How to draw a Big Data implementation roadmap
"Big Data implies that data sets are so large and complex they become awkward to work with using standard tools and techniques."
That was the definition of Big Data given by Rod Bodkin, founder and CEO of the consultancy, Think Big, at the 2014 Teradata Partners Conference taking place this week in Nashville, TN.
Buried within this simple definition lies many problems and pitfalls that retailers and other organizations face when their data needs exceed their current capabilities. In fact, Mr. Bodkin was quick to point out that new, affordable technology has enabled the growth of shopper and product data though new customer touch points and new retailer channels.
But organizations often struggle with how to best get from where they are today to a happier place where their database(s) and their access tools meet the requirements of doing business competitively.
Migrating to a Big Data solution is a journey, according to Mr. Bodkin, and the trip is rife with pitfalls and challenges. Among them are:
- Sorting through the corporate politics of siloed databases and siloed applications;
- Attaining a vision for the enterprise under a Big Data platform;
- Avoiding damaging short cuts for the sake of time or expense savings;
- Addressing skill gaps among current personnel;
- Assuming that the journey will be "business as usual."
To the contrary, Mr. Bodkin asserts that Big Data inherently involves change management and must be sponsored at the highest levels of the organization. To be successful in attaining corporate goals and financial rationale for the effort, it must be approached holistically, not as an ad hoc project that is layered upon the many other initiatives the organization has on its docket.
To mitigate at least some of the pitfalls and enhance the chance for successful implementation identifying "low hanging fruit" — in other words, a pain point that a Big Data solution can remedy that has an existing, accepted ROI — is an excellent starting point. From there a "roadmap" can be drawn that shows incremental milestones, which should be met and communicated as often as every 45 days. Finally, along the way assess the need for enhancing skill sets and adjust accordingly. In many cases, the team you have today will need to augment their expertise and perspectives for success.
What “low hanging fruit” should retailers focus on as part of the Big Data implementation process? What steps do you think are being underestimated by retailers in efforts to capitalize on Big Data?