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AUG 10, 2012 1:46pm ET

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Is Big Data Really What You’re Looking For?

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Do you think you are ready to tackle Big Data because you are pushing the limits of your data Volume, Velocity, Variety and Variability? Take a deep breath (and maybe a cold shower) before you plunge full speed ahead into unchartered territories and murky waters of Big Data. Now that you are calm, cool and collected, ask yourself the following key questions:

  1. What’s the business use case? What are some of the business pain points, challenges and opportunities you are trying to address with Big Data? Are your business users coming to you with such requests or are you in the doomed-for-failure realm of technology looking for a solution?
  2. Are you sure it’s not just BI 101? Once you identify specific business requirements, ask whether Big Data is really the answer you are looking for. In the majority of my Big Data client inquiries, after a few probing questions I typically find out that it's really BI 101: data governance, data integration, data modeling and architecture, org structures, responsibilities, budgets, priorities, etc. Not Big Data.
  3. Why can’t your current environment handle it? Next comes another sanity check. If you are still thinking you are dealing with Big Data challenges, are you sure you need to do something different, technology-wise? Are you really sure your existing ETL/DW/BI/Advanced Analytics environment can't address the pain points in question? Would just adding another node, another server, more memory (if these are all within your acceptable budget ranges) do the trick?
  4. Are you looking for a different type of DBMS? Last, but not least. Do the answers to some of your business challenges lie in different types of databases (not necessarily Big Data) because relational or multidimensional DBMS models don’t support your business requirements (entity and attribute relationships are not relational)? Are you really looking to supplement RDBMS and MOLAP DBMS with hierarchical, object, XML, RDF (triple stores), graph, inverted index or associative DBMS?

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Comments (3)
Great words of caution Boris! Seems to me that scant few organizations have even an inkling of a Big Data strategy. Also great to see the industry (analysts and others) finally adopting and adapting Gartner's original "Vs" of Big Data we first wrote about over 11 years ago. For future reference, and a couple chuckles, here's that piece I published in 2001: http://blogs.gartner.com/doug-laney/deja-vvvue-others-claiming-gartners-volume-velocity-variety-construct-for-big-data/ --Doug Laney, VP Research, Gartner, @doug_laney
Posted by Douglas L | Saturday, August 11 2012 at 10:15AM ET
Doug, thanks for the comments. And thanks for following Forrester work - it's very rewarding to see that other industry analysts pay attention to what we say. In the end - we all serve the same clients, so it's great that we have a consistent message. I also agree with my colleague, Mike Gualtieri (http://blogs.forrester.com/mike_gualtieri/12-05-17-whats_your_big_data_score), that while xVs are an important attribute of defining where DW/BI 101 stop and Big Data starts, by themselves they are not actionable. That's why Mike is recommending adding "activities" and I am recommending adding filtering criteria and categorizations. But xVs are still definitely important parts of the Big Data equation. Cheers!
Posted by Boris E | Saturday, August 11 2012 at 1:26PM ET
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