by: Peter Keers
Credit unions seeking to improve their Big Data/Analytics capabilities face a classic choice: build (DIY) or buy?
The “build (DIY)” option appears much more attractive. Typically, a new position within the credit union is created to handle the new Big Data/Analytics initiative. Whether it is through a promotion or external hire, the cost seems much less and more controllable. Another perceived advantage is the effort will be tailored to the exact needs of the credit union. There will be no need to deal with a “one size fits all” vendor solution.
As these “build (DIY)” scenarios unfold, the best case situations result in improved reporting and analysis. Having an individual dedicated to building new reports and running ad hoc queries is a welcome improvement over the old way of doing things.
Yet, in many cases there is a dark side to this happy state of affairs. A “black box” is quietly forming behind the scenes.
Like many home grown solutions, there are strong temptations to skimp on documentation, employ unstable and informal processes, and rely on less than adequate tools (e.g. – MS Excel) to get the job done. The credit union’s Big Data/Analytics program is at risk because a lot of the important information about how it operates is dependent on one person. And with the current boom in Big Data and Analytics, that one person’s skills are in high demand in the labor market.
When that person leaves for another opportunity, the credit union’s promising Big Data/Analytics program is transformed into a nearly unusable black box.
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