has written and released code to interface such boards with Robot Operating System (ROS) orchestrating our lamp’s features. This completes the underlying hardware components and associated software foundations for this robot lamp.

Once all the parts have been printed, electronics wired, and everything assembled, [Dheera] hacked together a simple “Hello World” script to verify his mechanical design is good enough to get started. The video embedded after the break was taken at OSH Park’s Bring-A-Hack afterparty to Maker Faire Bay Area 2019. This motion sequence was frantically hand-coded in 15 minutes, but these tentative baby hops will serve as a great baseline. Future hopping performance of control algorithms trained by reinforcement learning will show how far this lamp has grown from this humble “Hello World” hop.

[Dheera] had previously created the shadow clock and is no stranger to ROS, having created the ROS topic text visualization tool for debugging. We will be watching to see how robot Luxo will evolve, hopefully it doesn’t find a way to cheat! Want to play with reinforcement learning, but prefer wheeled robots? Here are a few options.

16 Apr 20:34

TeslAtari 🕹

by Tesla

TeslAtari updates inbound! Look for Super Breakout and more in the latest software update which will begin rolling out later this week https://ts.la/designyours
16 Apr 18:41

Creating a Marketing Persona: Influencers Matter

by Michael McNichols

Every marketer should take a moment and consider who their audience is. Who are they trying to reach? How? And why? You might have a huge and diverse consumer base to look at, but you can break them down into segments. Then based upon the characteristics of each segment, you can create a sketch, or a persona, that stands in for that segment. They act as their representative and avatar when you draw up your content marketing plans and strategies.

By creating a marketing persona, you can view that whole segment as a person and put yourself in their shoes. You can ask what keeps them up at night and what pain points do they experience as a part of their job. By breaking a whole segment down into a persona, you can see them more as a person, whom who deserves your empathy and help, rather a huge, disparate group. You have the product and services they need, and you can focus on properly reaching them with that message and connecting with them.

Essentially, a marketing persona allows you to create content that is both relevant and useful to that segment.

What, however, goes into crafting a marketing persona?

 Data, Data, Everywhere

As with almost everything that is marketing related, it comes down to data. The more you know about your audience the better you can craft marketing personas that represent them and can help you create content that can solve their business problems.

Parse through the data you have collected on your prospects and customers.  What are their:

  • Preferences and interests
  • Job titles and responsibilities
  • Age, gender, salary
  • Education, family, and other background information

That is all more general information. If you have any data on your customers’ hobbies, the blogs they read, the news sources they prefer, the types of content that better engage them, how good they are with certain types of software, and the social media channels they use, it can all create a stronger persona.

Once you have put all that information together into a sketch of a person, ask yourself:

  • What challenges do they face at their jobs and what must their goals be?
  • What values do they hold?
  • What fears do they possess? What annoys and irks them?
  • What do they not like about the sales process and what might they be worried about if they are seeking a new solution? What would stand out to them and impress them?
  • Most importantly of all, how can your services help with all of that?

The answers to those questions can all mix and stir together into what your marketing message to that persona is. Quite simply, your message should tell them what you can do for them, how it solves their problems, and how they benefit.

The next step then involves you making your pitch to them. What is the best way you can convey your message to a persona? How can you reach, connect, and engage them? Each persona is going to be slightly different, but the data will indicate the best way to go about marketing to each.

How Many Personas? And When to Market to Each?

You might wonder just how many personas you should create. Most generally consider three to five personas to work well, as they would cover your whole audience but also allow you to dive deeper into specificities about each segment via their persona.

Here are two persona examples: Mary the Marketer and Chad the CMO.

They both work together. Chad is Mary’s boss, and he is the one who signs off on any invoice and makes the final decision on what the company involves itself with and what software, solutions, and services it will invest in.

Mary, though, works with marketing software every day. She’s down in the trenches, crafting content strategies and organizing campaigns. She knows what software works best for her and her team. She knows the problems they encounter every day, and a solution that helps with that speaks to her.

Chad trusts Mary and values her opinion. If she tells him about a solution that would do a world of good for her and her team, Chad is going to listen. Mary then is an influencer. She influences decisions, because of her knowledge and experience.

Of course, Chad might be interested in solutions too, but might not have the time with his responsibilities to look at solutions. On occasion he may. In fact, he might actually bring a solution to Mary to garner her opinion on it.

Still, certain marketing campaigns might be better served focusing on Mary more than Chad, she being an influencer  they may have a better chance of reaching and engaging. A marketing campaign shouldn’t shut the door on any marketing persona, but they need to know where to properly and more wisely invest their time. In this instance, it would be mostly with Mary the Marketer and influencer but not always.

If there is a way to reach Chad as well, however, go for it. Keep all your options open, but bear in mind that he will have a different perspective and different concerns than Mary, so content should be better geared toward that end.

Learn how Eloqua can step up your content creation efforts with “A Brave New World – Create Beautiful, Responsive Content with Eloqua.

Read the blog

07 Apr 21:32

Facebook’s algorithm makes some ads discriminatory—all on its own

by Jeremy B. Merrill

Facebook built its lucrative advertising enterprise by showing businesses’ ads to just the right set of potential customers. But a new academic finding threatens the heart of that, showing that Facebook’s algorithms can steer some job ads in ways that are discriminatory—even when the advertisers weren’t trying to reinforce stereotypes about gender in the workforce.

The group of researchers, from Northeastern University, the University of Southern California and digital civil rights advocacy group Upturn, ran ads advertising jobs openings in the lumber industry and for preschool teachers to gender-balanced audiences. Facebook nevertheless mostly led men to the lumber ads and mostly showed women the preschool teacher ads.

Facebook offers granular options for advertisers to choose who should see their ads, ensuring a brand like Huggies could send its ads to new parents and political candidates can ask for money from their supporters. But even after advertisers choose everyone who could potentially see their ad, Facebook opaquely chooses who actually does, based partially on how likely Facebook’s artificial intelligence algorithms predict that each user is going to click on it.

Facebook said in a statement, “We’ve been looking at our ad delivery system and have engaged industry leaders, academics, and civil rights experts on this very topic—and we’re exploring more changes.” It said its recent changes meant to combat potentially-discriminatory choices by advertisers were “only a first step.”

In a separate finding of the study, ads for houses for sale—again targeted to have identical potential audiences— were directed mainly to white people, while ads for rentals were shown primarily to black people.

Automated but unintentional discrimination was a perhaps inevitable consequence of the tech industry’s favorite formula for maximizing “engagement.” Algorithms like this are designed to show you similar content to whatever content that “people like you” have read or clicked on or bought or listened to, and to do that for all content. That can be helpful when it leads to automatically advertising razor blades to new razor purchasers, or recommending Ariana Grande songs to Justin Bieber fans.

But using these algorithms on things that are highly regulated (like job ads) and “people like you” replicates sensitive offline groupings, like race and gender, could send the modern web’s fundamental formula toward a reckoning. When an algorithm “learns” a pattern that more men than women are interested in lumber industry jobs (even if it doesn’t know their gender and learns that by correlating other information about a person’s likes and habits), then what the system is doing is deciding not to show those job ads to other women, solely because they’re women. That’s problematic, recreating stereotypes, “boys’ clubs” and societal barriers that have existed long before software.

Facebook has already been settled a lawsuit for a separate issue: offering discriminatory options for targeting ads to advertisers who chose to show ads for, among other things, sausage-making jobs just to men. It agreed to remove those options in March. And just last week, Facebook was sued by the US Department of Housing and Urban Development, both for offering discriminatory targeting options and for the automated discrimination that this paper shows can exist.

In legal documents related to those lawsuits, Facebook described its advertising system as a “neutral tool”—a claim that’s challenged by this research paper. If Facebook is contributing to discriminatory advertising on its platforms, that could endanger its legal immunity under a US law foundational to the internet—section 230 of the Communications Decency Act—that protects internet businesses from being sued over the illegal activity of their users.

The researchers carefully constructed their study to try to discover whether Facebook’s algorithmic decisions were the cause of the gender-biased audiences for ads. To test this, they ran ads with images of either stereotypically male or female things, but with the images made almost entirely transparent; they’d appear all white to humans, but computers could still “see” the underlying image. Facebook still steered the ads with these modified images to specific audiences: ones containing, for instance, football went to men, and makeup supplies to women. That effect could not have occurred based on human reactions, since the photos appeared the same to humans.

The researchers caution that they haven’t been able to prove that Facebook’s algorithm would affect how any job ad was disseminated, since they can only see the results for their own ads, with potential audiences created under their carefully controlled conditions. While ads for some jobs they created were delivered to a roughly even mix, like artificial intelligence developers and lawyers, those for the preschool teachers, janitors and others had a skew worse than three people of one gender to one of the other. Ads for jobs in the lumber industry were seen by more than nine men for each woman.

To measure the difference between who could’ve seen their job ads and who actually did, the researchers constructed their ads’ potential audience to be evenly split between specific men and women in North Carolina (using public records that includes gender), then used Facebook’s existing tools to see the gender breakdown of who actually saw the ads.

Facebook calculates the gender breakdown for the audience of every ad in its system, but only shares that data with the advertiser. So, how often does Facebook’s ads system transform ordinary job ads into discriminatory ones? The public has no way to find out.

16 Mar 00:49

Blockchain Transformation Playbook

by Iftikhar Alam

Blockchain is transforming the financial industry, unlike anything we have seen before. The concept has been in the making for decades but it was not until the boom of cryptocurrency, Bitcoin.

It looks like, the world is still adjusting to the surprise growth of blockchain. More regulations are in order and the vision for the future seems to have an important role for blockchain.

According to Forbes, large businesses and enterprises are not just exploring blockchain, but now implementing it. In simple words, businesses are looking for the blockchain transformation from traditional technology to the blockchain.  

Future of Blockchain

Experts are predicting bright future for the industry. The global market value of the blockchain industry was mere $411 million, that is expected to reach around $8 billion in the next three years. In 2024, the blockchain market is expected to reach $20 billion.

According to a report, 10% of the global GDP will be stored on blockchains by 2027. This is huge progress keeping in mind, we are talking about less than a decade. There are some estimates that even predict $3 trillion blockchain market by 2030.

Similar to the dot-com era, the initial phase of blockchain was not spectacularly overwhelming. However, it is starting to find its footing in different industries outside the one it has pioneered.

Simply put, blockchain is no more limited to the financial sector, but have already transformed many other industries.

This Blockchain Transformation Playbook is for companies looking to incorporate blockchain in their structure and strategy. It is essentially tailored for companies with a market cap of at least $500 million. However, the basics can be followed by almost all companies irrespective of their size with little adjustments.

Any company can use the recommendation outlined in this playbook to work on blockchain projects that are ultimately poised to transform the entire business models.

Blockchain Implementation Challenges?

There is no doubt that Blockchain has the potential to transform many industries that we see today, but there are also challenges remain for wider adoption of this technology. For example:

  • There is a need to establish platform standards especially to implement blockchain at the Enterprise level.
  • Customers are looking into blockchain solutions that are industry-specific, not general purpose.
  • To unlock the true value of blockchain, better interconnectivity between multiple independent blockchain platforms is required.

Playback Quick Overview

blockchain transformation playbook

Here are five basic steps you need to take to transform the company using blockchain technology:

  1. Start with small projects
  2. Build a blockchain taskforce
  3. Train the teams
  4. Develop a strategy
  5. Create internal and external networks

Factors Driving Blockchain Revolution

Before we move onto each step of our Blockchain Implementation Playbook, it is important to discuss the factors driving the blockchain growth.

Here are the factors:

  • Transparency

This is a common issue with the supply chain industry. Lack of transparency creates serious issues in the global supply chain leads that include safety and health concerns, as well as counterfeiting.

For example, counterfeiting alone costs over USD 7.5 billion each year to US-based semiconductors alone. Blockchain certainly offers a layer of transparency to blockchain industry to reduce their losses due to lack of transparency.

  • Complexity

You can find several intermediaries like brokers, financial institutions, and several third-party institutions that make global trade complex as well as slow. According to estimates, suppliers and businesses, especially in emerging markets, pay up to 30% of interest on receivable financing. This adds a lot to the cost of doing business.  

  • Security

One of the key issues with a centralized architecture of information in the tech world is a security breach. Such centralized architectures are vulnerable to hacking attempts that makes them insecure. According to estimates, there will be 20 billion IoT devices in the world in the next two years, all vulnerable to hacks. Blockchain offers better security to the IT industry.

Blockchain offers a perfect solution to all of the above problem, thus these factors driving its growth. It builds trust at the enterprise level with the digitization of business processes, the codification of complex contracts, and tokenization of assets. By implementing these processes, businesses and enterprises can make business processes secure, simple, and more efficient.      

1. Start With Small Projects

Like with any new venture, it is best to start small. For your initial projects, if you focus on smaller pilot projects rather than a full-fledged transformation then the risks are fewer. Such projects will have a meaningful impact and draw an achievable roadmap for the company. The success would convince stakeholders to show more initiative and involvement.

Nevertheless, the projects should be significant enough to create an impact. Ideally, you would want to use the true entrepreneurial spirit by using the technology to address the existing problems in the company’s strategy or operations.

The blockchain is a leading digital transformation technology that allows you to address the problems in an effective way, to bring in more transparency, and to develop a more effective process.  

Blockchain Transformation Guidelines

Here are few guidelines regarding the small blockchain projects you want to start with:

  • The objectives of the project should be clear and measurable.
  • The project’s feasibility study should be conducted beforehand to ensure that the success possibilities are higher for the project.
  • The existing developing and network security teams should be able to collaborate with external teams versed with the blockchain technology development.
  • The project should show quantifiable progress within the first six months.

Starting out with a few pilot projects would help orchestrate a bigger movement. That is in essence what the transformation would be. When the success of the first or second project starts a momentum, it will essentially move the entire company in one direction.

The very best example of this would be Artificial Intelligence (AI). Today, it is paving the way in almost every industry from manufacturing to logistics to advertising. How did it all start? On a very small level.

Companies initiated small AI projects that helped them solve problems in their existing technology. Big tech companies that are investing millions today in AI once funded smaller projects within their organizations. This helped create a movement and created a path for the technology to dive into other industries.

The success of one project initiated another even bigger project. Engineers became more experienced. Investors became more interested. Finally, the technology boomed and transformed many existing industries. It is expected that by 2030, AI would contribute $13 trillion to GDP growth.

Same if the case with companies using blockchain technology, some big names that started small and now using blockchain on a much larger scale. These companies include Industrial Industrial and Commercial Bank of China (ICBC), JPMorgan Chase & Co. (JPM), Berkshire Heathaway Inc., Bank of America, Wells Fargo & Company, and many similar names.

Blockchain Use Cases

Here are some more companies using blockchain technology as an example or blockchain use cases.

  • China Construction Bank: The CCB started with some of their financial products and used IBM blockchain platform to make the process simpler and straightforward, mostly for services they sell in combination with insurance companies.
    • Agricultural Bank of China: Starting small, the bank has started a project to offer agricultural loans to mostly e-commerce merchants on a decentralized network.
    • Apple Inc.: The global tech giant is starting with using blockchain to timestamp data.
    • Toyota Motor Corp: The Japanese company is currently exploring ways to enable blockchain payments for self-driven cars.
    • Samsung: The South Korean tech giant is using a blockchain platform to track global supply chains.

These are some great examples who some of the largest companies in the world are starting slow with blockchain, and working on smaller projects for now.

Overall, some of the major industries that are going through a blockchain disruption include financial industry, healthcare, real estate, legal industry, security, government, and education.

2. Build a Blockchain Task Force

When you invest in a technology like a blockchain, you need the right people to make things happen and that too at a faster rate. Obviously, you will need expertise in the field.

However, blockchain is a rather nascent technology. This will make it difficult to hire people from the outside. Keeping in mind the fact that you want the long-term benefits of the technology, you need a task force with people from within the organization.

The demand for blockchain skills has increased by 300% in just one year, with increased demand from businesses looking for blockchain transformation. This also raises the average salary ($84,884) which is now above the median US salary ($52,461).

Developing an In-house Team or Outsourcing: A Lesson to Learn

You need to look back at other technologies and how companies used their resources to benefit from them. There are important lessons to be learned. The digital transformation of many industries starting in the 90s meant hiring an expert, i.e., a CTO.

In that era, a company hiring a CTO was a big deal. In contrast, those who used independent projects using outside help did not fully leverage the benefit of the internet.

For blockchain applications for your business, you need strong leadership, expertise, and initiative. Your task force will need to be much more centralized which is a bit ironic considering blockchain is all about decentralization.

You will need a CTO or CDO to head the team working on these projects.

The key goals of this new task force would be as following:

  • Create blockchain use cases
  • Build blockchain capabilities from the ground up
  • Build processes for successful delivery of different phases of the project
  • Once tested, these processes will need to be repeated to ensure timely implementation
  • Look for expertise and talent wherever there is a need in the team
  • Develop standards to achieve with the different processes
  • Utilize different divisions of the company to enrich the process and bring more opinions to the table

For big enterprises, cross-functioning would be a key to success. Many companies operate under different divisions that ultimately report back to the CEO. The new blockchain division would similarly report to the CEO under the leadership of the CTO or CDO.

  • Getting Ready for New Job Creations

There will be new job creations with specific descriptions and duties of your new blockchain team. Organizing these roles and what they contribute to the project is going to be the single most important aspect of the team.

The reason being this is going to be new for the task force as well. If their goals and duties are clearly defined, they will have a clear direction as to what their contribution and goals are.

You will need all hand on board including Project Managers, Product Managers, Data Scientists, Security Analysts, and Implementation Executives. All these taskforce members will report to the leader who will then report to the CEO. If need be the taskforce can be further divided into small teams.

  • Is There Any Blockchain Framework to Follow?

Unfortunately, there isn’t. There are no existing frameworks for blockchain development. The companies using blockchain will be challenged to devise frameworks and processes that work out best for their projects. The leader should be able to devise the right processes that utilize the skills and qualities of the task force.

Not every company would have ample people available to independently work on the blockchain project. Also, there may be a dearth of people with even the basic knowledge of this technology. That raises the need for looking outside the firm. Even if you do have people from within the organization for the project formulation and development, it would be opportunistic to still hire talent.

Once the blockchain technology becomes even more widespread as experts are predicting, the demand for blockchain experience and talent would skyrocket. This would be the prime time to hire people who have experience in this field. They can truly serve as a tool to get ahead of competition in the near future besides taking the current projects to fruition.

You may want to work with a recruiting partner to do this job for you. Even when you acquire such talent, providing them the right training to assimilate with the current team members would be required. It will help create a balanced task force that covers all aspects of the project and works in cohesion.

A Quick Checklist for Hiring Blockchain Developers: Skills to Look for

  • In-depth understanding of blockchain network and its architecture.
  • Ability to develop smart contracts independently.
  • In-depth understanding of data structure and their link to front-end applications.
  • Well-versed in cryptography and latest techniques
  • Experience in application development
  • An excellent understanding and knowledge of blockchain platforms
  • Ability to incorporate blockchain proficiency as a service

3. Train the Teams

Formulating the task force is just the beginning. The real challenge would be training these individuals existing as well as those new hires. The financial industry is seriously lacking people experienced with blockchain and cryptocurrency, thus hindering the blockchain transformation in many industries.  

The very nature of this technology can be attributed as a reason. Most people who started ICOs are independents who have worked with this technology for years without the support or involvement of big companies.

The good news is that these very people are pushing the blockchain narrative forward on the internet. There is a lot of material online in the form of publications, studies, white papers, and even interviews that can be used to train your team. Most importantly, there is a lot of talk about future blockchain applications.

  • Online Training Resources

You have online courses, YouTube videos, and podcasts that explain what is blockchain, how blockchain works, and how blockchain is different from the current technology employed by banks and other financial institutions.

This is obviously cost-effective as most of the content is free. It would be the job of the leaders and experts in the team to comb through the materials and find what they can use to train their team. Going through blockchain use cases is another great way to get your team ready.

Today, training practices within the companies have significantly improved. Again, thanks to technology and the internet. There are no boring lectures or slideshows but interactive videos and hands-on practices.

Such training can help the team understand the very basics as well as how to think about the future. They will be poised to become future innovators of blockchain in the service industry, banking, trading, etc.

That said, you will also need some in-person training. You can hire blockchain enthusiasts and consultants to deliver training sessions to your team. And this is not going to be just for the team members, leaders will need to take part as well.

Here is what you need to cover with the training material:

  • Basic understanding of what is blockchain, its current applications, and benefits
  • The impact of blockchain on current financial systems and economies
  • Case studies of the most prominent cryptocurrencies (Bitcoin, Ethereum, and LiteCoin)
  • Technical understanding of the technology how it works and what are the challenges
  • Understanding of tools available currently

Here is the recommended number of hours for different members of the team:

  • Executives: 4-6 hours
  • Individual team leaders: 12-16 hours
  • Engineers: 100-120 hours

4. Develop Strategy

No project can become successful without a solid strategy. Creating the best strategy for a blockchain project is going to be both simple and challenging. Since there are not many enterprises working on the technology, you would be at liberty to create your own distinct goals. However, creating an achievable path would be difficult. The biggest of the challenge would be to identify where blockchain uses can be employed.

If you are wondering why strategy is not the first step as it traditionally is, it is because there needs to be some familiarity with the technology first which the training would provide. When the teams have attained basic experience with blockchain, formulating a strategy would become easier.

Before developing a detailed strategy, you also need to first identify the areas where you can implement blockchain transformation. To put it simply, you first have to do a detailed analysis of blockchain innovation and its impact on core business practices.

For example, blockchain in the service industry can be used to streamline the transaction process by digitizing assets and offering access to users who don’t have access to bank accounts.

Here is what you can plan for, and the goals you can set for the business:

  • Providing end customers better access to your services through blockchain implementation.
  • Can enhance your bookkeeping process.
  • Better and flexible reserve management.
  • Improvements in common business processes.

Once you have identified the right process to implement blockchain transformation, here is how you can develop a sound strategy.

Find Opportunities:

As mentioned earlier, start by identifying opportunities. For example, you can start with a list of small projects you want to start with, and then narrow them down to two or three as pilot projects.

You can create such list be identifying pain points in your current business process. For example, the processes you aren’t really satisfied with. These could be processes that are causing delays, back-office workarounds, and any areas that are causing client’s dissatisfaction. A customer feedback survey can also help you identify the pain-points in the business.

Next, you can identify processes that you want to streamline, like reconciliation of data or transaction processing. The blockchain is an excellent way to eliminate redundancy in data repositories, as well as identity issues. It can also reduce the risk of cyber-attacks and data hacks.

  • Exploring Readiness and Feasibility

As you choose your pilot projects, you also need to develop a hypothesis about how blockchain can make a difference. For example, you may want to create a hypothesis like:

“The implementation of blockchain will decrease the time needed for adjustments or settlements, or reduce the need of it in the first place, and improves transparency.”

Once you have developed a hypothesis, solidify it by consulting experts, learning about blockchain use cases in similar situations, or collecting more conclusive data that can back up your hypothesis.

  • Develop Prototypes and Test Them

Before Implementing the blockchain on the larger scale, it is imperative you start with prototypes first. This is a great way to improve the blockchain technology you so far worked on and make it more align with current practices before using it to replace the older technology.

The testing and evaluation process is important, both for your team or employees to get accustomed to the new technology and the process to get aligned with the latest technology. It will also help you and your team expand their awareness about blockchain technology and its results.

Match the results with your original hypothesis. See if it proves your hypothesis. If not, what are the adjustments that you need to make? Proceed forward with the results of the prototype in mind.

  • Scale Accordingly

Once you are satisfied with the results of the prototype, next is to scale your efforts. Develop a plan that is designed for a long-term blockchain transformation and implementation. Create a detailed roadmap of how to scale blockchain technology and your prototype but in an achievable and measurable way.

5. Developing External and Internal Communications

Last but not least, you need to take care of the internal as well as external communications as blockchain will have an impact on your business processes as well as output. You need to align your internal and external communications accordingly.

Blockchain transformation as you scale will affect your stakeholders, which means you need better communication to implement alignment. Here is how to communicate with each stakeholder.

  • Investors

With the betterment blockchain transformation brings to your business, it is important to re-evaluate your business in front of investors. A good strategy and communication help you make investors realize the true value of your company after the implementation of blockchain technology.

  • Relations with Government

If you are working in highly-regulated industries like healthcare and finance, it is important to stay compliant with all government regulations. Keep in mind, governments are also adjusting with the increased penetration of blockchain technology into mainstream industries and widespread use of cryptocurrencies.

You must be well-versed about all regulations related to blockchain technology but also communicate well with the corresponding government institutions. For example, you need to look into the Security and Exchange Commission (SEC) in the United States that is making some major changes in their regulations related to blockchain and cryptocurrencies.

  • Educate Your Customers

The changes blockchain transformation brings in to your processes is also new for your customers. You need to educate your customers as well, about the benefits blockchain offers them.

  • Finding and Recruiting Educate Human Resource

As we have mentioned earlier, finding the right human resource and skills is not easy due to the scarcity of talent. To attract better skillset, you need to have better employer branding. You must show skilled human resource you are a company that they want to work for.

For example, blockchain developers want to work with companies that allow them creative space and independence, as there is not framework associated with blockchain yet. An environment that encourages learning as blockchain technology is still involving. You need to communicate skilled people that your company offers exactly the same.

  • Internal Communication

Last but not the least, it is very important to communicate all changes and implementations to your employees, who may be confused, and some cases, scared of the new technology that is not well-known yet. You need to ensure your employees that the changes are for the betterment of the business, and also need to educate them about the future they may have to walk in along with blockchain transformation.

Final Words

This is an excellent resource to help you bring in changes and complete the blockchain transformation. The blockchain is now an integral part of the latest digital transformation businesses are implementing.

However, you need to start small if you are new to the blockchain. Find small pilot projects mostly related to the pain-points in your business and processes you want to streamline. Hire a talented workforce or train your current human resource. Bring in structure and accommodate new blockchain team in your business structure.

Develop a strategy to scale further once initial pilot projects result in success. And in the end, make sure that all stakeholders are communicated in time about the changes your business is bringing in terms of blockchain transformation.

The post Blockchain Transformation Playbook appeared first on 101 Blockchains.

20 Jan 22:15

The ValueSelling Framework®

by Gerhard Gschwandtner

While most salespeople are stuck at the transactional or relationship sales model, ValueSelling is the domain of top sales professionals. Julie Thomas, CEO of ValueSelling Associates shares great insights that can help your sales soar. For more information visit www.valueselling.com
20 Jan 22:09

Delivering simplicity through complex data

by QuantiumEditor

29 Oct 19:54

AI in UK Artificial Intelligence Industry Landscape Overview Q3 2018

by Roxy Iqbal

*NEW* AI in UK Artificial Intelligence Industry Landscape Overview Q3 2018 

Click to read the REPORT
Click to read the PRESS RELEASE

The post AI in UK Artificial Intelligence Industry Landscape Overview Q3 2018 appeared first on APPG.

21 Oct 08:47

Qlik Sense September 2018

by info@opso.fr (OPSO - Expertise décisionnelle)
Qlik Sense September 2018

Quelles sont les nouveautés prévues de Septembre 2018 ?

Insight Advisor pour les clients

Utilisation étendue d'Insight Advisor pour TOUS les utilisateurs des applications y compris les utilisateurs d'applications publiées capables de rechercher et de générer des informations sur les éléments principaux.

Design avancé

• Activation de contrôles lorsque vous utilisez un périphérique hybride (prise en charge les événement de saisie tactile et souris).

• Il est désormais possible de définir un signet par défaut, qui est appliqué lorsque l'application Qlik Sense est ouverte en tant qu'état de sélection initiale.

 

Personnalisation supérieure

Personnalisation (en pixels) des feuilles pour permettre de désactiver la réactivité du client sauf en mode appareil mobile.

Améliorations de l'éditeur d’expressions

• Aide : lien direct vers la page d'aide de Qlik Sense à partir des fonctions d'expression

• Catégories : catégorisation de la fonction restructurée

• Rechercher : Plus facile de trouver des noms de champs, des fonctions et des variables

 

Meilleur contrôle dans les visualisations

• Possibilité d'afficher / masquer les colonnes dans un tableau croisé dynamique

• Les éléments de mesures de types Master Item peuvent désormais être personnalisés avec des échelles de couleurs ou des dégradés, une alternative pratique aux expressions de couleur personnalisées.

 

Visualisations et cartographie

• Nouvelle couche de carte

• Densification de la couche d’arrière-plan avec dégradé pour cartographier à des niveaux multiples telles que : statistiques commerciales, valeurs de maison, etc.

 

Améliorations de la carte

• Zoom adaptatif et panoramique lors de la navigation sur des cartes denses

• Couleurs par défaut pour les nouveaux calques; chaque nouveau calque a une couleur indépendante de la palette

• Les fichiers KML avec des données de lignes géographiques peuvent être chargés et affichés.

• Les étiquettes de champ pour la taille et la largeur vont permettre de faciliter la lecture des légendes ainsi que des fenêtres contextuelles.

 

Mobile

Qlik Sense est maintenant pris en charge dans l'environnement AirWatch EMM pour Safari et Chrome.

 

Connecteurs Qlik

• Ajout d’un connecteur MS Azure SQL DB et d’un connecteur JIRA.

• Optimisation de la sécurité, l’authentification LDAP est intégrée au connecteur ODBC Qlik avec les normes de cryptage et d’authentification reconnue par les industries.

 

Qlik Associative Big Data Index

• La version de septembre va intégrer la version du Qlik Associative Big Data Index spécialisé pour les gros volumes. Ce module sera mis à disposition dans le cadre d’un programme dédié, les détails seront précisés lors de la sortie de la version.

 

Qlik Sense Server

• Possibilité de déplacer une ou plusieurs applications d'un flux vers une autre dans la QMC, en

s'appuyant sur les fonctionnalités de la version d’avril 2018.

• Mise à disposition d'une application de Stream en streaming.

 

Remarque

Date d'expiration de Qlik Sense Desktop
Version : Juin 2017
Date d'expiration : 30 Août 2018
Date de signalement d'expiration : 31 Juillet 2018