Best Practices: Getting the Most of an Investment in Data Analytics
November 21, 2017
Amazon. Google. Netflix. We know these brands as the head honchos of data management and data analytics. In many ways, they’ve written the handbook for a game in which both the rules and the playing field change in the blink of an eye. If keeping up with that rapid evolution is can seem daunting, the good news is that data collection and analytics continues to become more accessible in a way that credit unions can utilize and maximize.
Ari Libarikian, a senior partner at global management consulting firm McKinsey & Company, has written at length about how a solid data strategy can support a variety of business goals including driving growth, improving member and stakeholder experience, reducing costs, managing risks, and engaging employees, to name a few. It all sounds good on paper: collect data, mine data, leverage data. In reality, we know it takes time, resources and commitment in order for a data strategy to have a positive impact on a credit union’s the bottom line.
What, then, are best practices to keep in mind when it comes to data analytics for credit unions?
- Leadership Buy-In. It’s tough to name any business strategy or initiative that survives and thrives without the support of, and buy-in from, key leadership. Data analytics is no exception. According to Libarikian, it’s important that credit union leaders not only agree on a data strategy as a priority, but as an important tool to solve existing organizational challenges and problems.
- Democratization of Data. Problem solving rarely happens in silos. The more people that are able to access, evaluate and use available data, the more valuable it becomes. Libarikian emphasizes that, “The people responsible for the quality of the data and the architecture and the governance and the strategy need to be cross-functional teams, not just IT teams.”
- Hiring the Right Talent. Whether internally or through external vendors and partners, having the right talent in place is crucial. Data can’t be leveraged to the best business end if it is not examined and translated by qualified team members through the lens of a specific credit union’s unique circumstances, members and goals.
- An Agile and Responsive Culture. Ignoring or undervaluing what is unearthed by the data is one of the fastest ways to squander an investment. As important as the buy-in of credit union leadership, making data more widely accessible, and having the right data management talent on board is the ability for organizations to see what is before them with fresh eyes and an open mind. Doing so might mean change is afoot, and we all know change can be scary. By definition, Libarikian says, advanced analytics means we’re “going to fail a few times and that’s okay.” But, he points out, more important is the willingness to get back up and keep moving forward–which is only possible in a culture that is purposefully agile and responsive.
Keeping in mind these best practices can help credit unions make the most of a data analytics strategy, from the big picture down to the daily details. For more tips and tools to help credit unions get the most from their data investment, read our whitepaper: Predictive Analytics for Credit Unions.