To Build a Great Analytics Team, First Build the Culture

To Build a Great Analytics Team, First Build the Culture

To Build a Great Analytics Team, First Build the Culture

Data analysts today are working in a red-hot field. Their expertise is in high demand, and there is no shortage of “dream” companies looking for their expertise.

Credit unions will need to fight hard to attract these in-demand professionals against big banks and fintech companies. And, it won’t be easy given that financial services is a legacy field. Many credit unions recognize the need to be transformed, but they may not yet have made the cultural transformation that must accompany it, in order to attract and keep top-flight data analytics talent.

What does an effective analytics culture look like? An organization should focus on building four key attributes:

1. Shared Vision

When everyone across the organization understands how data can lead to exceptional customer experiences, they are much more likely to keep contributing. Great leaders know that sharing the outcomes is a big part of this. There can be no shared vision – at least not sustainably shared – without transparency.

A great analytics culture uses top-down encouragement to announce and applaud the wins, but also to be honest about the losses. Analytics is still a new and developing field, and we learn by making mistakes. Even with the best hires and the most sophisticated technologies, there will be failures. But, that’s O.K. After all, rapid prototyping, one of the most essential methodologies to achieving digital transformation, centers on failing fast. The more accepted (and perhaps even celebrated) these moments are, the closer an organization gets to realizing its vision.

2. Collaboration Between Departments

“Collaboration between departments is critical, especially as more companies incorporate data blending strategies, which combines data from multiple sources into one solid, functioning dataset,” said Matt Maguire, Chief Data Officer for CO-OP. “To get the greatest view of an individual customer or segment, companies need data from a myriad of viewpoints. Within the context of credit unions, that means lending works with operations, card teams work with deposits, and compliance…well, they work with everybody.”

Transforming organizations often find adding a data person into every department can be helpful. This “horizontal embeddedness” can lead to a much quicker adoption of that shared vision, as the embedded data leaders also serve as champions for the cause.

3. Data Belongs to the Entire Organization

This is how organizations secure buy-in from all the teams expected to deliver data and insights for the greater good – and from all the people who are not data scientists but will be critically important to the firm’s data effort. When staff are asked to gather and push data to some mysterious lab never to be seen or heard from again, they are much less likely to engage in the effort.

In much the same way a company communicates the ROI of its products and services to its customers, an organization with a great analytics culture knows how to show team members what’s in it for them. CarMax holds an internal open house every two weeks to nurture its culture, which is centered on using data to move projects forward. Demonstrating ROI internally comes from freely sharing the resulting insights with all departments quickly (in real time, when possible). Smart companies make the intelligence that comes from everyone’s data available to everyone, and they encourage all decision makers to employ analytics methodologies to the choices before them.

This nurturing of an inclusive approach is essential within a transforming culture because it helps overcome something called “cognitive inertia.” It’s a problem that University of Virginia Darden School of Business researchers found plagues legacy firms that enjoyed success long before data analytics. “Managers will have to let go of decision-making processes that have worked for them in the past and learn how to use data analytics instead,” the professors advise.

4. Enterprise-wide Understanding of Goals

Here again, this speaks to buy-in. Why are we doing this? How does this help me meet my objectives? What does this do for our customers? How is this making my job easier or more meaningful? These are the questions that need clear answers before staff can become fully engaged in an analytics effort. What’s more, they need to be communicated regularly to keep everyone pushing toward the same goal.

“Today’s consumers aren’t comparing businesses to their direct competitors alone,” said Maguire. “They’re benchmarking the experiences they have with every brand against those they have with Google, Amazon, Facebook and Apple. Those experiences are crucially tied to data analytics competency. Developing an analytics culture will be critical to deepening relationships with these consumers as they are exploring new, seamless, even invisible ways, to get their financial jobs done.”

To build an effective analytics culture, you’ll need the right tools and information. Join us at one of the CO-OP Roadshows coming to a city near you to learn hands-on data strategies you can begin implementing immediately. See the full schedule: