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Data Revolution, Part 1: Is It a Revolution?

by Svetlana Sicular  |  February 12, 2013  |  1 Comment

Like Lilliputians from Gulliver’s Travels, divided over which end of a boiled egg to crack, modern technologists are debating whether big data is a revolution or an evolution.  Roughly 60% of the delegates at the last week’s Gartner BI Summit in Europe voted on evolution.  It looks more like an evolution to these BI and analytics practitioners, because data is part of their job routine.  It is a revolution for the others, who have not dealt with data before, and now they must.  Gartner predicts enormous amount of big data jobs, only one third of which will be filled.  Those, who voted for the evolution, are already a part of this one third.

Why are we on the brink of data revolution?

  • First of all, revolutions disrupt. The signs of data liberation and democratization are everywhere.  Organizations are interested in dark data, trapped in dungeons and silos, because they want to free it and use. People type text messages, blog and tweet — they democratize data.  Machines talk to machines via data. Cars send information back to the manufacturers — they liberate data (which disrupts the automobile industry).  Only non-disruptive effects of big data are evolutionary; the game-changing consequences of data are revolutionary.
  • Second, revolutions happen, when the “lower classes” do not want to continue in the old way and the “upper classes” cannot carry on in the old way.  Enterprise information management and analytics practitioners do not want to process new and liberated data in the old way — they start using new technologies, sometimes in the cloud closet, but more often, openly.  Upper management cannot carry on in the old way: companies have to compete and win in the data-driven economy.  They are attracted by the shine of gold, a pure result of data alchemy.
  • Finally, as it always happens during revolutions, most people are confused about what is going on.   They struggle to understand where, when and how to use data. They have ideas for big data or consider possibilities, but knowing which to pursue is difficult. These people are exactly the majority that usually decides the fate of revolutions.

If you are currently dealing with the dilemma of data revolution vs. evolution, keep in mind a Lilliputian prophet, who has said, “All true believers shall break their eggs at the convenient end.”


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Category: data-scientist  big-data  cloud  data  data-paprazzi  data-revolution  eim  information-everywhere  inquire-within  market-analysis  

Tags: data-revolution  

Svetlana Sicular
Research VP
6 years at Gartner
23 years IT industry

Svetlana Sicular is passionate about bringing analytics to domain experts and helping organizations successfully compete by applying their business acumen in analytics and data science. She is convinced that domain expertise and high-value data are the greatest assets that companies should monetize in new analytics applications. Read Full Bio

Thoughts on Data Revolution, Part 1: Is It a Revolution?

  1. Alex Kosty says:

    This is evolution till networks bandwidth increase with the same speed, but already we have a problem with uploading or downloading more then 100-200Gb, and when you need pass some Tb it became a problem. So in data growing we need to find “aurum mediocritas” which would be balance between speed and volume.

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