The ultimate guide to data maturity

Why do most companies fail at becoming data-mature? 

Is it that they don’t understand its value? Or maybe they lack the right tools? Perhaps they just don’t have the right processes in place?

In most cases, it’s a combination of all three.

In this guide, we dive into the world of data maturity, with chapters that comprehensively explain data governance, the four stages of data maturity, and data democratization. In this introductory chapter, learn what data maturity is, why it’s important, and the best practices for improving it.

What does data maturity really mean? 

Data maturity refers to how well a company manages and uses its data. This includes a range of factors, like

• Data governance: policies that define how you collect, store, and access data

• Data quality: the accuracy, completeness, and consistency of your data

• Data architecture: a framework that outlines how you structure and disseminate data throughout your organization

• Data analysis: how teams and analysts interpret data to get valuable insights

• Data security: how your business protects data in accordance with regulatory requirements and compliance standards

• Data literacy: how easy it is for people to access and interpret data throughout your company

• Decision-making: how stakeholders leverage data to make strategic business decisions

Why is data maturity important?

Data maturity gives companies a competitive advantage. While data-immature businesses rely on gut instinct or copy what everyone else does, data-maturate ones use quantitative and qualitative insights to connect with their target audience in innovative, personalized ways and drive business growth.

In fact, an IDC whitepaper sponsored by Heap (now part of the Contentsquare group) found that data maturity had a direct impact on performance:

• Companies with mature data practices achieve 2.5x better business outcomes across the board

• Over 80% of data mature teams can get answers to data-related questions within minutes to hours, vs. days to weeks for less mature teams

• 39% of data mature organizations have a Net Promoter Score® (NPS) above 60, compared to only 15% of data immature ones

What are the 4 stages of a data maturity model?

The four stages of a data maturity model are

1. Data-exploring: this is stage one, when an organization is just becoming aware of the importance of a data strategy and the benefits it brings, but does not yet have the frameworks in place to make the most of its data analytics

2. Data-informed: this is stage two, when an organization starts to prioritize data analytics and begins to implement new systems, tools, and processes 

3. Data-driven: this is stage three, when the organization is now effectively using advanced data analytics to make informed decisions

4. Data-transformed: this is stage four, when the organization has put data at the core of its processes, systems, and decision-making at both macro and micro levels

Sometimes these stages are given different names, but the main characteristics are the same.

📖 Read our comprehensive article on the 4 stages of data maturity.

How do you improve your data maturity?

To improve your data maturity, you should

• Consolidate or connect your data sources so everyone in the company has one single source of truth for data, instead of multiple disparate tools

• Provide data literacy training to all teams and as part of onboarding for new hires

• Create clear processes and systems for data governance, data management, and data security

• Maintain a high level of data quality to ensure all data is clean, accurate, and up to date

• Use data to guide decision-making and set business KPIs

• Democratize data throughout your organization via self-service tools that provide real-time insights

Become a data-transformed company today

Being a data-transformed company means operating at the height of data maturity: it’s embedded in how you approach, discuss, and present work. It drives how you onboard new people, strategize what to build and why, share your wins and losses, and ultimately measure success. 

To get started, make data accessible in real time through self-serve tools like Contentsquare. Train your teams to perform customer journey analysis. Teach everyone about your business growth model. Continue to drive your maturity by encouraging data-driven decision-making. Celebrating learning from both wins and failures.

Keep in mind that these practices will need to evolve over time, so don't wait another day to begin your journey toward becoming data-transformed!