Using a Data Culture Maturity Model to Create Real Business Value

By Nick Duce

Published on January 23, 2024

Long exposure night photography of a busy cityscape with light trails from traffic and illuminated skyscrapers.

Even the most data-driven organizations can find ways to improve how, and how effectively and efficiently, data is used to reach corporate goals. It’s an accepted fact that businesses run on data. Those who can effectively wield data have significant advantages over those who can’t, resulting in better decisions, increased efficiency, and better financial performance.

Yet, even with decades upon decades of experience finding new ways to collect and analyze data, most organizations would admit that how they utilize data as an organization — their data culture maturity — is lacking. That’s unfortunate, especially since it’s been proven that organizations with a robust data culture boast higher revenues, better customer service, increased profitability, and more. 

So, how can you begin to improve your organization’s data culture maturity? 

What gets measured gets managed

Ask any good consultant, and they’ll tell you that if you can’t measure it, you can’t manage it. Whether or not pioneering management consultant Peter Drucker uttered those words, Alation has worked with hundreds of enterprises across the globe and industry sectors to understand which metrics matter most when it comes to data culture maturity. It’s all captured in our white paper, “The Alation Data Culture Maturity Model,” but let’s focus on the metrics here.

We’ve found that the data culture maturity metrics that matter for you are likely different from those that matter for other enterprises, even your closest competitors. What’s clear is that your data culture must be aligned with the needs of your business, such as corporate goals and the processes and mechanisms that drive your business forward.

To help you determine which data culture maturity metrics will impact your business most, we’ve developed four key tenets of data culture maturity:

  1. Data Leadership

  2. Data Search & Discovery

  3. Data Literacy 

  4. Data Governance

Progress, acumen, and effectiveness across these four tenets can be measured in tangible, quantitative ways. Yet, the tenets allow flexibility to cater to your organization’s goals so that what matters most to you is what’s measured.

Measurement basics 

We’ve made the point that being data-driven is valuable, but it can’t be said enough. So, here it is again from another source: According to McKinsey, data-driven organizations are 23 times more likely to acquire new customers, nine times more likely to have higher customer loyalty, and 19 times more likely to earn above-average profits. 

Before we get too far into the data culture maturity metrics, let’s level-set on the types of common metrics. This will help you identify where to focus your initial efforts to determine where you are and how to get where you need to be. 

  • Leading indicators are a great place to start with measurement and can help you predict your trajectory toward your goals. An example of a leading indicator is the use or adoption of a data catalog measured in monthly active users. It shows how many workers are accessing your data catalog on a regular basis, which gives you a leading indication that a fundamental element of a data culture — your data catalog — is being used. 

  • Lagging indicators are after-the-fact measurements that demonstrate areas of the business impacted by your data culture maturity efforts. For example, measuring the impact on innovation within the business is something that could only be measured once certain criteria had been met, such as new product revenue, patent applications, and returns on R&D investments.

  • Domain–specific metrics measure what matters to the main stakeholders in the organization, from the C-suite and board of directors to middle managers. Identifying these metrics can be as simple as working with stakeholders across marketing, finance, operations, and other areas to understand their business goals and how they find, access, and use data to achieve those goals. 

  • Domain-agnostic metrics cover cross-functional initiatives that span domains and are deemed crucial by executive leadership. Consider measuring marketing lead to sales revenue time, customer lifetime value, order-to-cash time, cost per invoice, and other metrics that align with executive-level initiatives. 

Selecting and monitoring key metrics in all of these areas is vital for demonstrating the value of data culture programs. Having a clear understanding of which metrics matter and who in your organization cares most about them is a very important step. 

Quantifying data culture maturity with specific metrics

As mentioned, there are four key tenets of data culture maturity, each of which have varying levels of importance to your organization. Let’s look at each tenet and uncover some examples of common data culture maturity metrics to help you understand how you can measure and improve your organization’s data culture maturity.

Data leadership metrics

Workers tend to do what their managers and executives say, and rightfully so. Getting those leaders on board with your data culture maturity efforts is, therefore, imperative to its success. But how can you measure leadership?

Leadership can be seen as empowering workers to be better data consumers and derive more value from data. Sure, macro metrics like revenue and profitability are leadership-dependent, but are also a bit too dependent on many other factors. Getting granular can help. For example, if cost reductions are a corporate goal and the goals of data culture maturity are increasing efficiency and cutting costs, maybe productivity gains can be tied to data leadership. Or, business speed can be connected, even qualitatively, to better informed and faster decision making, as well as increased innovation.

Consider Texas Mutual Insurance Company, which wanted to improve decision making and help its business move faster. But too much data in too many systems slowed its processes. So, it migrated its on-premises data to the cloud and deployed workshops and programs to drive worker adoption. It also used Alation to create a one-stop shop for executives to access reliable data to make in-the-moment decisions about how best to deploy capital. Creating data-driven dashboards for constantly changing initiatives was imperative to helping executives make better, faster decisions, and Alation helped them measure and reduce by 80% its key metric of delivery time for those critical dashboards.

Data search & discovery metrics

Workers need to find data before they can put it to use, obviously. But finding data is an ad hoc adventure in most organizations. Using a data catalog is a fast track to easier search and discovery, and provides additional context and connections to ensure workers end up using the right data.

When measuring data culture maturity with respect to data search and discovery, important metrics include data search time saved, comprehension time saved, and time saved by using published queries versus custom queries. 

Drilling down on comprehension time saved, this metric tracks the time needed for an analyst to find and understand data. Improvements generally translate into increased analyst productivity and reflect the value of self-serve analytics and data democratization. It also builds data literacy throughout the business, since workers who comprehend the data will be more confident when using the data.

GoDaddy, the venerable Internet domain registry turned entrepreneurial services platform, used Alation Data Catalog to provide self-service analytics. Using comprehension time saved, reduction in impediments to data use, and other metrics to track progress, it saved hundreds of hours annually in improved analyst productivity, reduced data quality issues by more than half, and cut impediments to data usage by one-third.

Data literacy metrics

Data literacy is how well workers understand data and the resulting analysis. Organizations with high levels of data literacy see non-technical workers doing more self-service analytics, which removes the bottleneck of requiring specialists or data scientists to run most analyses or provide continuous support. That’s highly inefficient, especially for those in-demand and highly-compensated data scientists. 

Improving data literacy requires infrastructure, education, and training. But where? Gaps in data literacy can be measured by the percentage of workers accessing data, percentage who have completed training, percentage contributing to the data catalog, and more. Data catalog adoption is also a key leading indicator of data literacy, and depends on change management, data leadership (see above!), and more. 

Data governance metrics

Data governance comprises the frameworks, rules, policies, and responsibilities that define how data is used in your organization. It also underpins the other pillars to support and contribute to data leadership, search & discovery, and literacy, and can reduce risk and lessen the likelihood of audit issues and resulting regulatory impacts. 

Data culture maturity in governance efforts can be measured across assets, usage, errors, infrastructure, process progress, and more. Specific metrics include the share of critical data assets curated, data error percentages, share of data assets with conforming metadata, percentage of workflow requests approved, and percentage of data policies reviewed and approved. 

Take assets curated, for example. Large enterprises might have thousands of data assets, many of which are outdated, infrequently used, or low priority. Focusing on the most used, most sensitive, those containing personally identifiable information (PII), or other important attributes can help you determine which data assets are critical. Then, tracking the percentage of those critical assets curated reflects data governance progress where it matters most. 

A European supermarket chain focused on data governance metrics to curate over 800 data assets in its Alation Data Catalog, saving more than $1.5 million in data search. It saved an additional $3 million in data literacy and comprehension from easy access to better data documentation housed in its data catalog. 

Driving value with a data culture maturity model

Businesses exist to create value. Connecting a healthy, mature data culture to business value creation ensures data supports what drives your business and that investments of time and resources into improving data culture maturity will garner executive champions.

Ultimately, a more mature data culture means workers are using the right data to drive more value for your organization. The examples above merely scratch the surface of how data culture maturity can be measured and tracked. The goal is to show improvements where it matters most for your organization.

It’s also helpful to benchmark your organization’s maturity against peers, industry leaders, and other organizations to find specific areas of focus and improvement. To help, download our white paper, “Whitepaper: How Can Organizations Measure the Value of Data Culture?. In it, you’ll find case studies from organizations pushing data culture maturity, guidance on creating a data culture maturity program, and a detailed data culture maturity model showing the five stages of maturity across 20 data culture topics.

Where it all begins, however, is by quantifying your data culture through metrics. That’s easiest with a data catalog or a data intelligence platform, and Alation provides a scorecard for your data program to track and manage progress, see live metrics, and support better, faster data culture decisions.

  • What gets measured gets managed
  • Measurement basics 
  • Quantifying data culture maturity with specific metrics
  • Driving value with a data culture maturity model
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