|By Udayan Banerjee||
|February 18, 2013 01:15 PM EST||
“…data on its own is just numbers, and numbers can’t run a company…”
“…the quest to assemble relatively unimportant information can actually distract us from the few relevant facts…”
“…small data sets won’t always give you the full picture, but the most impactful conclusions often actually require the least data…”
“…the world contains an infinite amount of data and almost all of it is irrelevant to whatever you’re trying to measure…”
“…the objective of analysis is to boil down an impossibly complex world into a digestible set of approximations…”
“…always choosing the most granular option … (makes) it easier to miss the forest for the trees…”
“…clarity in how you present your analysis is often much more important that the precision of your model or completeness of your data set…”
These are not my words. I have picked it up from the post Mo Data, Mo Problems: 7 Inconvenient Truths About Big Data by Nick Petri.
And, no these are not exactly the seven inconvenient truth about Big Data. These are words in the article which I loved.
The seven inconvenient truths about Big Data as per Nick Petri are:
- Data ≠ Knowledge. Data x Analysis = Knowledge
- Data and Analysis Compete for Valuable Resources
- Data Always Seems Important. Often It Isn’t
- Small Data Can Have a Huge Impact
- There’s No Such Thing as a Complete Data Set
- Granularity Often Makes Decision Making Harder, Not Easier
- Big Data is Useless if You Can’t Communicate it Clearly to the End User
More I dig into Big Data more I get convinced that the key is…
…the ability to ask the Right Question!
Everything else will follow.
Well this is probably true not only for Big Data but for most things in life!
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