How to Improve your Data Maturity

Common data maturity issues keep your company from putting its data to work.

Data Maturity looks at the degree to which an organisation uses—or is able to use—the data that it produces.

A company that’s able to leverage all or most of its data to advance its business goals is considered data mature. By contrast, a company that fails to put its data to work is considered data immature. Simple, right?

But how do data mature organisations actually manage to put their data to work?

There are a few key characteristics shared by just about every data mature organisation: 

  • They manage data as an enterprise asset and have data strategies that are closely tied to their business objectives
  • Their data handling personnel have clearly delineated roles and responsibilities and understand the importance of great data to the business
  • Their Data Producers/Data Creators are fully aware of the need to provide high-quality data to their Data Customers/Data Consumers
  • They have well-defined Critical Data Elements (CDEs) and good interdepartmental communication; and last but not least, they work with high-quality, dependable data.

Ok, but how do you measure Data Maturity, and more importantly, why should you bother measuring it in the first place?

Source: Gartner

A Data Maturity assessment works something like a company audit—evaluating the effectiveness of your organisation’s data policies, processes, and personnel. An especially thorough assessment can even highlight weaknesses in the minutiae of your Data Management and handling practices, helping you zero-in areas ready for improvement.

And of course, improving your Data Maturity is just plain good for business. After all, data-driven organisations are 23 times more likely to acquire customers, six times as likely to retain customers and 19 times as likely to be profitable as a result. Clearly, even if you think your organisation has a bulletproof Data Strategy, it’s always a good idea to assess the state of your Data Maturity.

So, what are some common Data Maturity issues that an assessment might uncover, and how can you solve these issues should they arise? Good questions, let’s dive in. 

Business Alignment Issues

One of the most common Data Maturity mishaps involves having either no Data Strategy at all, or a Data Strategy that’s poorly aligned with the goals of the business. A lot of organisations make the mistake of conceptualising data purely in terms of liability or costs, rather than benefits. Instead, data should be seen as a major business-driver, something that can and should be leveraged to improve the bottom line.

If your company is struggling with business alignment issues, get back on track by visualising how data can be used to advance your company’s specific business goals. Begin by pinpointing your organisation’s key stakeholders and identifying their business-critical objectives. Ask yourself how data can achieve these objectives, improve the company’s products or services, and streamline internal workflows.

Show exactly what your business needs to accomplish and demonstrate to management precisely how your data can be leveraged to achieve those goals. Maintaining this business-first mindset and incorporating it into your company’s Data Strategy will ensure that your data is actually being put to use.

Personnel Issues

Another common Data Maturity issue involves confusion around personnel. Many businesses erroneously think of data as belonging solely to the IT department. This approach fails to recognise that company data is a universal business asset—not just some esoteric technical feature—and leads to valuable data being neglected and business opportunities being lost.

For data to be effectively put to use, multiple departments need to be involved in its management. To ensure this kind of cross-functional engagement, a data mature organisation will involve subject matter experts from a variety of departments in the Data Governance process.   

In addition to ensuring company-wide engagement, a data mature organisation will have dedicated Data Officers with clearly defined roles and responsibilities. This includes drawing a distinction between Data Owners and Data Stewards.

Data Owners are senior staff from outside of IT accountable for the quality of a particular data set. Data Stewards report to Data Owners and are responsible for the day-to-day management of the data itself.

By making it crystal clear who is responsible for what and who reports to whom, this distinction promotes accountability while ensuring that data is being “digested” by the entire organisation— not just being pawned off as “an IT thing.” 

To identify appropriate Data Owners at your organization, just follow the money. Who loses the most if a particular data element or set  isn’t up to par? The person with the most on the line is typically the best choice for that data element or set’s owner. 

With cross-functional engagement and clearly defined roles and responsibilities, data is far more likely to be put to use and actually generate value for the business.

Communication Issues

A third common Data Maturity issue involves the miscommunication of essential data. This issue can arise either from ambiguity about how certain Critical Data Elements are being defined, or from an outright failure to communicate or share that data in the first place.

Either way, the problem can usually be boiled down to the absence of a single, unifying Business Glossary. A Business Glossary defines the meaning, format and uses of an organisation’s Critical Data Elements (CDEs). By contextualising and defining individual data elements, they improve business understanding, save time on searching and reports, and prevent misuse. Without them, CDEs go undefined and business-critical data gets siloed.

Without a Business Glossary to establish common terminology, any discussion can quickly turn into a data brawl. Individual departments will have their own, often incompatible interpretations of the same numbers.

For example, a sales team might think that a customer is anyone who has ever bought anything, whereas a finance team might restrict that definition to those who are actively paying invoices. Presented with the very same data, the two teams will come to radically different conclusions, and neither will be able to make a confident, data-driven decision for the business.

In many cases, without a clear Business Glossary, Critical Data Elements (CDEs) are never even defined in the first place. Instead, potentially valuable data goes neglected and business opportunities are lost.  

Clearly, Business Glossaries are essential for keeping everyone on the same page.

The best way to create an exhaustive Business Glossary is to make sure you’ve accurately identified your CDEs. One sure-fire way to do that is by mapping out your critical business processes and identifying which specific data elements are involved at every stakeholder touchpoint. Define what these data elements are, how they’re being formatted, who is managing them, and where they’re being stored. 

With your CDEs defined and a Business Glossary in hand, you’ll be in a much better position to get the most out of your data. 

Data Management Issues

Data immature organisations are likely to struggle with a number of Data Management and capability issues. To keep things simple we’ll focus on what may be the most common Data Management issue of all—poor Data Quality.

Of course, every organisation deals with Data Quality issues from time to time, but the difference between a data mature and data immature organisation is in how Data Quality issues are dealt with when they arise.

A guaranteed sign of data immaturity is when businesses patch up Data Quality errors at the point of use—late in a dirty data element’s life cycle—rather than by addressing errors at their root cause. This isn’t just sloppy Data Management, it’s also wasteful since it requires additional, non-value-adding work downstream.

The least mature firms will have business teams running reconciliations and reworking errors at or after the point of use. Slightly more data mature organizations will have IT teams patching up Data Quality errors just before they get to the end-user; better, but by no means ideal.

Truly data mature organizations will solve this issue by systematically tracing Data Quality challenges back to their root causes and fixing them there, at their points of origin. 

The rule of ten says that a dollar spent on prevention saves $10 on cleaning the issue and $100 on remediating it if something goes wrong. It may seem like a simple solution, and that’s because it is. The ability to recognise and implement simple, clear-sighted and cost-effective solutions is a hallmark of Data Maturity.

How Data Mature is Your Organisation?

Now that you’ve learned how to recognise and solve some of the most common Data Maturity issues, why not see how your own organisation stacks up with Cognopia’s free Data Maturity assessment

Still have questions? Want to learn more about Data Quality, Data Maturity, or the bigger picture of Data Governance? Not sure if your company is even ready for Data Governance in the first place? Cognopia is here to help.

With a range of bespoke consulting services, Cognopia’s team of Data Governance experts will help you craft a bulletproof Data Strategy unique to your organisation’s needs.

Schedule a chat here and find out what Cognopia can do for you.