Big data is what you make of it

Submitted on: Tue, 08.26.2014 03:07am - Gary Owen |

"There’s no magic button, only myriad software techniques that may or may not work for problems specific to particular industries.”

Greg Huang’s article for Xconomy (What’s After Big Data? Niche Analytics, Data Wrangling, Smart Storage) had me nodding my head as I read through it last week. The concept of Big Data, like Social Marketing before it, is starting to mature as a buzzword. We see this cycle repeated over and over again in business circles. From my perspective, the lifecycle for any given major buzzword can be described in the following steps:

  1. A single company finds a new and novel approach to a class of problem.
  2. A few more organizations find their own application of that novel approach.
  3. Management Gurus and Consultants study those successful models and attempt to extract some abstract concepts.
  4. Those abstract concepts get named ("Big Data”, “Social Selling”, “Synergy”, etc…)
  5. The general population attempts to understand the abstract concepts and how they can be applied to their own organization.
  6. A very small group of organizations are successful at figuring this out.
  7. Everyone else is confused and doesn’t see how the abstract concepts apply to them.
  8. A new market emerges to help organizations bridge the gap between the abstract concepts and their industry.

We are currently making the transition from Step 7 to Step 8 for Big Data. We see evidence of this in the business media at large as major publications (e.g., the New York Times and Wired) are starting to post articles highlighting the shortcomings of the abstract concept of “Big Data.”

There’s nothing earth shattering in the Xconomy’s case studies. Each highlights a certain way that the “Big Data” concept, translated into a specific application is falling short of expectations. The problem we are seeing is that not all applications of “Large and Complex data sets” are equally valuable. Sometimes the data simply doesn’t support a conclusion, or even if the data itself is incredibly valuable, the signals in the data might be misinterpreted, or misrepresented. (See Edward Tufte for a significant example of this.)

The true value of the data is exclusively in the insights and ultimately the decisions and organizational changes that come from those insights. If the insights aren't made, or worse are inaccurate, then there is no value from “Big Data.” It’s impossible to overstate how important this is. If your company cannot take data and turn it into action, there is no value in the data. That’s right, there is no intrinsic value in being a digital hoarder.

"But once the data is cleaned up and shared, how do companies actually make sense of it all? That’s a separate story, and it lies in the domain of analytics.”

This is where companies like MITS come in. Our expertise is in helping companies (Distributors specifically, since our insights are in this specific domain) take this information and organize it in such a way that non-technical business users can integrate those insights into their daily workflow. If they are able to look at a report and decide to call a customer, increase inventory of a specific product, change a process or workflow, or validate a strategic shift, then we have taken data and turned it into value. Absent that kind of a value we are just playing with technology.