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The Four Levels of Analytics Maturity

We outline our four-step model to categorize how successfully a company uses analytics by its ability to show the analytics, uncover underlying trends, and take action based on them.

By Kevin Smith, Head of Product Marketing at GoodData

Analytics Maturity Curve

We’re in a rapidly transformative age where analytics are concerned. As McKinsey recently stated, “For leading and lagging companies alike, the emergence of data analytics as an omnipresent reality of modern organizational life means that a healthy data culture is becoming increasingly important.” To that end, we’re seeing more companies starting to introduce analytics in hopes of capitalizing on some of the value at stake. There’s just one problem: I’m seeing companies implementing analytics for analytics’ sake and not actually doing anything with the resulting insights.

I’ve seen this trend even in my own career. Years ago, I worked at a company that had recently started “using analytics.” I remember sitting in an eight-hour meeting where an executive asked all of the organization heads to come in and present their performance. Those presentations inevitably involved someone walking through a 100-page deck of visualizations showing surface-level trends with no attempt to dive into the “so what” behind the data.

This got me thinking. Can we categorize how successfully a company uses analytics by its ability to show the analytics, uncover underlying trends, and take action based on them? This four-step model is what I’ve begun using to figure out where a company is on the “analytics maturity scale.”

Level 1: Analytics visualization with little or no action

That meeting I talked about above is what I call “Level 1” on the analytics maturity scale. Basically, an employee looks at an analytics visualization and maybe uses it for a presentation, but they don’t do much with it in terms of action to improve the business. At this point, the visualization’s only purpose is to serve as a pretty picture that an employee can display. There’s no drill down into the data, and there’s no analysis of root causes. Many companies and employees are currently in this stage, because it’s easier to do or the information presented isn’t actionable and it isn’t clear to the employee what they should do with the information. At Level 1, they just need visualizations to paint a picture of the current state of affairs in the business. For higher maturity levels, they need to take concrete action—who they manage, how they manage, and how business in done.

Level 2: Analytics for personal performance and metrics

In Level 2, an employee looks at an analytics visualization, but he/she only cares about the output that will help with personal metrics or performance. Maybe a salesperson has a certain quota for the month. If he or she closes this next deal, will that quota be met? They’re using analytics and taking action based on it, but they’re only using it to track their own performance and output. This is a step above Level 1 as they’re using the analytics to drive action, but it doesn’t look at company objectives outside of their role and how they can help meet them or create long-lasting change.

Level 3: Analytics for organizational performance

In Level 3, an employee performs analysis as part of his or her job but is focused more on individual organization performance instead of the business as a whole. Because they’re looking only at their own business unit or group, it’s possible to get into a situation where they’re optimizing for their organization but may be hindering performance for the business overall. Similar to Level 2, there’s action, but is there really a widespread positive impact for the entire corporation? At best, the organization in question is able to improve its performance in pockets, while the more widespread improvements go unrealized.

Level 4: Analytics to improve business performance

Level 4 is what we should all be striving for. Here, employees perform regular analysis and look at analytics in relation to the performance of the business as a whole. They’re looking at their own responsibilities, their own organizations, then reaching left and reaching right to work with other organizations within their company. Someone working in the marketing department uses analytics to look at their own performance, but they also look at what’s going on in the product marketing, product management, and demand generation teams. If marketing materials are produced, is demand generation using it? If demand generation has certain targets, are the marketing teams doing anything to support it? With analytics, they’re able to understand the impact of their own work and see how their organization fits with other organizations to drive the success of the whole.

Every company using analytics is represented in one of these four levels I’ve just outlined. For those companies in Levels 1-3, begin taking action using analytics to drive improvement across the business. However, knowing what you need to do and actually doing it are two different things. At the end of the day, the cultural shift required to change the way people have been operating for years is an enormous undertaking. At the company I referenced at the beginning of this article, it took a couple of years to get the team operating at a higher plane of analytic existence.

For those companies looking to successfully make this cultural shift, this effort has to be driven from the top. Business-wide analytics is ultimately about improving business performance from the customer's point of view, a perspective that is hard to maintain if you’re a front-line employee sometimes without visibility into the corporate strategy. Instead, senior-level employees are the ones who have a view into those disparate silos in the business. If companies want to make the leap from a lower level to a higher level of analytics maturity, then they need to do the legwork and ensure a cultural transition takes hold. If not,  they’ll find themselves stagnating at a lower level on the curve.

Bio: Kevin Smith develops strategies for monetizing data and delivering analytics-based products. For analytic platform vendors, Kevin has developed product marketing/go-to-market strategies that help them understand and reach the ISV market. For companies building analytic apps, he has designed complete programs to define key personas, to design the product structure, to set pricing, and to successfully test and launch the product.



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