Becoming a data-driven business doesn’t happen overnight. It takes time, money, and effort to develop the combination of knowledge, skills, and technological proficiency needed to weave business intelligence into the cultural fabric of your organisation. It’s a journey: one with many steps and milestones along the way.
To reach your destination safely, you need a guide. And we call that guide a data maturity model.
What is a data maturity model?
A data maturity model provides an objective measure of your business’s data capabilities, relative to your competitors. It separates the journey towards data maturity into several distinctive stages, highlighting the areas you need to improve to reach your goal.
There are countless data maturity models to choose from. And while each has its own structure and terminology, they’re all built around the same core competencies and capabilities.
- People: How comfortable your people are at managing, interpreting, and using data to improve business processes and systems.
- Technology: Do you have access to the right data management tools and services? And how proficient is your business at using them?
- Governance: How effective your controls are at ensuring data is accurate, consistent, reliable, secure, and compliant.
What does a good data maturity model look like?
The best data maturity models are easy to understand. Whether you're just setting out on your journey or have reached a point where you need to refine your approach, your chosen model should provide a clear set of steps to take you through each stage.
This includes everything from which data visualisation tools you should use to the training and support your team need to get up to speed.
Our model splits the journey into four stages:
- Foundational
- Reporting
- Predictive
- Embedded intelligence
1. Foundational
The journey of a thousand miles begins with a single step. For many businesses, the foundational stage is that step. You use a mixture of internal systems, third-party tools, and manual processes to manage your data. Microsoft Excel is your go-to reporting tool and, except for one or two specialists, your team has limited knowledge and experience interpreting data.
Your reliance on a handful of individuals means there are many single points of failure in your business. There’s an overall lack of consistency and accuracy in your data ecosystem. And, because your approach is almost exclusively manual, it takes considerable time and effort to extract meaningful insights.
It’s not all doom and gloom. In our experience, the foundational stage is where the smallest changes can have the biggest impact.
2. Reporting
At the reporting stage, you manage all your data from a central data warehouse. Everyone in your team has access to accurate and reliable insights, and your stakeholders have started to implement data in an increasingly wide range of projects.
You still have a small internal team whose experience lies primarily in gathering data for standard reports. But they’ve begun to deploy automated solutions to process data and perform high-level reporting.
3. Predictive
Now you have the technology and infrastructure in place, you’ve broadened your horizons to look beyond historical data and use predictive analytics to unlock exciting new opportunities. You’ve formed self-sufficient, multi-disciplinary teams capable of managing data at scale and using their growing expertise to tackle complex challenges across your business.
Stakeholders have seen a significant return on their original investment and are now leading the charge to make data a fundamental part of your culture.
4. Embedded intelligence
This is the holy grail of data maturity. You’ve embedded high-level business intelligence and data governance into your core systems and processes. At this point, data analytics isn’t something you do. It’s part of your DNA and the driving force behind your business strategy. Everyone has access to actionable data insights that drive performance and efficiency.
You have specialist teams for every discipline – from data engineering and analytics to data science. This has enabled you to build and deploy production-ready machine learning models capable of unlocking powerful business insights quickly and at scale.
Why do you need a data maturity model?
Embarking on your journey without a data maturity model is a bit like trying to navigate an unfamiliar city without a map. You’ll reach your destination eventually, but it’ll take twice as long and there’ll be many wrong turns along the way.
A data maturity model focuses your efforts. It helps you keep track of your progress as you ascend through the different levels of data maturity and ensures you don’t lose sight of the wider business goals your data transformation supports.
And, by clearly defining the capabilities you need at each stage, your data maturity model provides guidance on which technologies and solutions you should invest in.
It’s time to start your journey
Business intelligence isn’t a luxury that only tech giants can afford. In today’s data-driven world, it’s an essential strategic asset that powers innovation and leads to success. No matter your starting point, a data maturity model can guide you through every stage of your journey – until extracting business actionable insights from data becomes second nature.
