Reasons Your Data Journey Has Stalled

The prospect of using data to make better business decisions can be very exciting. Intuitively, we know that our decisions are better supported by as much information we can possibly gain in the decision-making process. This enthusiasm, coupled with Big Data hype, has enticed many credit union leaders to begin a data journey in their own organizations, investing in software and man-power in hopes of gaining access to intelligence dashboards and decision support analytics. Unfortunately, many of these projects have failed, at least in attaining their initial promise, leaving these same business leaders disappointed with the results and accountable for the investments made. It is possible that you are in the same position today, either considering your first step on your data journey or struggling to get a project to the finish line. This article identifies challenges that organizations encounter when attempting to complete a data project.
No Data Strategy
Alan Lakein, popular self-help writer in the 70’s, is credited with declaring, “Failing to plan is planning to fail.” While the phrase makes practical sense to us, it is all too often ignored in the data realm. An organization’s data strategy should provide an enterprise assessment of its current state, where the organization wants to be in the future, who will be involved in the process and how data will be used in decision-making across the organization.

All-too-often, data projects are started in fits and starts when an individual with an inclination to follow the data trail starts a project on their own or acquires Business Intelligence software that promises to solve a specific challenge. What this individual soon discovers is that the data requirements to complete an implementation of BI software, or attain the goal they set out to reach, often requires data acquisition outside their realm of data governance. They also encounter a headwind when trying to explain data-driven decisions in an organization that has not fully adopted data-driven decision strategies.
You can discover more about creating a data strategy by taking this CUBI.Academy course, Creating a Data Strategy.
No Leadership Support
The difference between the have’s and the have-not’s when it comes to data-driven decisions, in credit unions, is the level of support from organizational leaders. Don’t mistake encouragement with support. Organizational leaders will often encourage business users’ pursuit of good data to support business decisions, but without their commitment to allow the organization’s strategies to be guided by Business Intelligence, those pursuits will, likely, be in vain. If the organization’s highest-level strategies are not driven by the same data that business users’ decisions are, the organization will often find itself fighting against itself trying to align strategic objectives with operational tactics.

In my experience working with credit unions, the size of the credit union and its available resources do not have as much impact on data journey success as does the commitment from the organizations leadership team. Without that leadership, larger organizations can find themselves less successful as they encounter territorial walls that cannot be broken down, even with a collaborative spirit. These walls must be destroyed by leadership. In fact, I’ve found the greatest success with smaller organizations where the leadership has been invested in the data journey. When leadership is invested in the journey, decision-makers and business users are able to tap into higher levels of creativity and are enabled to work together for success.
Assumptions of Expertise
Just because someone is a superior analyst, lender or marketer, doesn’t necessarily make them a Data Scientist. Many credit union organizations rely on their departmental leaders to acquire data and interpret the outputs of analysis independently. The issue here is that it takes more than a knowledge of the business discipline and an inclination for analysis to properly form Business Intelligence. Data Scientists are trained specifically for decision support and have the technology, mathematical and domain expertise to provide business users with the level of information required for decision-making.

There are literally hundreds of Business Intelligence and Analytics products sold as Software-as-a-Service (Saas) with the promise of quickly and accurately solving your decision-making debacle. Truth be told, the business model of these providers is to primarily focus on software delivery, not provide training or expertise to the client. For this reason, many credit unions struggle with the implementation of these products and, finally, gaining insight from the products’ outputs. Often these systems simply become reporting systems that lack the flexibility one would have if they built their own reports in Excel®. As a previous software product manager, myself, I confess to often feeling as if I was creating new problems for my credit union clients rather than solving an existing challenge.

Credit Unions must consider investing in Data Scientists to get to the destination of their data journey destination. It might not be required that the credit union invest in a full-time employee at the onset, but at the very least, investing in fractional Data Science resources can help the credit union get to their destination much faster, and more accurately, than trying to do it all with existing internal resources.

I recently worked with a credit union that had purchased pricey software to accomplish at least one task that was required of them by regulators. Besides the price invested, the credit union had also invested in hiring a data analyst to take on ownership of data projects within the credit union. But, after months of implementation, the credit union had not reached their destination and contracted with CUBI.Pro to complete one of the tasks quickly. In the end, one of the organization’s primary goals was completed by simply contracting out the work, instead of doing the work themselves. In the future, the credit union will be able to achieve what it wants to do internally, once their software is completely implemented and internal staff are all brought up to speed, but in the meantime, they have benefitted from bringing in temporary expertise to fill the gap.

Think about it this way, you want to invest in internal learning when that learning will support an ongoing business process. You don’t want to invest in internal learning for projects that pop up from time-to-time or are outside of the resident expertise. You wouldn’t take your branch employees off of the front line to paint the branch lobby. You would contract that work out to a professional painter. Why? Because a professional painter can typically complete the project faster, already possesses the proper tools, and the results will be much more impressive. If you are going to establish a new risk-based pricing scheme in your credit union, you may not want to invest in the learning required to do this internally as it is not something that is done on a regular basis. Instead you may wish to contract this project out to a Data Scientist.