Why Your Investment in Analytics is Likely to be a Complete Waste of Money

Big Data DashboardPresumably, analysis of data holds the promise of increase in growth and profitability of any business. This presumption leads to wide adoption of any technology that has “big data” or “analytics” in its name, as well as massive venture capital investment in the sector.

The number of companies that sell services and tools to aggregate, store and process very large volumes of data is astonishing. In addition, there is a dazzling number of companies  offering “free” analytics and data visualization tools. Yet, there are very few companies that can claim success of achieving growth and profitability goals after investing a lot of money, efforts and political capital.

Let me make it clear – I sell customer experience analytics services and I am not in a position to throw stones at technology providers or data scientists. My beef is with the upside-down approach taken by many organizations in attempts to leverage data analysis into the production of profitable decisions.

Those who believe that bigger data possesses unreasonable effectiveness will invariably be disappointed. Many believe the more data you have, the more unexpected insights will rise from it, and the more previously unseen patterns will emerge. This is the religion of big data that promises to defy the gravity of management science. While I do believe in miracles, I am very skeptical about being able to buy them from third parties.

Miracle in step two

 “Big data is like counting grains of rice in front of a hungry man. He doesn’t care about the number of grains. He just wants a bowl of cooked rice.”  Stephen Yu, Willow Data Strategy

One is not likely to make sound management decisions without knowledge of the specific domain of business.  When the domain knowledge meets data analytics many good things start to happen:

  1. Cultural biases and beliefs, that often act as barnacles impeding your progress, will be challenged;
  2. Departmental impacts on a business process, that are often overlooked, will get exposed;
  3. New hypothesis (model) for better business decisions can be created. More data can be identified to test these hypothesis. Better testing produces better, more accurate, models and better models support better decisions: the decisions that make your business grow faster and more profitable.

The role of analytics is to provide support for building predictive models. Predictive modeling is about knowledge of domain and scientific method. Big data can provide and store a lot of additional content for mining, but not much more.

“As for the modeling… it’s like any other science. You learn the domain, then you make the model, then you learn some more. There’s no magic. It does help to learn plain old ordinary statistics, and if you want to do it like Nate Silver you learn a lot of statistics.”

Analytics without domain knowledge, is as likely to provide you with actionable decisions, as a pile of bricks without blueprints is to help you construct a house.

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7 Responses to Why Your Investment in Analytics is Likely to be a Complete Waste of Money

  1. Maz Iqbal says:

    As one who filled the role of European Practice Director for Customer Analytics where the core servIce was data mIning to build predictive modeLs and segment the customer base, I find myself in total agreement. One does not and cannot mine data with a blank slate – sound business understanding is necessary. Further big data can mean a lot of dirty data…..

  2. Olaf Hermans says:

    Hear hear… Sorry for all the managers who thought it was about buying a software package that can replace good old research

  3. Jerome Pineau says:

    Before searching for a needle in a haystack, one should probably calculate the probability of said needle for being in the haystack 🙂

  4. Charles Lafage says:

    Thanks for making this great point. Data science & predictive analytics in isolation from business savvy, intuition, experience, are bound to produce only the most predictable recommendations. Data isn’t knowledge. it is one source of information among others can can be used to develop knowledge.

  5. Gregory says:

    “Big data is like counting grains of rice in front of a hungry man. He doesn’t care about the number of grains. He just wants a bowl of cooked rice.” Stephen Yu, president and chief consultant of Willow Data Strategy

    I am glad it resonated with you. It takes a concentrated effort to merge different, specialized perspectives to produce better, more productive and sustainable models.

  6. Gregory says:

    Too many people rely more on their “vision” than probabilities estimates. Probably because it takes less effort.

  7. Ramon Chen says:

    Very nice post Gregory We too believe that big data without context is not useful. In fact, we would go a step by saying that reliable data quality is the foundation, upon which relevant insights can then be derived. The next step then is to translate that into recommended actions for business users. So they can actually take action via data-driven apps.

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