The last few years have seen a major shift in how organizations perceive the importance of data. Now that data has become a recognized resource, a similar storyline is developing in analytics. This makes sense. After all, if data is the “what”, analytics is the “why”. Data is nothing without the analytics to create the insight and generate real business outcomes. What this means is that analytics is switching from a supporting function to a driving role.
Getting the most value out of that data requires analytics maturity. In practice, there are several stages of how effectively organizations use their data; this is called the analytics maturity model. To explain further, we need to understand the foundations of an effective analytics maturity model as well as how organizations can reach each of the five stages.
What is an Analytics Maturity Model?
The simplest way to think of analytics maturity is as a measure of how well an organization uses its data. Those with a high maturity have data deeply ingrained in the organization and across all decision-making and departments.
The analytics maturity model is something of an industry standard – a way of better understanding the different levels of maturity. The basic idea is that you’re on a journey over time, starting with a small startup/entrepreneurial approach. As you gradually add more governance, control capabilities, people, and functionality, you’ll move up the maturity scale. The benefits of moving through the levels of maturity are high, but the road is full of potholes and detours. At every level, there are common challenges.
Stages of Analytics Maturity
If you’re looking to gain insights from the data your company collects, it’s helpful to understand each stage of the analytics maturity model and what a practical application might look like. The model breaks down into five levels: Descriptive Analytics, Diagnostic Analytics, Loading...Predictive Analytics, Prescriptive Analytics, and Cognitive Analytics.
1. Descriptive Analytics
The descriptive analytics stage answers the question: “What happened?” By interpreting current and historical data points, businesses can better understand what has changed in their organization.
- Example: Entrepreneurial Analyst
At this level, data usage is very spreadsheet-driven and IT independent. As such, some of the common dysfunctions include a lack of trust and governance, and a lot of subjective ‘truth’ guiding ad-hoc decisions.
2. Diagnostic Analytics
The diagnostic analytics stage answers the question: “Why did it happen?” By closely examining your data, you can uncover the reason behind a certain result.
- Example: Business Governance and Controls
Here we see the development of departmental ‘marts’ and dashboards based on static data. The dysfunctions at this stage include departmental silos, slow proliferation of tools, and poor performance because of high latency.
3. Predictive Analytics
The predictive analytics stage answers the question: “What will happen in the future?” By interpreting historical data, a business can predict future trends, events, and scenarios.
- Example: State of the Business
At this level, we start to see the emergence of data warehousing and business intelligence (BI), and a more central organizational data and analytics capability. This is when IT capacity can become overwhelmed by the scope of what’s required. Data is still not a business priority and performance often limits adoption.
4. Prescriptive Analytics
The prescriptive analytics stage answers the question: “What actions should we take next?” Businesses can determine the most optimal course of action for a desired outcome by analyzing all their data points.
- Example: Agility to Adapt
Here we see data becoming more of a strategic asset and the emergence of cloud economics. Low latency data pipelines and strong SLAs are driving new Loading...data science capabilities. However, there are still governance gaps and IT capacity is often still a struggle. AI/ML is only adding very limited value at the edges.
5. Cognitive Analytics
The cognitive analytics stage answers the question: “What don’t I know?” This is considered the highest level of automation and utilizes Loading...machine learning, Loading...deep learning, semantics, and natural language processing. This application gets smarter over time as it interacts with data and humans.
- Example: Business Innovation Driver
At the last level of maturity you start to bring data science to the data, rather than the data to data science. AI/ML are being applied at the operational core with query performance, concurrent usage, and high availability key components. But it’s not nirvana. There are still dysfunctions to overcome in terms of AI/ML being seen as a poorly understood black box. And the challenges around explaining results hinder adoption.
3 Benefits of an Analytics Maturity Model
1. Ability to Track KPIs
One of the benefits of using an analytics maturity model is that it can help organizations track key performance indicators (KPIs) and compare them against their desired goals. This information can be used to identify areas where performance is falling short and where to make the necessary changes. Additionally, tracking KPIs over time can help organizations measure their progress and to ensure that they stay on track.
2. Identifying Gaps
Identifying gaps in your data analytics strategy is critical in being able to make improvements in your organization, which can ultimately lead to better decision-making. Additionally, it can help organizations avoid potential problems down the road.
3. Benchmark Against the Competition
Using an analytics maturity model can also help organizations benchmark their performance against their competition. This information can be used to identify areas where an organization is doing well and areas where there is room for improvement. Being able to set realistic goals backed by data can save a lot of time and money.
Why Identify Your Analytics Maturity Level?
The need to know your baseline analytics maturity comes back to the need to have a clear data analytics strategy. You can’t put together a proper strategy unless you know and understand what your maturity level is. If you don’t know where you are starting from then you can’t define where you want to go or confirm when you’ve got there.
Your data analytics strategy will and should be multi-dimensional. A linear strategy that attempts to move from A to B will ultimately fail, as your organization will have multiple data-related priorities in various areas of the business. Instead, you’ll need to work on multiple fronts. There will always be things to be done immediately, such as a high-risk compliance problems that need solving. Then you’ll have some quick wins: probably tasks that are easy to pick up and use (these can help you the most in showing the data team is adding value). Meanwhile, your underlying long-term strategic target to build all your real competencies and grow your baseline maturity will be bubbling away in the background. At some point, those quick wins will disappear, the urgent fixes will be gone, and you’ll focus on the strategic baseline work 80% of the time. That’s the theory, anyway.
Is It Complicated?
It’s probably worth clarifying at this stage that it’s very rare for one data maturity level to apply across a whole business. Normally different parts of the business will start at different levels and probably have different aspirations. For example, maybe HR doesn’t want full AI/ML and just needs a couple of reports. But the marketing or finance teams have bigger aspirations.
It’s also important to understand that the journey through the maturity levels isn’t always straight. Sometimes you might even go backwards, for example when you have to re-platform, or when you first move to the cloud. You’ll also spend a lot of time going sideways and rebuilding to align with changes in your organization’s enterprise strategy.
Final Thoughts
The good news is that regardless of the issues you’re facing or where your organization stands on the analytics maturity model, Exasol can help you grow and move through the levels – to ultimately help you achieve your goals via the quickest, straightest path possible. There are no barriers to entry. Exasol is a fast, flexible, and proven platform used by some of the most ambitious businesses in the world. Working on-premises, in the cloud, or in a hybrid role, Exasol provides the analytics solution that grows and adapts to the aspirations and direction of any organization.
The truth is that everyone would like to move up to the highest maturity levels, but in most cases, they don’t have the tools, the people, or the culture in place to make it happen. Although there are a lot of ways to become a data mature business, it’s not always a linear path and it’s not going to happen overnight. Each time you rise to the next level of the analytics maturity model, you’ll need to support data democratization, not only by enabling more people to access data but by empowering them to do something smart with it.