The four stages of data maturity– and how to ace them

What causes most companies to fail at becoming data-driven? It’s a tricky question to answer.

Is it that they don’t understand the value of being data-driven? Or maybe they lack the right tools? Or perhaps they just don’t have the right processes in place?

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

In this post, we’re going to dive into the world of data maturity. We’ll cover the four maturity levels companies progress through on the journey toward becoming data-driven. 

By the end, you’ll be able to diagnose where you and your business are on the data maturity scale. Afterward, you can explore our Best Practices hub to learn more about how you and your team can increase your maturity level and harness the full potential of your data.

 Let’s get started.

What does “data maturity” really mean? 

Before we can explore data maturity in depth, we must first understand what it means. Let’s take a look.

Data maturity boils down to how well a company leverages data for decision-making. Rather than relying on gut instinct or defaulting to the status quo, data-mature companies make decisions based on qualitative and qualitative insights into their customers. It's by bringing together all the data points across the entire customer journey and knowing how these tie up to business impact, that they know how and where to prioritize their investments.

In a Heap-sponsored IDC White Paper, “How Data Maturity and Product Analytics Improve Digital Experiences and Business Outcomes,” data mature leaders were found to see a 2.5X increase in business outcomes compared to lagging organizations. 

Rather than guesswork, data maturity leaders combine specific tools and practices with a data culture that supports data-driven decision-making throughout the org. Mature data practices can include everything from requiring A/B Testing before pushing a release to GA, to adopting a self-serve analytics solution.

The 4 levels of data maturity

Data maturity evolves in four levels. Each level is defined by a combination of strategic, operational, and cultural data-driven practices. To progress through the levels, it's essential to understand what it means for a company to be at each one.

Let’s take a closer look at each level now.

Level 1: Data-exploring 

The first level of your data maturity journey is really about exploring the data you have. You understand that you need to collect data to drive the right business investments, but you aren’t capitalizing on your data’s full potential. You lack standard best practices for how and when to use data, as well as policies for data management. Who should have access to what data? And who enforces those rules? These are still questions you’re figuring out.

In many companies, silos between teams and functions blur any direction on how data should be used to drive success. As a result, each team has their own take on data best practices and different teams may be looking at disparate data points across different tools.

At this level of maturity, decisions are still often being made with little to no data, and initiatives are rarely measured for impact, making it impossible to truly learn from each iteration.

Level 2: Data-informed

The second level of your data maturity journey is where we start to see some momentum. In this phase, your leadership is beginning to see the value of investing in analytics tools, the data stack, and best practices. They're curious about data-driven tools and want to explore different practices and processes. Plus, they're on the hunt for any gaps that need filling.

At this stage, your leaders start prioritizing investments in data collection and management. They start establishing best practices for roadmap planning and post-launch analysis. Adding success metrics to each brief or campaign becomes the norm, and so does post-mortem analysis. The team starts going through basic analytics training and gains access to self-serve tools that help answer their day-to-day questions in real-time.

Level 3: Data-driven

Now the fun begins! This is when data access has been more democratized and embedded into the company's culture. Data drives your Enhanced Project Delivery and Go-to-Market strategic and operational practices. Every initiative aims to drive business impact through data-driven decision-making. Teams are starting to understand how to drive business KPIs by optimizing the digital experience. 

Trusted and complete data is now readily available to anyone driving planning and execution. But beyond that, teams know how to use data to design effective and valuable digital experiments to enhance their digital experiences. Investment decisions are driven by past learnings and roadmap prioritization is mapped to business impact.

Level 4: Data-transformed

You’ve made it to Data Nirvana! Data is now part of your organization’s DNA. Every team and process is totally focused on data, and there’s a culture of sharing it throughout your organization. 

That means that everyone across teams and functions is looking at the same numbers and dashboards, and any new hires are quickly learning the data-driven ropes as they onboard. Your EPD and GTM teams can predictably invest in the right areas that drive your business growth levers, and commit to business goals accordingly.

Once your data-driven habits and culture have been formed, all you need to do is maintain them by consistently investing time and resources in data education, maintenance, and agile optimization.

How to determine your organization’s data maturity level

Determining where you are in your data maturity journey is a key step to figuring out what you have to do next. After all, while you might think yourself to be close to data-driven or above, it might not be the case.

Here are a few questions to ask yourself and your team to get started:

1. How do you measure the success of digital projects?

2. Are data and analytics easily accessible to the team? How quickly can they find and analyze data to answer their questions about product performance?

3. How does your organization connect product changes to business performance?

4. How does your organization experiment with new ideas?

5. Does your team effectively use both qualitative and quantitative data for analysis?

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To wrap it up, it's clear that increasing data maturity is crucial for improving business success, but it's not a quick fix. It takes a conscious effort and leaders with a clear mission and vision to make it happen. 

To get started, make data accessible in real-time through self-serve tools. Train your teams to perform customer journey analyses. 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!

If you want to learn how to start building data maturity, visit our data maturity hub.