The Best Way to Build Data & Analytics Capabilities

Analytics & Insights

Author: Jacob Kirschner


To build your data and analytics capability, start by addressing problems with tangible business outcomes. It’s the surest strategy to gain buy-in and create sustainable success through results.

Too often we see companies start by investing in new high-powered software tools and data-savvy employees with the hope that value will spring forth. Not to say this never produces value, but it’s a costly gamble. Why? Data is expensive. Capturing it, storing it, and making sense of it requires ongoing investments in people and technology. Without a clear purpose, data architecture, AI, and the like will drain more dollars than they create.

Alternatively, we recommend rallying the data and analytics resources at hand to solve a problem that impacts your top or bottom line. Then move on to the next problem and continue to build data, technology, and process capabilities along the way. This way analytics is growing for a purpose. You must still invest in the nuts and bolts that make it all possible – data governance, backend architecture, processing software – but you can be more certain the investment will pay off.

Success Builds Momentum: A Real-world Use Case

Let’s say a mid-sized bank wants to make it easier for their customers to do business with them – a major driver of customer loyalty. Lagging customer retention rates and pointed feedback suggest the bank is struggling in this area.

This is a problem with a direct impact on revenue. It’s also an analytics problem. Measuring the Customer Effort Score (CES) and the drivers behind it requires solid backend data architecture, data governance controls, security, and the right technology.

A chief technology officer investing in data architecture with the explicit purpose of deriving a CES will get more traction than her counterpart looking to update data architecture for a hypothetical future purpose. While the result is the same – better data architecture – without a clear purpose, the investment is a more difficult sell.

This same bank also needs to know how its customers feel about their business. How likely are they to promote the bank to their friends, family, and social media followers? This metric – Net Promoter Score – is a sure driver of business growth. An analytics leader that works to unpack this metric is contributing directly to top line revenue. With the foundation in place to measure the CES, the analytics group will have a much easier time adding the new metric.

As successes follow one another, the organization grows organically. Leaders across the business see a tangible benefit from their data and seek more solutions from analytics. The culture shifts as profit grows.


Better Collaboration = Better Solutions

This outcomes-based, problem-solving approach requires cross-functional collaboration. Instead of the “build it and they will come” approach favored by some IT departments, growth through problem solving is, “come on over and let’s build it together.” Analytics – though highly technical – works when it is a function of the business and IT together and not the sole purview of IT. If the only interaction between a business and its analytics group is an intake request form for a new management report, data is not driving much value.

By seeking out and solving problems, analytics leadership is engaging with business leaders on a more meaningful level. Conversations and workshops between leadership and the analytics team build understanding and help create confidence in the data, the processes, and the people. Business leaders who trust the data are more likely to use it in making key decisions and research shows that leads to better decisions.


Jacob Kirschner is a senior consultant at RevGen Partners.  He is passionate about helping clients solve problems through data and analytics.




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