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Posts by Ravi Kalakota

2
Jun

The NoSQL and Spark Ecoystem: A C-Level Guide


EvolutionofDBMS

New Technologies | New Possibilities

As a C-level executive, it’s becoming clear to me that NoSQL databases and Machine Learning toolsets like Spark are going to play an increasingly big role in data-driven business models, low-latency architecture & rapid application development (projects that can be done in 8-12 weeks not years).

The best practice firms are making this technology shift as decreasing storage costs have led to an explosion of big data. Commodity cluster software, like Hadoop, has made it 10-20x cheaper to store large datasets.

After spending two days at the leading NoSQL provider  MongoDB World event in NYC, I was pleasantly surprised to see the amount of innovation and size of user community around document centric databases like MongoDB.

Data Driven Insight Economy

It doesn’t take genius to realize that data driven business models, high volume data feeds, mobile first customer engagement, and cloud are creating new distributed database requirements. Today’s modern online and mobile applications need continuous availability, cost effective scalability and high-speed analytics to deliver an engaging customer experience.

We know instinctively that there is value in all the data being captured in the world around out…no question is no longer “if there is value” but “how to extract that value and apply it to the business to make a difference”.

Legacy relational databases fail to meet the requirements of digital and online applications for the following reasons:

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29
May

Love, Sex and Predictive Analytics: Tinder, Match.com, and OkCupid


Have we got a girl for you” Some very sophisticated machine learning and predictive analytics models are powering the online dating or hookup world.

A lot of innovation is taking place around real-time, geo-location based matching services.  Coinciding with the trend toward mobile, there is a meaningful shift of usage from desktop to mobile devices. The mobile trend also enables tailored dating products to meet the varying romantic and hookup preferences of users.

Take for Match.com which debuted its online dating first site in the U.S. in April 1995.  Today, the Match.com brand hosts sites in 24 countries, in fifteen different languages spanning five continents.  Match.com offers an interactive way for singles to meet other singles with whom they might otherwise never cross paths.

How to model and predict human attraction? Match.com is powered by Synapse algorithm. Synapse learns about its users in ways similar to sites like Amazon, Neflix, and Pandora to recommend new products, movies, or songs based on a user’s preferences.

IntelligentMatching (1)

Enabling dating in a digital world… Match.com uses Chemistry.com to do personalized surveys and get detailed preference data. But when it comes to matching people based on their potential love and mutual attraction, however, analytics get significantly more complex when you are attempting to predict mutual match… the person A is a potential match for person B…. but with high probability that person B is also interested in person A. Read more »

28
May

Data Science and Analytics Outsourcing – Vendors, Models, Steps


feldframework_whyData-driven business processes are not a nice-to-have but a need-to-have capability today. So, if you’re an executive, manager, or team leader, one of your toughest assignments is managing and organizing your analytics and reporting 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.  Systems of Record, Systems of Engagement, Systems of Insight are being transformed with consumerization and digital.

With this tsunami of data and new applications, the bottleneck is clearly shifting from transaction processing to Analytics & Insight-driven “sense-and-respond” Action. This slide from IBM’s Investor Briefing summarizes the data-driven transformation underway in most businesses.

datadrivers

Better/Faster/Cheaper Analytics Execution

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25
May

Big Data Analytics Use Cases


Are you data-flooded, data-driven, data informed? Are you outcome oriented, insight driven or hindsight driven?

Are you a firm where executives claim – “Data is our competitive advantage.” Or sprout analogies like, “data is the new oil”.

The challenge I found in most companies is not dearth of vision… everyone has a strategy and a 100,000 ft general view of the importance or value of data. Every executive can parrot the importance of data and being data-driven.

The challenge is the next step….so, how are you going to create new data products? How are you going to execute a data driven strategy? How are you going to monetize data assets? What are the right business use cases to focus on? How to map the use case to underlying models and data requirements? What platform is a good long-term bet?  The devil is in these details.

Everyone is searching for new ways to turn data into $$$ (monetize data assets). Everyone is looking for new levers to extract value from data.  But data ingesting and modeling is simply a means to an end. The end is not just more reports, dashboards, heatmaps, knowledge, or wisdom. The target is fact based decisions, guided machine learning and actions. Another target is arming users to do data discovery and insight generation without involving IT teams…so called User-Driven Business Intelligence.

In other words, what is the use case that shapes the context for “Raw Data -> Aggregated Data -> Intelligence -> Insights -> Decisions -> Operational Impact -> Financial Outcomes -> Value creation.”  What are the right use cases for the emerging hybrid data ecosystem (with structured and unstructured data)?

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24
May

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


DataVisualizationNumerical quantities focus on expected values, graphical summaries on unexpected values.” – John Tukey, Exploratory Data Analysis, 1977.

 

Decision support needs better visualization. Scorecards, Dashboards, Heatmaps, Alerts, Management Reporting, Operations and Transactions Reporting are all enterprise example of data visualization outputs.

Some data visualization examples include:

  • Data Scientist — uses “R”, a programming language used for statistical modeling, to understand traffic flows and congestion patterns and advise on options to improve travel times for Amazon.com 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.

DataLeverage

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15
Apr

Domo – The Hot New Visualization Stealth Startup


Domo seems to be hottest emerging company in the visualization, BI and so-called “Business Management Platform” area.  I have been seeing it at several clients recently.

According to their pitch.. “Domo is the future of business management.  For all the trillions spent on technology solutions, the way we do business still feels pretty painful. Data lives everywhere. Insights arrive too slowly. Collaboration is still wildly inefficient. Until now. Domo puts the right information, at the right time, into the hands of the people that actually use it to collaborate and make decisions. And it’s transforming the way people manage business.”

Domo grabs live business data from some 300 different sources — Salesforce, NetSuite, Twitter and Facebook included — and presents the data in a live interactive dashboard that can be customized and mixed and matched in all kinds of ways.

A screenshot of is shown below (courtesy of Recode).

domo_screenshot

 

Given the 2 bln valuation and growing hype, I have been struggling to figure out what Domo does that Tableau Software can’t do.  Domo claims to bring the business and all its data together in one intuitive platform. Doesn’t every BI and Data Visualization platform do this? Domo claims to be developed hundreds of proprietary connectors (traditional data sources and cloud based) that connect directly to any source of data across your entire organization, and bring it into one intuitive platform.  Again so does everyone else.

Recode explains this in the following manner…”Domo is not just an application but a platform, which means that if there’s some specialized business app that Domo hasn’t connected to yet, you can now start building your own connections to it and bring that data in. Those individual sections of data in the shot above are called “cards,” and you can rearrange them and add new ones to your view all the time. But if you need a special card that combines a few different bits of data and which hasn’t already been built, Domo today announced a feature it calls Card Builder that lets you create your own.”

You can be the judge and jury on how novel all this is.

Notes and References

1. Domo have been valued at 2 Bln and raised a round of 200M.

2. List of Domo Connectors

Domoconnectors

2
Jun

Apple’s HealthKit vs. Google Fit – Wellness Platforms powered by big data and analytics


mobile-applicationsGame on….I think we just witnessed a next generation leap in Healthcare Wellness (powered by Data and Predictive 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 Apple Watch, (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.

The connected health and wearable devices market has a multitude of participants, including specialized consumer electronics companies, such as Fitbit, Garmin, Jawbone, and Misfit, and traditional health and fitness companies, such as adidas, Nike and Under Armour. In addition, many large, broad-based consumer electronics companies either compete in fitness market or adjacent markets, including LG, Microsoft, and Samsung. Read more »

13
May

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.

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12
May

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.

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.

This posting has been updated and posted on disruptivedigital.wordpress.com

 

10
Mar

Fan Engagement and Wearables: Disney MyMagic+


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

Wearables are the next big thing.  The 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 biometric 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 predictive 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+

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