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Posts from the ‘Big Data’ Category


Machine Learning and AI @ FaceBook

FacebookAIMachine Learning (ML) and AI are at the bleeding edge of data science, deep learning and predictive search today.

Everyone is jumping on this AI powered data-driven engagement (“ambient experience and convenience”) trend.

Salesforce CEO Marc Benioff said at a recent conference: This is a huge shift going forward, which is that everybody wants systems that are smarter, everybody wants systems that are more predictive, everybody wants everything scored, everybody wants to understand what’s the next best offer, next best opportunity, how to make things a little bit more efficient.”

Facebook is a case study of where AI/ML are being used to transform user engagement and experiences. I am starting to see many leading firms investing in ML Accelerators and Platforms as part of their data science strategy.

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Analytics and ML Use Case – Robo-Advisors in Wealth Management

New data driven FinTech business models built on Hadoop, Spark and Machine Learning are rapidly emerging and disrupting wealth management.  Here is my recent posting from about one such use case… Robo-Advisors.

We are in the early stages of a generational shift in wealth management, especially “plain vanilla” investing for the mass affluent and millennial segment.  Until recently, you had only two options when investing:

  • Do-it-yourself (DIY)
  • Hire a registered investment advisor (RIA)

Now there is a third option.  Robo-advisors are new class of personal financial advisors that provides online, algorithm based portfolio management with minimal human intervention. Robo-Advisors going after the low-end of brokerage/RIA business with automated asset allocation using Modern Portfolio Theory.

The Robo-Advisors market leaders who are serving the mass affluent include are:

  • Wealthfront (with over USD 2.6bn in assets under management (AuM) and 20,000 investors);
  • Betterment (with over USD 1.4bn in AuM and 70,000 investors); and
  •  FutureAdvisor (With over $600 million in AUM).

The timing for this market shift coincides with three trends: consumerization, digital tools, and disillusionment with status-quo investment advisors.  The gyrating stock market driven by program trading is increasingly bringing Robo-Advisors, algorithmic portfolio management to the forefront.  Investors are getting disillusioned with traditional investment advisors who simply track the market indices (SPY, QQQ or Russell 2000) by purchasing ETFs at best.

Many banks and brokerage firms over the years have shifted their focus to serve ultra high net worth (UHNW) and high net worth (HNW) investors, leaving an opportunity for firms to target the “mass affluent” investors, or those with less than $1 million in investable assets. Younger investors are increasingly interested in online digital advice (trial-and-error bets), as opposed to hiring an adviser.

wealthfront3 Read more »


Customer Journey Analytics and Data Science

  • DigitalJourneys-WhyWhere do customers abandon the shopping process? Is it the same in every geography?
  • Audience of One…. Who are your fans versus haters in the marketplace?
  • How do customers feel about your products? How engaged are customers with your brand versus your competitors’ brands across social media and web channels?

Fortune 500 companies are making large investments around Programmatic Marketing, Sales and Service (“marketing that learns”). Programmatic marketing is the application of automated technology through which media buyers and sellers may align organizational processes in support of ongoing, channel-agnostic customer engagement (and to allow for the continuous optimization of that effort as business strategies evolve) in order to drive revenue.

One of most often implemented use case in Programmatic Marketing is customer journey mapping and analytics.

Why? Because, deciphering the nuts-and-bolts” of individual customer journeys online (and deducing intent) is core to improving customer experience and driving brand loyalty.

Specifically, the objectives are:

  • Visualize and map the end-to-end customer journey by personas
  • Optimizing on the right journey attributes to increase yields  by >30% lift… Uncover the right combination of web, mobile and physical channels, content and experiences that  best achieves the target goals
  • Enable marketers to identify journey bottlenecks for individuals and aggregates
  • Leverage actual behavior data to enhance and personalize the experience for each individual customer


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Data/BI/Analytics Evolution @ NetFlix

More data + Better models + More accurate metrics + Better approaches & architectures = Lots of room for improvement!

netflixIt’s amazing to watch how quickly the data engineering / analytics/ reporting/ modeling/ visualization toolset is evolving in the BI ecosytem.

There are clearly massive foundational shifts taking place around big data. I am not sure how large conventional Fortune 500 firms can innovate and keep up with what’s going on.  I have run into CIOs who have not heard of Hadoop in some cases.

It’s also fascinating to see how data-driven “bleeding” edge firms like NetFlix are pushing the envelope.  Netflix stats are amazing:  1/3+ Internet traffic (NA / peak);  100+ Million hours per day; 65+ Million members / 50+ countries; 500 Billion Events / Day.

NetFlix is clearly reinventing Television and targeting 90 million potential subs in the US market alone.  Binge-watching, cord-cutting are now part of our everyday lingo. What most people don’t realize is how data-driven Netflix is…. from “giving viewers what they want” to “leveraging data mining to boost subscriber base”.

Viewing -> Improved Personalization -> Better Experience is the virtuous circle.

Here is a glimpse at how their BI landscape has evolved in the past five years as they integrate 5 million to 6 million net adds for several years now.  The figures are from a presentation by Blake Irvine, Manager Data Science and Engineering.

BI tools @ NetFlix pre-Hadoop

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The NoSQL and Spark Ecoystem: A C-Level Guide


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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Love, Sex and Predictive Analytics: Tinder,, 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 which debuted its online dating first site in the U.S. in April 1995.  Today, the brand hosts sites in 24 countries, in fifteen different languages spanning five continents. 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? 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… uses 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 »


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).



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



2015 Year in “PreReview” in Technology

The summer of 2015 marked the release of the blockbuster Sci-fi movie, tEREUy1vSfuSu8LzTop3_IMG_2538“Terminator Genisys,” which grossed a record $350 million at the box office and further popularized the notion of time travel. In addition to sequels and prequels, Hollywood has now adopted plots for movies in which the audience can choose among alternate storylines and follow them to their logical conclusion. The future, as we know it, is plural. This year in our PreReview of 2015, we once again present a few alternative scenarios for the future from our vantage point at the end of 2014.

New business models created by emerging technologies and unforeseen partnerships dominated the headlines in 2015.  Trending technologies such as the Internet of Things approached half the level of big data during 2015. Trending terms in the mainstream media such as drones and Bitcoin scored high in Google trends.

Here are three headlines from 2015 that caught our attention.

FedEx launches “parcelopter” service for 50-minute delivery  Read more »


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 »


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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