You have probably seen highly charged atmospheres in companies where the executive team has decided that the company must have a Big Data strategy. Those tasked with then putting it together feel this pressure to prepare an impressive plan that represents a big leap and a lot of investment. It’s similar to what happened in the 90’s with CRM and then what happened right around the 2000 mark with the Internet. Faced with the rising wave of interest and investor questions, companies risk making knee-jerk reactions which all too often result in corporate and personal disaster. Big Data is on the same trajectory. We see it in our everyday conversations with others, in the press and promotional arena. It has also been shown in the Gartner “Hype Cycle” for 2012 — http://www.infoq.com/news/2012/08/Gartner-Hype-Cycle-2012.
Leveraging Big Data in your company does not have to be mysterious, intimidating or expensive. There are different ways to approach the elephant — and as the adage goes, maybe the best way it to take it one piece at a time to digest it properly and align it within the organization.
One approach I’d recommend is doing what I call the 3-V Application Value analysis. This is where you assess the specific Big Data that you have access to and then look at the differences that Big Data offers from what data, analyses and resulting applications you currently use. Do this by each of the V’s that define Big Data: Velocity, Volume and Variety. This leads to an opportunities and costs analysis that will then be the basis of a plan of action. This is a reasoned approach to getting the best value out of your investment in Big Data.
For instance, let’s take Velocity. What is it that is different about Big Data because of Velocity? And, when looking at the form of Big Data you have access to, what does that imply for the applications you could build? A very high level assessment is where you would start and it may look something like this:
- Opportunities. The opportunities that come from high velocity data include the development of real time or more immediately updating applications. These might be
- New and more relevant executive dashboards
- Tools that allow you to make adjustments to engagement campaigns while they are executing
- Development of individualized recommendation systems
- Quickly identifying product quality issues
- Better capitalizing on unforeseen benefits or uses of your product or service
- Costs. The costs of taking advantage of these opportunities would be driven by a number of factors, including:
- Instituting new layers of data connectivity
- Building machine learning and continual statistical processes layers
- Designing and implementing real time reporting and simulation tools
The benefit of this kind of approach is the creation of a rational framework for advocating specific kinds of Big Data investment. A team can examine the detailed differences between existing data being used and Big Data, link those to potential new analyses or applications, and tie them to specific investments. The contrasting and incremental nature of this approach takes the mystery out of Big Data by relating it to what you have experience with and providing a stepping-stone approach that builds on strengths and ensures investments will be made with confidence and less risk.