In our earlier posts, Part 1 and Part 2, we shared credit union trends revealed in the bi-annual survey conducted earlier this year by Information Builders’ partners Best Innovation Group (BIG) and OnApproach. Chief among the results was the lagging pace of analytics – the use of data to understand and better serve the needs of our members. At the time of that late 2017 survey, credit unions indicated that analytics investment was slated to take place in the next three to five years (55 percent). This left room for fintech and bank competitors to widen the competitive disparity in growth and retention, loan volume, operational efficiency and member satisfaction.
Almost one year later, looking at the second-half 2018 survey, we find that a near-majority (42 percent) have accelerated their plans for data strategy, now two years away. Perhaps this is in recognition of the growing competitive gap: 70 percent of the surveyed institutions surveyed agree that banking and fintech competition are at least one year ahead in their development of data analytics strategy, and implementation. More than 25 percent of those surveyed believe that the competition is three or more years ahead.
It has been our observation however, that this acceleration sometimes takes place at a sacrifice, as the survey data now reveals that more than half (55 percent) of credit unions have yet to formulate a data strategy for their organization – up from 45 percent in December 2017. These organizations do not have a data strategy or roadmap to guide their implementation of analytics.
In our view, implementing analytics and tools without a strategy is a recipe for disappointment and muted return on investment. The needs of all departments -- finance, marketing, operations, lending, IT, etc. – should be examined, and a sequence of implementation be developed to reflect the prioritized needs of the credit union as a whole. For example, product and member profitability for Finance first, member growth and retention for Marketing second, loan volume growth for Lending third, ‘Next Best Conversation’ for Retail fourth, and so on. This example suggests sequential implementation although, with additional focus, parallel delivery is always an option.
In addition, the integration of systems required to implement these departmental priorities produce even greater insight and analytical value. For example, integrating core and G/L derive member and product profitability insight, unachievable with core data, alone. Adding operations and marketing data to that mix provides the platform for ‘Next Best Conversation’ and member segmentation, service and channel propensity, as so forth.
It quickly becomes clear that a single enterprise analytics platform is required to unite data and analytics needs across all departments. This does not preclude the use of ‘point solution’ siloes in this effort (each serving one department or subject area, only), but a single, integration platform is required to bring all credit union data to bear. If the capabilities of the solution or platform don’t support the analytics needs of the credit union as a whole, implementation grows increasingly complex and expensive, with uncompetitive and unsatisfactory results.
Interested in learning more? You can find the full results from the latest BIG and OnApproach survey here and information about our analytics solution that’s tailored to quickly meet the needs of credit unions here.