Big Data: The Seven Step Method
October 19, 2015
I hear quite a bit about methods with steps. I, therefore, was not surprised to read “The Seven ‘Simple’ Steps To Big Data.” The main idea is that Big Data can be complicated. I suppose most things can be complicated if certain fundamentals are not mastered. My great grandmother could tat, which I learned was a way to make weird things placed on chairs to prevent the fabric from wearing or staining. I watched her.
Why write a book explaining how to “do” Big Data. It takes seven easy steps. Making a lace thing requires a book, good vision, supplies, and skill.
She explained. I did not get it 60 years ago, and I don’t know how to tat. I can, however, write about it. Most of the comments about Big Data fall into this category. Folks cannot “do” Big Data, but, by golly, many people can write about Big Data.
The article presents seven steps. Before you try to follow these steps, you may want to consider whether you or your organization has the resources to get the foundational knowledge and processes in place before you “do” Big Data.
Here are the steps:
- Get a “business rationale.” I think this means that one should have a reason to “do” Big Data and then explain how Big Data will make an immediate and direct contribution to one’s organization. Accountants may not understand Big Data, but they do get the idea of cost overrun and spending for something that generates grousing in carpetland.
- Learn the lingo. Yep, knowing what words mean can be important. However, if one employs a mid tier consultant, why not let that expert translate? Works well, at least for the compensated consultant.
- “Care about data lineage.” With regard to terminology, I am not sure what data lineage means. My hunch is that data should be valid, in a processable form, and fresh.
- When and where factor. This is another puzzler to me. The idea remains murky, which may inhibit one’s ability to “do” Big Data. But maybe not?
- Correlation does not imply causation. Ah, a chestnut from various classes which taught me about mathy things. The idea is that bonehead mistakes occur in Statistics 101 and real life in Fortune 1000 outfits. See www.TylerVigen.com/spurious-correlations.
- Be a trained seal: Balance “new innovation” with “hardened enterprise grade tech.” I think this means use what is in the text book and whizzy new system.
- Rely on “reference architectures.” I assume this means buying a name brand Big Data system to “do” one’s Big Data activity.
Does the list appear simple? Not to me. Tatting is a walk in the park compared to figuring out how this list of sophisms makes Big Data easy. Maybe tatting will make a come back? Is there a market for tatted antimacassars into which terminated Big Data experts can dive.?
Stephen E Arnold, October 15, 2015
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