The Evolution Toward Data Maturity

(mmatee/Shutterstock)

Over the last decade, organizations have focused on becoming more “data-driven” as a core tenet of their business strategy. Being data-driven refers to the use of data to make business decisions, but that doesn’t necessarily measure a company’s overall data posture. Becoming a data-driven organization takes time and effort, with active investment in your overall data strategy.

Being realistic about where your company sits on the “data maturity” spectrum is a great place to start. Data maturity provides a more concrete measurement of data penetration within a business, and encapsulates the people, process, and tools involved in a data-driven organization.

In order for data professionals to evolve and face new challenges of governance and regulation, it’s important that they focus on increasing data maturity as their core posture. By treating data as a first-class product rather than an afterthought, data mature professionals and organizations can reduce complexity and adjust how they approach data management and analytics for the realities of today.

Data Maturity Defined

Data maturity can be defined by how much data is a part of the everyday strategy and fabric of a company. People often talk about company size in relation to data maturity, but those two things are completely orthogonal to one another. The size of the company alone misses some important nuances like mindset, experience, and pervasiveness of data within the company culture.

For example, with data collection only increasing, it’s likely that data silos will continue to persist unless a company puts real effort behind a solid data strategy. Mitigating the impact of data silos requires a level of maturity that extends beyond just the technology or the resources a big company has to offer.

Broadly speaking, data maturity is a function of three things: people, tools, and readiness.

Mature data organizations treat data as a product (ImagineDesign/Shutterstock)

On the people front, data mature organizations understand how much experience employees have with the latest data and analytics technologies. They help business units use and share their data to the best of their ability, and show a continuous hunger for upgrading their data ecosystem to remain at the forefront of what’s possible. Furthermore, they encourage all employees–no matter what their role is–to pursue data literacy, or the ability to read, understand, create, and communicate data as information.

On the tools front, data mature organizations treat data as a first-class product, rather than an afterthought. They are building a data-centric technology ecosystem and proactively creating organizational and technical infrastructure to support data products. Further, teams understand where their data product is being used and how, and treat those users as customers.

On the readiness front, data mature organizations have a good understanding of where they sit on the spectrum of maturity and can articulate what they want to accomplish with their data. Leadership has a data-first mindset and knows what business outcomes they hope to drive, and how data will help them get there. The company culture is one that weaves data into its values and strategy, whether via OKRs (objectives and key results) or employee expectations, and empowers employees to use data in every decision.

By putting the power back in the hands of the data producers, the data will be shared and defined by the people who know it best – those who created it.

Treating Data As a Product

Just as an organization would invest in product development, it needs to prioritize data in a similar way. Treating data as a product means enabling organizations to provide business users with data they can rely on.

A mature data ecosystem should focus on the same principles of a good product: speed (a short path between the data and the business user), scale (ability to seamlessly grow the data ecosystem along with the business), simplicity (users should be able to understand the data and have confidence in it), and SQL (users should be able to interact with data using the tools of their choice, including SQL, the lingua franca of data). I call these the “Four S’s of Data.”

​​With new data assets rapidly accruing, and ever-changing regulatory requirements forcing data to remain in its respective regions, organizations are starting to take a hard look at the traditional approaches they’ve taken to managing and analyzing data at scale. Achieving the Four S’s is critical to ensuring that organizations can rely on their data and gather meaningful insights.

Evolving with Data Maturity

Data maturity will continue to evolve as organizations become more data-driven, while also recognizing the limiting factors of regulatory requirements and data accessibility. With the ever-accelerating evolution of cloud computing, managed service offerings, and production of data itself, being thoughtful and focused on data as a product and key asset will continue to be a differentiator in modern businesses.

About the author: Colleen Tartow is the Director Of Engineering at Starburst Data. Tartow has more than 20 years of experience in data, advanced analytics, engineering, and consulting, and has been obsessed with data her entire life. She holds a Ph.D. in astrophysics from U.C. Santa Barbara, and lives in Massachusetts.

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