To quote Lewis Carroll: “If you don't know where you are going, any road will get you there.” It’s a great quote, but what if you do have a specific destination in mind? Say, becoming a data mature organisation?
There are several roads that lead to true data maturity. The challenge many businesses face is knowing which one to take. And, equally important, how to measure their progress towards data maturity along the way.
The signposts are there: you just have to know where to look…
What is data maturity?
Data maturity is the measure of your data management and analytical capabilities relative to others. Date mature companies combine in-house expertise and advanced technologies to deliver business intelligence quickly and at scale.
The three key measures of data maturity
The simplest way to measure your progress is with a data maturity model.
A data maturity model provides an objective measure of your business’s digital competency. It does this by outlining the key capabilities that distinguish data-driven organisations from those that aspire to reach that level.
Our model splits data maturity into four stages:
- Foundational
- Reporting
- Predictive
- Embedded intelligence
Developing a clear understanding of your in-house capabilities will give you an indication of skill gaps, redundancies, and opportunities to take your business further. To determine which category you belong to, you must consider how your business performs in three core areas:
- People
- Process and governance
- Technology and infrastructure
1. People
It takes more than advanced business intelligence (BI) tools to make a data mature company. You might have the most sophisticated platform in the world, but it won’t count for anything if you don’t have the people to use it.
So, before you look at your processes and technologies, it’s important to measure your in-house capabilities.
- Are your people comfortable managing, interpreting, and analysing data?
- Do you have a dedicated, in-house analytics team or do you rely on external resources?
- Does data play a vital role in day-to-day activities across your business or is it limited to a specific department or function?
Culture is equally important when it comes to measuring data maturity. Data mature companies understand the value of data and use it to inform high-level decisions across the business. It’s more than a resource; it’s part of their DNA.
2. Process and governance
The “this is how we’ve always done it” mindset is one of the biggest hurdles to a successful digital transformation.
Every business department and function creates reams of data every day. But to extract value from this continuous flow of information, you need a systematic and consistent way to store, process, and share insights across your business.
To get an accurate measure of your data maturity, assess your current processes.
- Are they reliable, consistent, and repeatable?
- Do you have a system in place to report and share business insights between departments?
- How do you validate the quality and accuracy of your data?
- Are your processes manual, automated, or a combination of the two?
Governance should be at the heart of your processes. The insights you extract from your data will only provide value if you can verify the integrity of your sources. Equally, you must ensure your storage and management processes comply with data privacy regulations, such as GDPR.
3. Technology and infrastructure
Technology alone isn’t an accurate measure of your data maturity. The most advanced, cutting-edge platform is useless if it doesn’t match your current capabilities or support your business goals.
Before you invest in expensive data visualisation and analytics tools or infrastructure, ask yourself:
- What do you use to collect, store, analyse, visualise, and share data?
- Do you rely on manual tools like Excel, or do you use automated dashboards?
- Have you invested in machine learning and predictive analytics?
- Is your infrastructure scalable?
Data mature companies don’t just use historical data to identify customer trends and logjams in their processes. They harness the power of predictive analytics to shape the future of their business. We call this Embedded Intelligence – the holy grail of data maturity.
Data-driven businesses are 58% more likely to beat their revenue goals than those that rely on hunches and assumptions.
It pays to be data-driven
Peter Drucker once said: “If you can’t measure it, you can’t improve it”. Before you can progress through the different stages of data maturity, you must have a clear, unbiased view of your current capabilities across people, process, and technology.
This will also focus your efforts, ensuring you don’t lose sight of the business goals your digital transformation supports. Whether that’s to streamline operations, develop a deeper understanding of your customers, or identify new revenue streams.
