Feb 9

Feb 9 What Goes Up Must Come Down: Comparing 'Big Data' with 'Web 2.0'

For reasons unknown an old blog post from 2006, Comparing Eras of Innovation: 1950’s Aerospace Advances and Today’s Web 2.0, recently showed up in my blog’s weekly top ten list.

While I used to post a lot about “web 2.0” related topics along with my consulting, I don’t do much along those lines any more.

Some years ago the term “web 2.0” gained traction with a lot of people. Some looked at it from a communication perspective. Others approached it from a technology perspective. It eventually became a vague marketing term.

Those who were involved in consulting and project management (raises hand) found it an impossible term to shake even as the term’s meaning morphed beyond control.

As the Google Trends chart of relative frequency in searches at the right shows, web 2.0 searches peaked around early 2007 and have been declining ever since as more specific terms and interests have evolved or taken over.

Based on what I been learning as I investigate big data’s implications for planning and project management I wonder if something similar may be about to happen with the term “big data.”

One might not think so based on the Google Trends chart below. Yet, some who approach big data from a technology perspective are already saying the term is “old hat” (see Regarding Big Data, One Manager’s ‘Resistance’ Is Another Manager’s ‘Caution’). Others are beginning to question the business value of the movement (see Big data initiatives not quite delivering yet, survey shows).

As suggested in my “Regarding Big Data” article, different communities adopt technology at different rates.

Also, some organizations find it difficult or expensive to change key elements of its business-critical infrastructure (including both systems and processes) rapidly or cheaply.

While I expect that the term “big data,” like the term “web 2.0,” will be with us for a long time, it use may very well decline as different organizations move ahead by incorporating improved analytics into both operations and their decisionmaking — regardless of what they call it.