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Big Data: Fundamentally Different, Not Just Bigger

Why traditional methods of analysis just aren’t sufficient for working with big data


  • Victor Smart

    This is intriguing for management accountants. Are they and their tools suited for Big Data? And what does Tom mean the business models may be different. I am currently writing a book on Big Data and finance role for Wiley. Interested to hear your views.

    • Tom Deutsch

      Hi Victor, thanks for the note.

      To be fair, I haven’t personally seen an accounting oriented use case. Most of the compute needs there are pretty well served via traditional ERP-oriented systems, most of my time is spent in exploratory or new frontiers. That said we are seeing a lot risk related work where sheer speed and flexibility of compute is the driver, so there is probably an adjacent space here.

      What do you think?

  • Mike Vostrikov

    Thank you for the interesting article, Tom. Maybe when we speak about big data we are used to think of huge amounts of heterogeneous unstructured data, when analytics takes place during chains of map-reduce jobs. This approach significantly differs from traditional and requires bunch of new skills and tools. However in my opinion big data doesn’t always mean fundamentally different ways of working. For example, we can use Hadoop+Hive just for storing huge amounts of data and extracting small data, that we can then process usual ways, using usual analytic tools. Nothing fundamentally new in such a case but large self-healing data-storage cluster.

    • Tom Deutsch

      Hi Mike, thanks for the note. I agree with your point that not all the use case result in a massive data set for end users, but in the example you gave how you end up with that small/familiar data set is indeed different. I’d also cooperatively suggest that if that is “all” you use your Hadoop cluster for we probably need to all get more creative on other un-met needs.

      Thanks for the note, and appreciate you making the valid point.