Many of us remember the great comedy routine by Abbott & Costello “Who’s” on First, “What’s” on Second, “I Don’t Know’s” on Third. If not, take a look at this YouTube link for a quick laugh:
So what does this have to do with Big Data and the value that can be derived through meaningful business analytics? Well just about everything. In the Abbott & Costello routine, the names of the players on the bases are creating miscommunication and confusion. The same confusion can often be seen when companies are trying to derive meaningful insights through Big Data initiatives.
Big Data initiatives that create “One Version of the Truth” are more successful in driving strategies that are actually implemented and that have significant financial impact.
Here are three considerations to help drive One Version of the Truth to achieve success and the realization of significant financial impact through the use of analytics and Big Data:
- Cross-functional participation is critical. This is especially true in defining the objective (intent of the effort), reaching agreement on how the Big Data is going to be used (repurposed), determine the sources of the data and the data validation process. Cross-functional consensus early in the process can help eliminate the analytical results being questioned and or dismissed. Without cross functional participation, you minimize your chances at finding One Version of the Truth and achieving success.
- Assuming the result you are seeking is an ongoing need of the business, it is important to recognize early on that your Big Data approach must be sustainable. This means that the sources of the data, the rules on how data is going to be used and the way that validation occurs are repeatable and consistent. Not giving this the right level of priority is a sure way to lose the organization’s confidence in the ongoing results. You don’t want to create One Version of the Truth once, you need to be able to create it every time.
- Big Data initiatives need to be very intentional with regard for the desired result. Hoping to find those nuggets of gold by wandering around a landscape containing masses of data is futile. One Version of the Truth eliminates turf battles and highlights not just nuggets but veins of gold.
Naturally there needs to be flexibility to make changes but ensuring that the above considerations are applied is critical.
For those that don’t agree, consider the following: how many times have you been involved in a project where the work that was done was solid but at the close of the effort someone objects to the results? Their argument is that they don’t trust the data that was used or agree with the analytics approach that was taken. Yep, one step forward, two back and then the nail in the coffin.
No one wants to be associated with a career ending project that fails. Following the three considerations above can help. Otherwise you may find yourself answering the question from others in your company “Who’s on First”?
I would appreciate hearing your thoughts and comments.
All the best, Richard
Richard Sharpe is CEO of Competitive Insights, LLC (CI), a founding officer of the American Logistics Aid Network(ALAN) and designated by DC Velocityas a Rainmaker in the industry. For the last 25 years, Richard has been passionate about driving business value through the adoption of process and technology innovations. His current focus is to support CI’s mission to enable companies to gain maximum value through specific, precise and actionable insights across the organization for smarter growth. CI delivers Enterprise Profit Insights (EPI) solutions that enable cross-functional users to increase and protect profitability. Prior to his current role, Richard was President of CAPS Logistics, the forerunner of supply chain optimization. Richard is a frequent speaker at national conferences and leading academic institutions. His current focus is to challenge executives to improve their company’s competitive position by turning enterprise wide data from a liability to an asset through the use of applied business analytics.