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Data Visualization, Discovery and Visual Analytics – Use Cases, Tools, CoE, Vendors

DataVisualizationScorecards, Dashboards, Alerts, Management Reporting, Operations and Transactions Reporting are all enterprise example of data visualization outputs.

Some data visualization use cases include:

  • Data Scientist — uses QlikView to visualize and extend statistical models built in “R”, a programming language used for statistical modeling, to understand traffic flows and congestion patterns and advise on options to improve travel times for Local delivery drivers.
  • Pharmaceutical Sales Representative — uses QlikView on an iPad to access current industry sales trends and doctor prescription history while on a sales call with a busy physician.
  • Healthcare Chief Medical Officer — uses Tableau Software to analyze all aspects of hospital performance including population management, emergency room effectiveness and Affordable Care Act compliance.
  • Crime Analyst— uses Microstrategy to maintain a consolidated view of crime levels and optimize staffing allocations to dispatch police into high crime areas.
  • Retail Store Manager — uses QlikView to analyze which products are selling best which impacts store assortments and which products get featured vs which ones get discontinued.
  • Telecom Customer Service Agent — uses Spotfire to monitor call center statistics and how it translates into customer satisfaction and retention.

From these use cases you can see that there are many different ways of asking questions and telling a story. The story in the enterprise typically is around the these Use Cases:

  • Interactive Modeling – Speed-of-Thought Analysis, What-if Analysis and Forecasting, Rapid Scenario Planning
  • Scorecards – Personalized scorecards, Measure against goals, “At a Glance” information on Business Performance, Convey information in intuitive format
  • Dashboards – Support “what-if” scenarios, Drill down capabilities, Visualize Key Performance Indicators (KPIs)
  • Management Reporting – KPI monitoring, Briefing Books, Summarized views
  • Operations and Transaction Reporting –  Alerts, summarized views of day-to-day activities of the batch and raw data reporting

Enterprise Data Visualization Fundamentals

Data visualization is the software to help companies analyze large amounts of data through easy-to-read charts and graphs. It’s the use of abstract, non-representational pictures to show numbers by means of the combined use of images, diagrams, animations, points, lines, a coordinate system, numbers, symbols, shading, words, and color-coding.  Visualization today has ever-expanding applications in business, science, education, engineering (e.g., product visualization).

A big complaint from business users…. BI platforms and big data today increasingly suffer from poor visualization. Most organizations are data rich, information poor and insights starved. Lots of tools, new technology and data but insights are hard to visualize from the background data noise.

Transaction automation and multi-party data aggregation is no longer the focal point. The problem has shifted to effective use. It’s interesting how in almost every meeting I am in, More Effective Data Visualization (and Improving User Experience at even reports/dashboard level), is coming up as key business initiative.  There is a growing demand to enable everyday business users to answer critical questions with ease (self-service visualization).

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Apple’s HealthKit vs. Google Fit – Wellness Platforms for Digital Health?

mobile-applicationsGame on….I think we just witnessed a big next generation big data leap in Healthcare Wellness Data and Analytics.  Apple jumped into the health information business on June 2 2014, launching both a new health app (Health) and a cloud-based health information platform with IOS 8 (HealthKit). This was followed by the Watch launch in September 10, 2014, an intelligent health and fitness companion.

Google followed with Google Fit on June 25. Fit is a set of APIs that will allow developers to sync data across wearables and devices. Google Fit is the equivalent of Apple’s HealthKit.  Google didn’t announce an equivalent of Apple Health app.  It is expecting its ecosystem of Android partners to innovate with apps. Google also might be taking a different approach with Fit aligned with Android Wear SDK which extends the Android platform to a new generation of wearable devices.

Understanding Health, Wellness and HealthKit

What is the end game? First is that many healthcare companies are trying to change patients’ behavior to improve outcomes with data:

  • being more proactive and taking a preventative approach
  • trying to build relationships with patients and engage them more in their own care
  • trying to get them to adopt healthier behaviors and make better choices

The goal is collect real-time biometric data to feed EMR for clinical care management and CRM for outreach and engagement.

Apple’s mobile App, called “Health”, will collect an enormous of realtime data in the form of number of body metrics including blood pressure, heart rate, and stats on diet and exercise.  Data is collected about personalized daily fitness goals from either motion sensors in phones or next generation wearables like the Watch. Health will constantly monitor key health metrics (like blood sugar for diabetics or blood pressure), and if any of them begin to move outside the healthy range, the app can send a notification to the user or a surrogate like user’s doctor.  What Apple does well as usual is providing simple, easy-to-use dashboards consumed via mobile apps for health and fitness (see below).

The mobile Health app will share all its information with a new cloud platform called “HealthKit.” The new health cloud platform is designed to act as a global repository for all the user’s health information. It will accept data collected by a variety of third-party devices and apps. For instance Nike is now working to makes its health and fitness apps integrate with HealthKit.

HealthKit cloud-based platform is where the real heart of the operation is. It uses real-time and historical data, aggregating data from all the devices tracking vital statistics. HealthKit will allow consumers to know more about their body and track every step. HealthKit also allows consumers to share data with healthcare Providers and even Payors.  HealthKit also takes a big burden off developers who no longer have to build custom tools and various API interfaces to transfer, sync and collate health data.  This way the developers can focus on value add apps like visualization, interpretation/analysis rather than plumbing like data security, permission gathering.

So what’s the big deal? Apple’s iPhone 6, 6 Plus, and Watch are essentially migrating from part-time health stat monitor (Bluetooth heart rate monitors or step trackers) to full-time mobile preventative wellness monitoring platforms.


Patient-centered, consumer-driven, and value-based Business Models

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Cloud-based Healthcare Analytics and Decision Support Solutions

CostTransparencyThe old playbook no longer works. Everyone acknowledges that U.S healthcare is broken.

Technology (preventative apps like Apple Health and HealthKit; EHR, claims and reimbursement analytics; Physician Practice management etc.)  will reinvent healthcare as we know it.  I expect the  healthcare transformation to start incrementally and develop slowly in sophistication.  Though the early changes will appear clumsy and underwhelming, by 2030 they will seem obvious, inevitable and well beyond the changes we might envision today.

Why change? Consider this:

  • Honeywell, a Fortune 100 technology and manufacturing company, needed to manage the ever-escalating cost of insuring its 130,000 employees and their dependents. Honeywell has reported that health care costs were growing approximately 8-10% per year.
  • Self-insured employers like Wal-Mart want to make health care cost and quality information available to their 1.2 Million employees.  Useful information that can be used by employees to select physicians based on how their rank, or how much they cost, resulting in savings for both the employee and the employer. Decision support enabler.

Historically, employers like Honeywell, Wal-Mart and their employees have not had access to comprehensive  information about the cost and quality of care as they evaluate benefit designs across multiple health plans and treatment options.

In some cases, U.S health care providers and other market participants have actively resisted efforts by employers and others to obtain information about the costs and quality of health care services. Why? because opaqueness means money.  UCSF researchers uncovered an enormous discrepancy in what different hospitals charge for the same procedure, ranging from a low of $1,529 to a high of $183,000. The median hospital charge was $33,611. The startling cost variation illustrates an inefficient system.

Despite this resistance, the health care industry generates extensive data that is relevant to determining the cost and quality of health care services. These data reside in myriad formats and disparate databases, without a common infrastructure, and have therefore been of limited value to employers and employees in controlling costs and improving outcomes.

In many cases, information relating to health care services has restrictions on its use, such as contractual agreements that some health plans and providers have historically entered into to not disclose price information. These factors make it challenging for employers and employees to use these data for the purposes of measuring cost and quality and making informed decisions. Read more »


Consumerism, Health Exchanges, and Payor Big Data – A Primer

EmployerRequirementsHealthcare Benefits are the 2nd costliest line item for companies in the U.S. So, companies are taking aggressive steps to reduce this spend. Consider this:

  • IBM is moving to a private health exchange…Extend Health private exchange will be handling plan options for 110,000 IBM retirees
  • Walgreens is moving employees to a Corporate Health Exchange. Of the 180,000 Walgreen employees eligible for healthcare insurance, 120,000 opted for coverage for themselves and 40,000 family members. Another 60,000 employees, many of them working part-time, were not eligible for health insurance.
  • Trader Joe’s  — decided to send some employees to the new public exchanges. Trader Joe’s has left coverage for three-quarters of its work force untouched but is giving part-time workers a contribution of $500 to buy policies. Because of the employees’ low incomes, the company says it believes many will be eligible for federal subsidies to help them afford coverage.
  • Time Warner will direct retirees to an exchange to get health coverage

For the past year I have done strategy and implementation work in the employee Healthcare benefits and Private Exchange area.  I wanted to share my insights into the massive structural changes taking place in health insurance. The move to patient-centered, consumer-driven, and value-based models is real.

Employee Health insurance in the U.S. is at the cusp of a major transition from an employer-driven payor model to a model directly involving many more employees and consumers.  Private health insurance exchanges with a defined contribution approach represent a significant step in this journey. Also some clever risk shifting strategies are emerging where employers are moving part-time workers onto public exchanges.

The market size is enormous.  Healthcare spending is forecasted to be ~$3.1 trillion in 2014, with $620 Bln of this paid by U.S. employers.  In 2013, employers contributed 32% more in health care expenses than 2008.

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Guest Personalization and Wearable Computing: Disney MyMagic+

MagicBandA satisfying customer experience is the driver of any business’s revenue growth. Disney Theme Parks is no exception. Disney is executing a guest personalization strategy leveraging wearable computing (and analytics) to track, measure and improve the overall park experience. The ultimate goal is increase sales, return visits, word of mouth recommendations, loyalty and brand engagement across channels, activities, and time.

Wearable computing seems to be the next big thing.  Many believe a new crop of gadgets — mostly worn on the wrist or as eyewear — will become a “fifth screen,” after TVs, PCs, smartphones, and tablets.

Wearables are already being used to monitoring vital signs, wellness and health. Devices like Fitbit, UP, Fuelband, Gear2 track activity, sleep quality, steps taken during the day. Consumers of all sorts — fitness buffs, dieters, and the elderly — have come to rely on them to capture and aggregate data.

What most people don’t understand is how powerful wearables (coupled with  analytics) can be in designing new user experiences.  Businesses thrive when they engage customers by creating a longitudinal view of each customer’s behavior. To understand the wearables use cases and potential we did a deep dive into a real-world application at Disney Theme Parks.

Wearable Computing at Disney: MyMagic+

Disney has been rolling out a new guest experience called MyMagic+ to the 30 million guests per year at the Walt Disney World Resort in Orlando.

Realizing that guests were arriving with smartphones and tablets in hand and expecting access to more information, Disney started the MyMagic+ initiative to provide a next generation experience. The overarching goal of MyMagic+ is to provide a much more personalized friction-free vacation at various theme parks, even down to characters knowing your name.

Disney is following in the steps of Harrah’s (now Caesars Entertainment) Total Rewards program that provided an integrated experience for gamblers across nearly 40 resorts and casinos. Loyal spenders were rewarded with innumerable entertainment options, enticing special offers, free hotel rooms, and different ways to redeem credits.

How does MyMagic+ work?

A key element of MyMagic+ is MagicBand.  MagicBands is a ultra-personalization experience.  These brightly colored bands link with online profiles for each visiting family member, and can be scanned at park kiosks to access advance ride bookings, receive customer service, and pay for all the stuff your kids want to buy.

The key to a great experience is being predictive in terms of context. For instance, while wearing her MagicBand, a young lady who loves Disney princesses might be approached by her favorite of the park’s life-size characters and be greeted by name.

Disney extracts and integrates all the information about the guest from all the park siloed data systems. as well as from external sources. This allows them to create a longitudinal view of each guest’s behavior over channels, activities and time.

Sophisticated pattern-detection science is applied against the 360-degree view to extract each guest’s behavioral predictors – like early warning on guest/family fading, real-time park experience dynamics (via feedback), and each guest sensitivity to specific promotions. The objective is to turn these signals into individuated recommendations served via customer marketing systems.

Technology behind MagicBand

According to Disney, each waterproof MagicBand contains an HF Radio Frequency device and a transmitter which sends and receives RF signals through a small antenna inside the MagicBand and enables it to be detected at short-range touch points throughout Walt Disney World Resort. MagicBands can also be read by long-range readers and  used to deliver personalized experiences, as well as provide information that helps us improve the overall experience.

The next version of MagicBand might have much more computing built into it. If they go the Android route…Google has announced an SDK aimed at making Android, more palatable for small devices. Android apparently was consuming more battery. Samsung tried using Android for the Galaxy Gear, its smart watch, and the results were not so great. It couldn’t last very long without a recharge. For the Gear 2 Samsung dropped Android in favor of Tizen, its own operating system.  I won’t be surprised if Apple and Disney team up in a few years around this.

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Big Data Performance Anxiety and Data Grids

In Memory Data Grid (IMGD) is a data structure that is being increasingly The Gridcited as a solution to the problem of scaling big data applications. Unlike in-memory applications, IMGDs distribute only the data across RAM over multiple servers.  With memory prices continuing to fall and the volume of data for an application continuing to rise, solutions based on memory are looking more attractive to manage the performance bottlenecks of applications using Big Data. Should IMGD be on your radar screen for a Big Data application?

In order to understand this and other questions on IMGDs, Carpe Datum Rx spoke to Miko Matsumura, VP of Marketing and Developer Relations at Hazelcast, who has seen recent adoption of this technology in banks, telcos and technology companies. Here is an extract from our discussion.

Why is it so important to distribute data in a data grid? Why should it be In-memory?

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Security Analytics – Big Data Use Case

Another day, another data breach.  Just received another “We’re sorry you got hacked”…letter.  

This is the fifth letter I have received in the past 3 months:, Target, Neiman Marcus, credit card company and a previous employer.  What is going on?

Why aren’t firms investing in beefing up their predictive ability to spot the cyber-security intrusion threats? What’s taking them so long to identify?  Why is the attack signature – sophisticated, self-concealing  malware – so difficult to spot?   Do firms need to invest in NSA PRISM type threat monitoring capabilities?

The three impediments to discovering and following up on attacks are:

  • Volume, velocity and variety – Not collecting appropriate security data
  • Immaturity and not identifying relevent event context (event correlation)
  • lack of system awareness and vulnerability awareness

Obviously… where there is pain…there is opportunity for entrepreneurs see below – data from IBM).  There is a growing focus on big data use case for security analytics after all the breaches we are seeing.  General Electric announced it had completed a deal to buy Wurldtech, a Vancouver-based cyber-security firm that protects big industrial sites like refineries and power plants from cyber attacks.



Here are three recent examples that I was personally affected by – Forbes,  Target, Neiman Marcus.  

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2014 Year in PreReview for Big Data Analytics

In the movie “Minority Report,” set in 2054,Time Travel Tom Cruise plays the captain of the “PreCrime” police force, which uses “precognitive” abilities of mutants to stop crime before it happens. Silicon Valley futurists have sometimes used this reference in the context of the art of the possible with Big Data. We have another 40 years to go to see how analytics can accurately forecast future events based on human behavior. Meanwhile, imagining the future with some level of accuracy is within our reach today.

Value creation in the data economy made headlines in 2014. While Big Data continued to be the buzzword of the year in 2014, solutions that created economic impact were center stage.  Trending terms such as “predictive analytics” and “advanced analytics” approached the levels of “Big Data” on Google Trends during the year. “ROI,” which was vaguely referenced in the last two years, became the most commonly used term with Big Data in 2014. Here is a cross-section of 2014 events.

Apple announces TopsyTV

This is their next-generation TV appliance that integrates social media engagement with the TV watching experience. Earlier in 2013, Apple acquired Topsy Labs, a reseller for Twitter content for $200M. This was followed by a series of less publicized acquisitions of social media data companies. Apple is characteristically tight-lipped about its plans for monetizing this product with advertising, but speculation is rife that Apple is poised to get a piece of the $600 billion that is spent on advertising today.

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Data Science and Analytics Outsourcing – Vendors, Models, Steps

feldframework_why“Everyone has a [data] STRATEGY right up until they get PUNCHED in the FACE”
– Mike Tyson


If you’re an executive, manager, or team leader, one of your toughest responsibilities is managing and organizing your analytics initiative.

The days of business as usual are over.  Data generation costs are falling everyday. The cost of collection and storage is also falling.  The speed of insight-to-action business requirement is increasing.

Let’s step back and look at the scale and pace of the change that is facing every business today. Think about some facts. There are 3 billion people connected to the Internet. And it is not just about people. Think about 200 billion devices from sensors to machines all connected to the Internet.  And think about the pace. Every 60 seconds, there will be 1 million Facebook updates, 265,000 snapchats, 2.5 million Google searches and 204 million emails sent.

What does all of that mean for enterprise IT and the business? First, it is a connected world. Second, data is the currency of the new economy much like energy and natural resources in the industrial economy. And third, businesses and IT will be intelligent – marked by highly automated processes and artificial intelligence. In other words, business is changing like it never has before. It is a time of tremendous transformation for the technology industry and for our clients.

With this tsunami of data, the bottleneck is clearly shifting from transaction processing to Analytics & Insight-driven “sense-and-respond” Action.

Here are just a few examples of “data-to-insight” as well “insight-to-action” analytics at work:

The following slide from IBM’s Investor Briefing. It summarizes the data-driven transformation underway in most businesses.  IBM like every other IT Services firm is seeing that almost every company in a quest for growth has business initiatives like (1) Identifying root causes of customer attrition & developing retention strategy; or (2) Collecting data and constantly improving the efficacy of multi-channel marketing campaigns and customer targeting;


Better/Faster/Cheaper Analytics Execution

Industrialization of analytics is the new buzzword. Overcoming the jumble of point solutions is a non-trivial challenge in a big firm.  Disparate vendors, disparate capabilities, different interfaces, all acquired over a long period of time.

To meet demand for faster/better/cheaper  innovation around analytics, CFOs and CIOs are rethinking their silo’d sourcing strategies, fragmented tech budgets aligned against one-off projects, and are looking at new ways of doing things via out-tasking,  IT outsourcing and business process outsourcing their Analytics and Data Science functions.

The “should we or shouldn’t we outsource data science” discussion is heating up in board-rooms and executive suites  as analytics becomes core to the firm, C-level execs have to consolidate efforts for delivering the same services to different groups within an organization.

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Market Sizing – Business Analytics and Big Data

future-of“Google, Facebook are really big data companies, not software companies. They collect data, process it and sell it back with value added extensions. They don’t have better algorithms. They simply have more data.”   —  Anonymous


The convergence of cloud, social, mobile and connected computing has sparked a data revolution. More than 90 percent of the world’s data has been generated over the last two years . And with a projected 50 billion connected “things” by 2020 , the volume of data available is expected to grow exponentially. This proliferation of data has created a vast ocean of potential insights for companies, allowing them to know their customers in a whole new way.

Data is valuable. Data is plentiful. Data is complex. Data is in flux. Data is fast moving. Capturing and managing data (Cloud, On-Premise, Hybrid IT) is challenging. It’s a paradox of the information age. The glut of information that bombards us daily too frequently obscures true insight.

Help people uncover, see, understand and visualize data presents a broad and momentous market opportunity….call this user-driven discovery. Take for instance, Facebook (like builds a custom Web page every time you visit. It pores over all the actions your friends have taken—their postings, photos, likes, the songs they listen to, the products they like—and determines in milliseconds which items you might wish to see, and in what order. Is this the future for every firm…..

The opportunity is simply getting bigger by the day. Every customer interaction is generating a growing trail of data (“data exhaust”). Every machine that services the customer is generating data. Every conversation, transaction, engagement, touchpoint location, offer, response  is a potential digital bread-crumb of opportunity.

Now let’s flip the context.   A typical mobile user check their phone interface 150 times a day for updates.  A Gen Y  or Millenial user obviously much more than a Gen X user.  The consumption patterns for information are changing continuously.  Facebook style real-time updates which were revolutionary 5 years ago seem outdated in the mobile world. We live in an “attention deficit economy” where attention is the new basis for competition. The firms that create the evolving experience using data which can grab/hold your attention will attract marketing and ad $$s.

As a result, the buzz and hype around data…small data, big data, machine data, social data, mobile data, wearables data….is relentless. As a result there are a lot of new initiatives and companies.  I have been asked repeatedly by a lot of entrepreneurs and strategy teams about analytics market size and opportunity size.  Product and services firms are also interested in opportunity sizing as they create new offerings in the data rich world.


I thought i would share a mashup of industry and market sizing data i have collected so far.

  • How big is the overall market for Analytics, Big Data?
  • How big is the market for Digital Customer Interaction or Engagement?
  • How big is the market for Mobile and Social Intelligence?
  • How big is the market for Wearables?
  • What is growing fast, faster and fastest?

All good questions as services firms think about digital strategy, analytics and future state.  You always want to be in the “hot” area… selling is easier, valuations are richer, revenue growth percentages exponential.

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