Dinosaurs made extinct by Big Data – Part 2

My recent post on Brad Pitt’s Moneyball, led to a great discussion with Dr. Vijay Mehrotra, a well known management professor and analytics thought leader at the University of San Francisco’s School of Management.  Vijay has also written a number of pieces around Moneyball (Part 1, Part 2, Part 3).  The story turns out to be a great roadmap of how successful Big Data initiatives typically run.  Dr. Mehrotra pointed out several additional key elements as well as warnings that are worth repeating.

It starts with pain or opportunity

Successful Big Data initiatives are rarely started just to “do something” with Big Data.  Instead, they grow out of either a real pain point or an identified opportunity.  In Moneyball, the pain was the underfunded Oakland A’s simply couldn’t field a competitive team against their better funded rivals.  At least not by competing the old way.

Google offers a classic example of the opportunity side of the coin.  By the early 00’s, a huge amount of data was buried in web pages that were publicly available.  The opportunity lay in using this data in a better way to displace search incumbents.

Most of the work is in selecting, capturing and cleaning the data

This stuff doesn’t make an exciting story.  Indeed, it’s often skipped for narrative clarity – or just to keep viewers interested.  This work, however, usually constitutes the bulk of the efforts and is critical for success.   Think of the challenges that Google overcame to execute their vision – web crawling, de-duping, filtering and data management.  These are at the heart of any Big Data initiative.

Not a linear pattern

Too often the story is simplified in retelling: collect the data, clean the data, analyze the data.  In real life, the journey is more messy.  At each stage, you’re typically circling back to the start with new questions, new data requirements, new directions.  Having this willingness to ask questions and learn throughout the initiative is critical for success.

Take the case of VinoEno, a San Francisco-based wine-recommendation start-up that built a recommendation engine that can match people’s various tastes to the myriad attributes of various wines.  As the project progressed, success meant being willing to learn and to change initial assumptions.

New ways to make decisions

At the start, many Big Data initiatives imagine using analytics to make decisions the old way, just better.  However,  successful projects, lead to revised ways of making decisions.  This change in the way decisions are made is the end point and requires to new ways of doing things and new processes.  Google revolutionized advertising not just by providing better data.  What Google ended up doing was creating new decision frameworks and approaches to how campaigns are run.

What’s your pain or opportunity?


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Categories: Data Analytics / Big Data, Disruption, Strategy

Author:Tom Molyneux

A business process strategist with a focus on real-time event management.

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One Comment on “Dinosaurs made extinct by Big Data – Part 2”

  1. Jeanne Roué-Taylor
    June 18, 2012 at 9:09 pm #

    Reblogged this on Fabless Labs.

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