The Data Analytics Maturity Model

The idea of IT maturity is now decades old, and there is a number proven of maturity models from Gartner, ITIL and others that organizations use to measure their overall effectiveness. Now that data is at the core of today’s decision-making, shouldn’t it have a maturity model of its own?  How else can you determine at a high level how Data and Analytics are driving value in your organization. How can you create a roadmap for further analytics success if you don’t where you are – if you can’t pinpoint your maturity level?

ACTS today works with organizations to transform their approach to Data and Analytics, which ultimately transforms the business itself.

At the center of this Advanced Data Analytics revolution is the ACTS Analytics Services’ framework and its five-level maturity model.

The maturity journey starts at the lowest level, where a business has only ad hoc processes and disparate siloed data sources. At the end of the journey, the enterprise reaches innovator status where data is all together as a service in a repository, automation is applied as are artificial intelligence and machine learning. This is when the enterprise is truly transformed, rises above other market players, and experiences accelerated growth.

The ACTS Data Analytics Maturity Model

Data Maturity Spectrum

A key step is recognizing that data is important. Once that sinks in, you think about what to do with your data, what questions you want to be answered such as ‘What happened? Why did it happen?’

Then you think about the future. Once you are good at operationalizing data to see why something happened through diagnostics, you start discovering what will happen, and even better – how do we make it happen. That is the innovator level, the highest rung on the Data Analytics Maturity Model ladder. At the innovator level, the enterprise is leveraging deep learning, computer vision, natural language processing (NLP), and other advanced cognitive techniques and technologies. The innovator also involves DataOps and MLOps automation techniques where actions happen automatically inside the system, and people just don’t even realize it’s happening. In fact, there are AI implementations today for Data Analytics that have autonomous functions right out of the box.

Nucleus Research did an in-depth ROI analysis of Data Analytics, and how economic value increases as maturity rises. “Nucleus identified four stages in the evolution of analytics. The initial stage of automating reports yielded an average ROI of 188 percent. This more than doubled to 389 percent on average in the tactical stage where organizations leverage analytics to improve decision-making, rather than just increase productivity. ROI jumped to an average of 968 percent in the strategic phase, where enterprises deployed analytics across most of the organization and used them to align daily operations with the goals of senior management,” Nucleus argued. “The predictive phase yielded the highest average ROI of 1,209 percent, as organizations tapped into ‘Big Data’ and extended their analytics to larger data sources beyond their firewall to include partner ecosystems and social media.”

Let’s walk through the five ACTS levels.

Basic Level

At the very bottom, the organization uses little to no technology for data analytics and relies upon manual means and low-level software tools such as basic databases and spreadsheets. Data is not truly managed or effectively analyzed.

Things are a bit better at the ACTS basic level as data is captured and stored. However, data usage and analysis are done on an ad hoc basis. There is reporting that is done on an operational level, but it tends to be basic and not with a view of two other parts of the organization. Reports tend to be tactical and based on who yells the loudest. In essence, reports are often created on an on-demand basis.

As you might imagine, there is no real strategy for data and data analytics, or structure as to how it is done. As a result, decisions are made on small batches of non-analyzed data and often rely on gut instinct, or intuition. This kind of guessing is not the way to move your business forward.


While reactive is just one step up from basic, there is a world of difference. At this stage, the enterprise is getting more serious about data analytics. In fact, the business is starting to understand value streams and is embedding the usage of Data Analytics in driving those value streams. At the same time, the enterprise is beginning to understand the value of a data repository and is starting to introduce the concept of a data warehouse. With data now being more centralized, the enterprise is beginning to apply business intelligence to the analysis of that data. With these strategies in hand, the enterprise is starting to understand the notion of data governance.


At the proactive level, the enterprise is exploring why something happened. They are now harnessing data analytics to do diagnostics.

As you have seen in the reactive level enterprise understands value streams and the importance of embedding data analytics to drive those value streams. At the proactive level, data is embedded more fully and in more value streams and is applied to more operational activities.

The idea of a central data repository is handled at a higher level through data-centric storage such as applying a Data Lake. To improve analytics efficiency and better support end-users, self-service analytics are beginning to be deployed.

Finally, data governance is taken even more seriously and is applied to the overall data analytics operation.

In general, proactive features a consistent process that everyone follows, but not everybody understands it. Data Analytics is starting to be operationalized but is not 100% of the way there.


The strategic level moves beyond diagnostics and moves towards being predictive – as in answering the question ‘what will happen’? Much of this predictability comes from machine learning.

At this stage, level data is becoming far more of a strategic asset, and at the same time, the analytics ability has improved such that it can be applied to many tactical situations.

At the same time, analytics are introduced even more into value streams, and the analytics applied are of a far more advanced nature. These advancements include advanced visualizations and machine learning.  Another critical advance is the application of automation to analytic processes and the overall move to a DataOps and MLOps mindset.

Some of an organizations diagnostic tools often include predictive abilities. Machine learning drives this forward by performing automated forecasts based on your company’s key success criteria, business metrics, and use cases.

In general, predictive is more of an optimized model where people are still utilizing or doing some work manually but are following a consistent proven process. There is an established reason for the process and the team understands its value.


Now that you know why something happened, and have an idea of what will happen next, wouldn’t it be great to know how you can make it happen? That’s where prescriptive analytics comes in. With this ability, data becomes more core to decision-making.

At the innovator level, analytics techniques also move to the leading edge. Now the enterprise is applying cognitive AI systems and services through deep learning, computer vision for some industries, augmented analytics, and autonomous analytics. At this stage, the organization has fully embraced a data-centric culture.

Next Steps

If you’re looking to monetize, drive innovation and radically boost ROI with your data, ACTS empowers you to unleash that value. We deliver solutions for the entire data journey map from data collection to data management to downstream analytics, to cognitive AI and Machine learning.  Get started with our Modern Data Maturity Assessment  or contact us directly at [email protected] for a personalized consultation.

David Baldwin Photo

David Baldwin

Practice Manager – Data & Insights, ACTS, Inc.

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