With the huge investments being made on big data throughout the business world — and the financial industry, in particular — it’s easy to see why organizations feel pressure to keep up with the Joneses.
Banks, investment firms, credit unions and the like represent 11.6% of big data app development — one of the most active sectors in that realm, according to a recent status check by Forbes.
These entities aim to capture more data than through traditional methods, visualize data in new ways, and manage unstructured data. Those are all worthy goals, and hold great promise to locating and leveraging market differentiators in an ever-increasing 21st century financial services landscape.
But that quest also carries great potential to drown your organization in a sea of murky, unremarkable data that paralyzes or even compromises your ability to make forward-looking decisions. That hazard can evidence itself in loan portfolio analytics management, because of the wealth of data available and the sheer number of opportunities presented in the burgeoning real estate, auto and consumer lending spheres, to name a few.
A movement is afoot for businesses to create a “data czar” position that places oversight for all collection and analysis into the hands of one person, to ensure organizational focus on a specific (and short) list of objectives. On the other hand, many believe strongly in the cross-departmental model, placing their trust in the synergy developed through subject matter experts empowered to mine data to test their occasionally tangential hypotheses.
Either way you approach this effort from a personnel point of view, keep these three tips in mind to avoid being overwhelmed by big data in your loan portfolio analytics:
1-Start with small data. If your analysis tools are nascent or underdeveloped, biting off more than you can chew will be problematic from the standpoint of results and perception. To inspire confidence within your development team and encourage a deeper financial commitment by the people who hold the purse strings at your credit union, focus on small, achievable projects — ideally, projects that are components of a larger goal you can work toward incrementally or a scalable aim. However, a targeted one-off project can also prove effective.
2-Define objectives, but refrain from predefining answers. Because the possibilities are limitless, but resources and time are precious commodities, identify and isolate areas where big data analysis almost certainly will serve a clear business purpose. To select a project, read case studies of other organizations’ projects that have efficiently yielded acute analysis and tangible change and either mimic them or generate a similar concept more germane to your audience or cause. Set a reasonable timeline for checkpoints and completion, and agree to a reasonable budget that allows flexibility to branch out should initial results lead in an unexpected direction. In that respect, it’s in your best interest on both fronts to adhere to the underpromise / overdeliver mantra, because you must enter big data analysis projects with an open mind and allow the results to shape your mindset, rather than vice versa.
3-Focus on generating actionable results. Your goal isn’t to distill a monumental set of numbers into a smaller but likewise mundane grouping of numbers from which you can draw any number of conclusions. Instead, use your analytical mind to evaluate data findings; determine patterns and make observations that present clear courses of action — or at least crystallize a limited number of options, which leads to more informed decision making.