Skip to content

Recent Articles


Customer Journey Analytics and Data Science

DigitalJourneys-WhyFortune 500 companies are making large investments around Programmatic Marketing (“Marketing that learns”). 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


Read more »


Consumerization of BI: Data Visualization Competency Center

heatmap-pgWhat do users want? Self-service, interactive analytics with all kinds of datasets with instant response times, no waiting.

Today there is a strong move towards “Consumerization of BI” as business users are demand the same speed, and ease of use from their workplace applications as their at-home software.

Consumerization of BI means:

  • Help employees see and understand data
  • Helping employees gain insight into their data using simple drag-and-drop operations to solve business problems
  • Ability to quickly change filters and query conditions and conduct top down analysis via drill down
  • Make data analytics fast, easy, interactive, and most importantly – useful

In every major corporation there is a renewed push to industrialize and improve data visualization and reporting capabilities.

The challenge is not in procuring the next greatest tool or platform but how to organize the people, process and assets effectively to create value, reduce training and support costs.  In other words, how to facilitate and create a flexible operating model for data mining and visualization delivery that provides discipline at the core while giving the business the agility that they need to make decisions or meet client needs?

Decision making is a core business activity that requires facts and insights. Slow, rigid systems are no longer useful enough for sales, marketing and other business users or even IT teams that support them. Competitive pressures and new sources of data are creating new requirements. Users are demanding the ability to answer their questions quickly and easily.

So the new target state is to empower business users along the Discover, Decide and Do lifecycle:

  • Discover new insights by rapidly accessing and interrogating data in ways that fit how people naturally think and ask questions.
  • Decide on best actions by publishing dashboards, collaborating with others, discussing insights and persuading others through data presented in an interactive application (“app”) rather than in a static view.
  • Do what is best at each decision point with confidence, based on the consensus that develops when new data is aggregated and explored with multiple associations and different points of view. Teams can take action more rapidly and move projects forward more effectively when everyone understands the data underlying decisions.

The challenge for business users is data discovery and ease-of-use. They want to focus on aggregating and visualization. They want the interactive ability to quickly change filters and query conditions.

The challenge for infrastructure and application teams in every corporation is to deliver new easy-to-use platforms to their business partners quickly and consistently while maintaining governance and control.

To meet both sets of requirements, best practice firms are creating Data Mining and Visualization Competency Center or Centers of Excellence (DV-CoE) to ensure that the people, process and technology investments are not duplicated and addressed in a way that maximizes ROI and enhances IT-Business partnership. I have seen many cases where not having a proper structure leads to sub-optimal results. Read more »


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

Read more »


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:

Read more »


Love, Sex and Predictive Analytics: Tinder,, and OkCupid

Have we got a girl for you” Some very sophisticated predictive analytics are powering the online dating or hookup world.  A lot of innovation is taking place around real-time, geo-location based matching services.

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) 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 »


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.


Better/Faster/Cheaper Analytics Execution

Read more »


Big Data Analytics Use Cases

Are you data-flooded, data-driven, data informed? Are you 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 view of the importance or value of data. Every executive can parrot the importance of data.

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 use cases to focus on? 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 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 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)?

Read more »


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 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.


Read more »


The Sand Hill 25 Bitcoin Innovative Disrupters

Bitcoin — the Internet currency, payment system and technology — is about the birth of a new “digital” monetary ecosystem. Bitcoin bypasses traditional banks and clearinghouses with blockchain technology. Like every innovation it creates new regulatory and compliance challenges. There is growing interest in knowing where the money has come from and at the same time the anonymity of bitcoin makes creating an data trail a tricky task, but it’s possible to say whether certain bitcoin addresses are involved in mining, or have been associated with gambling transactions.

More recently, nationally known merchants like, Zynga and the Sacramento Kings basketball team have begun to accept Bitcoin payments. Even political candidates are taking donations through the system. Worldwide transaction volume keeps growing, as does the number of Bitcoin users.

Bitcoin is built on some heavy and complex data-crunching.  Like any ecosystem, it will have its share of winners and losers. The Bitcoin “Innovative Disrupters” are those that have the best odds at being winners.  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



Get every new post delivered to your Inbox.

Join 677 other followers

%d bloggers like this: