Maturing in Data is Like Fine Wine — Here’s Why

Maturing in Data is Like Fine Wine — Here’s Why

In today's data-driven world, organizations are constantly striving to leverage data to gain a competitive edge and drive meaningful insights. So, what does it mean to be mature in data, data science, or AI?

As a Chief Data Officer (CDO), you may have been asked about your contribution and value. Questions like, “When are we done with data?” “When are we good enough? Mature enough?” “How do we measure success and are we using AI to its full potential?” are all good questions about your data maturity and deserve careful consideration.

Can maturity assessments provide the answers?

Achieving data or AI maturity is not an overnight process; it requires time, effort, and a strategic approach. Much like the aging process of fine wine, maturity is a journey that improves with time, experience, and careful cultivation.

Data maturity assessments provide some answers. One might question the relevance of maturity assessments in the ever-evolving landscape of data analytics and technology. After all, maturity is not a static state but an ongoing process. And while it is true that maturity is not a destination, but rather a continuous journey, maturity assessments still have a vital role to play.

Maturity assessments provide organizations with a benchmark to evaluate their current data capabilities, pinpoint gaps, and serve as a guiding compass toward more effective data governance. Ultimately, the intent of maturity assessments is to enable more insights that unlock greater business value.

Often the results of the assessments are not a real surprise but simply confirm the right activities or priorities in the journey. For example, being less mature and deprioritizing one area might be still the right choice, as it might provide higher benefits in other areas.

Which maturity assessment fits your organization?

It is crucial to remember that data maturity, like cultivating wines, is not a one-size-fits-all or a cookie-cutter concept. Each organization has unique needs, priorities, and challenges, and the assessment should be tailored accordingly.

For example, the Social Security Administration Analytics Center of Excellence developed its own maturity model based on its focus and needs. Their six capability attributes of culture, technology, people, analytic opportunities, analytic techniques, and data cover what they want to measure.

Although culture, people, technology, and data are traditional components of maturity assessments, maturity in AI and responsible AI have been added recently. These dimensions will evolve even further with the advent of generative AI.

Managing risk in AI will certainly be added to AI maturity. Policies will also evolve and require companies to evaluate their maturity in additional areas.

The best way to use maturity assessments is to treat them as a roadmap for growth and improvement. 

  • Be okay with changing an attribute over time as priorities shift during the journey

  • Be okay not measuring areas where you are already good, or which are intentionally down the road in your roadmap

  • Identify areas of focus and develop targeted strategies to enhance the capabilities you need in the future

  • Be careful with measuring too many attributes, it might lead to a loss of focus 

What does “good” look like?

Though “good” can be subjective with wines, it is much more concrete in the realm of data. A well-defined data strategy, aligned with its business objectives, comprehensive data governance frameworks, and robust data management practices are all ways to define “good.”

Other examples of what “good” looks like: 

  • Data is treated as a valuable asset owned proudly by business and their functions

  • Accessible, trusted, and connected data products as a self-service are offered

  • Employee experience confirms that the time it takes to get answers is much faster

  • The workforce continually adds new capabilities allowing them to use data more efficiently and innovatively

  • Data productization is based on business demands and runs like the Amazon flywheel concept 

Advancing in the right direction towards data maturity can be both challenging and rewarding. It requires a shift in mindsets, organizational alignment, and continuous learning. One indicator of progress might be the ability to capture value through data analytics and data science initiatives.

Some say that not capturing the value any longer by a data organization is a sign of maturity since the value is enabled by data but created by and for the business. What matters is the impact on the bottom and top line. Somebody once said to me, “We know when we look at data, we will find value. We need to invest in it because of that.”

"Data maturity is not a top-down initiative; it requires active participation and engagement at all levels of the organization."

Ellen Nielsen | Chief Data Officer at Chevron

Another consideration to think about is who should be involved in the data maturity assessment process. Data maturity is not a top-down initiative; it requires active participation and engagement at all levels of the organization.

Regular assessments can help uncover valuable insights, highlight areas for improvement, and foster a data-driven culture across the entire organization.

Let's go agile with maturity assessments

Organizations might wonder why they are not conducting maturity assessments. Reasons vary, often it is due to a lack of awareness about the benefits or the belief that the organization isn’t ready.

But other reasons are the amount of time and resources involved, or that there is too much burden on the organization which is focused (and rightly so) on execution.

Though maturity assessments are often known as once-a-year, or maybe an every-other-year activity, our world has changed. We have all embraced an agile way of working. Possible assessments can focus on only a few areas at a time and be conducted throughout the year – even to the point that it is integrated into your organization’s product and portfolio management.

Get a “taste tester”

Wines are frequently judged by outside sources to provide feedback and improve. An unbiased view from outside the organization can also be valuable during maturity assessments. External experts or consultants can bring fresh perspectives and objective insights, helping organizations identify blind spots and uncover hidden potential.

It's important to emphasize that data maturity assessments should not be seen as a judgment of an organization's capabilities. A lower score in a specific area does not necessarily indicate failure; it might simply reflect differing priorities or the need for further investment.

The assessment should be viewed as a tool to prioritize and measure progress rather than a measure of success or failure.

Conclusion

Maturing in data is indeed like a fine wine – it takes time, effort, and a thoughtful approach. Maturity assessments serve as valuable tools to evaluate an organization's current state, identify areas for improvement, and guide the journey towards data maturity.

While no single framework or model can fit all organizations perfectly, assessments should be customized, iterative, and involve teams across the enterprise. By embracing external perspectives and treating assessments as catalysts for growth, organizations can unlock the full potential of their data and pave the way for a data-driven future.

So, let us raise a glass to data maturity, for it only gets better with time.

About the author:

Ellen Nielsen is the Chief Data Officer at Chevron. She has received numerous awards including CDO Magazine Global Data Power Woman, and DataIQ 100 – 5th most influential people in data in the USA. Nielsen serves on several boards including Women Leaders in Data and AI (WLDA) and the Global Editorial Board of CDO Magazine.

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