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What Is Analytics Maturity and Why Does It Matter? (Part I)

In a world where data’s importance is no longer questioned, organizations must reflect on their analytics competency and create plans to improve it.

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In a recent webinar, Srikanth Nayani, 7-Eleven’s senior director of business analysis, discussed the challenge organizations face to remain data-driven during the COVID-19 pandemic. In times of uncertainty, emotions can run high. Panic threatens to overshadow objectivity, and knee-jerk reactions poison data-driven decision-making.
While organizations know making data-driven decisions is worthwhile, many don’t understand where they’re at in their analytics journey or how to take the next step.
After all, how does an organization know when they’ve reached analytics maturity? Should every business decision be based on analytic insights? What about 70% of decisions? And what kind of insights do organizations need for the best results, e.g., descriptive (what happened?), diagnostic (why did it happen?), predictive (what is likely to happen?), or prescriptive (what should we do?)
For most, analytics maturity is nebulous. A recent HBR article provides an excellent starting point to understand where you’re at in your analytics maturity. In this blog, I further break down the basics of analytics maturity: what it is, why it matters, and how you can take the next step in your journey.

What Is Analytics Maturity?

Analytics maturity is the measure of an organization’s analytics competency. It’s a helpful benchmark for organizations to understand their usage of data, both historically and presently, and can help organizations shape their future plans for leveraging data. It can also help organizations quantify the ROI of analytics investments and optimize for future success..
To quantify analytics maturity, the International Institute of Analytics (IIA) has adopted the framework first proposed by Thomas Davenport in his seminal book “Competing on Analytics.” There are five stages of analytics maturity, as illustrated below. On average, companies score a 2.2 — almost right in the middle. Coincidentally, according to a recent IDC survey, only half of all business decisions are based on analytics.

Stages of Analytics Maturity
The five stages of analytics maturity.

Analytics Distribution
The average score varies by industry, with Digital Native as the highest and Healthcare Provider as the lowest.

Why Analytics Maturity Matters

The previously-mentioned global IDC study of 1,500 business leaders revealed that 73% of organizations plan to invest more in analytics than any other kind of software over the next 18 months. Organizations know data analytics is essential. However, discrepancies arise regarding what to invest in and how to become more analytically mature.
“What’s really helpful [about the analytics maturity assessment] is it is a framework you can use for the business but also a framework to be applied at the departmental level, and it's also very good for managers in the business to articulate where they are and where they need to go relative to their peers.”
— Brian Millrine, CIO & Strategy Director, Brookson Group
McKinsey reports that organizations that are very analytically mature experience 15%-25% growth in earnings before interest, taxes, depreciation, and amortization (EBITDA). Data analytics is simply an arena that organizations must immediately enter with a calculated plan.

Why Analytics Maturity is important


The IIA has also correlated analytics maturity with company performance. If you look at the graph, which compares 3, 5, and 10-year revenue and 5- and 10-year operating income, you’ll notice quite an increase in company performance by maturity level.


The Four Dimensions of Analytics Maturity

Analytics maturity is comprised of four distinct areas that I briefly cover below. Businesses more advanced in each of these four dimensions will see higher levels of ROI.

AMA Score
4 Dimensions of Analytic Maturity

Data Maturity

Without access to the right quality of data, an analytics strategy won’t get far. Data is the raw foundation for all analytics, the fuel for every report, dashboard, and machine learning model. An organization’s data maturity will depend on where and how it stores its data, the quality of that data, and how accessible it is.

Organizational Dynamics

This encompasses strategy and culture. Effective organizations know where they’re going and why. They have strategic investments, talent, and processes to support their data analytics plans.

Analytic Team Dynamics

Team dynamics can be internal and external. This covers how well analytic teams work with each other (think analysts and data scientists) and how well they work with key stakeholders across the organization

Usage and Technology

This spans the set of tools, techniques, architectures, methods, and practices that connect the analytic teams to the rest of the organization for the analytic team to realize its strategy.

Take the Next Step in Your Journey

In part 2 of this blog series, I’ll discuss how to improve your analytics maturity. However, the very first step in advancing your analytics maturity is understanding where you’re at. Here’s an interactive analytics maturity assessment that takes only ten minutes to complete. It will score your company and assign one of five levels of maturity. The tool will then prescribe resources to help you progress in your analytics journey.
Success won’t happen overnight, but once you understand where you’re at and commit to change, you can only improve. Altering your organization’s ways of thinking and operating won’t be easy, but it will be more than worth it.
Start your journey with an analytics maturity assessment.

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