Missing Wood for Trees, Why Big Data Isn’t About the Big Data

Big Data is a hot topic of discussion these days and is on top of “To Do” list of senior executives of large and medium sized companies, many of whom have started investing in required infrastructure for leveraging Big Data.

While this is a good start, it is important to remember that Big Data isn’t about the Big Data. Yes, you read that correctly – Big Data isn’t about the Big Data, rather it is all about making better business decisions using the insights gained from analyzing structured and unstructured data that is available. If one focuses solely on “Big Data” and forgets the business context/how the data is going to be used, one is likely to miss wood for trees.

Instead of focusing on Big Data per se, how about starting with business problems that we are trying to solve, for example reducing customer defection/churn or improving cross-sell/upsell rates in CRM context or reducing inventory carrying costs in ERP context.

Once you have identified the problem to be solved, identify what structured and unstructured data are required for making better business decisions. This may include Social Media data for example besides ‘internal’ CRM data. Once you have identified the structured and unstructured data, internal and external to the organization, formulate a strategy on how the data is going to be used or analyzed and at what level of granularity. Only after this should one decide on what tools to use and make required investments in Big Data infrastructure and not other way around.

So don’t invest in Big Data infrastructure first not knowing what, why and how, and put cart before the horse. Rather identify a business problem to solve first, identify what structured and unstructured data are required for making better business decisions, develop a use case for Big Data, run a  successful pilot project and then invest in required Big Data infrastructure.

What do you think? Do you agree that Big Data Isn’t About the Big Data, rather it is all about making better business decisions using the insights gained from analyzing structured and unstructured data? Look forward to hearing your thoughts and comments:

 

 

  • http://twitter.com/ChatWeekly WeeklyBusinessChat

    Making a distinction between structured and unstructured data and segregating them sure should be an useful idea. Having an approach starting with business objectives sure is the only way to go. Not only with Big data, it is the same with Social Media tools, Cloud computing, Mobile or the convergence of these with Big Data. Starting with tools and technologies sure is missing wood for trees. Very basic but often missed. I keep harping the same with businesses. It is hard but the only way. 

  • http://blog.varadh.com/ Varadh

    Hi sorry that I had posted my comment from the id weeklybusinesschat. This is my id. 

  • paras_doshi

    Great insight! Thanks for sharing.

  • http://www.client-line.com Natalie Lihacova

    Harish, thank you for sharing your thoughts on this.

    Right now we are developing a Case Study on how Social Media monitoring tools can help us with our customer’s Complaint Management process. Agree with you that business idea is more important than data.

    However, I believe that an opposite approach can be used just as well: a good Data Scientist can draw business ideas just by looking at data with the professional eye…

  • About Dr. Harish Kotadia


    That's me with photo gear,  taking snaps of Texas wild flowers. #texas

  • Dr. Harish Kotadia

  • Dr. Harish Kotadia is an industry recognized thought leader on Big Data and Analytics with more than fifteen years' experience as a hands-on Big Data, Analytics and BI Program/Project Manager implementing Enterprise Solutions for Fortune 500 clients in the US.

    He also has five years' work experience as a Research Executive in Marketing Research and Consulting industry working for leading MR organizations such as Gallup.

    Dr. Harish Kotadia's educational qualification includes Ph.D. in Marketing Management. Subject of his doctoral thesis was Customer Satisfaction and it involved building a statistical model for predicting satisfaction of clients with services of their ad agency.

    His educational qualification also includes M.B.A. and B.B.A. with specialization in Marketing Management and Diploma in Computer Applications.

    Dr. Harish Kotadia currently works as Principal Data Scientist and Client Partner, Big Data and Analytics at a Global Consulting Company. Views and opinion expressed in this blog are his own.



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