Analytics Maturity Model

An analytics maturity model measures your organization’s maturity level, which you can then use to assess how effectively your organization uses its analytics capabilities to deliver results using its data. After completing an assessment, you’ll receive a score graded on an analytics maturity curve.

The higher your organization’s level of maturity, the more capable it is of using data to deliver business outcomes. Organizations at stage 4 of the analytics maturity model generate 6x more revenue over ten years than those at stage 2.

In a scenario where two organizations have the same data, the more analytically mature organization will make better pricing decisions, increase marketing performance, improve forecasting accuracy, and more.

What Are the Levels of Analytics Maturity?

There are five levels (also called stages) of analytics maturity.

1. Analytical Beginner
2. Localized Analytics
3. Analytical Aspirations
4. Analytical Companies
5. Analytical Competitors

Analytics Maturity Model

Stage 1: Analytics Beginner

If your organization is at stage 1, you are at the beginning of your analytics journey. You don’t have the data needed to answer questions, or if you do have the data, you’re not able to easily use it yet and the data quality is poor. No matter the situation, data isn’t part of the decision-making process, and decisions are often based on experience or intuition.

Despite the lack of analytical maturity, you have a fantastic opportunity to establish analytics maturity. Because you’re new to the analytics game, you can establish analytics best practices from the onset.

At stage 1, data is:

• Siloed or can’t be queried
• Inaccessible or requires permission
• Disorganized and improperly labeled
• Unprepared for analysis by anyone

At stage 1, analytics are:

• Siloed to departments
• Only shared during meetings
• Never used to make decisions
• Descriptive and only used for reporting

Stage 2: Localized Analytics

At this stage, your organization uses analytics for general business intelligence, such as dashboards and visualizations for reporting. Datasets are available but stuck in siloes or limited to departmental access. You might have predictive analytics capabilities, but most data analysis occurs in spreadsheets. Reports often contain simple data visualizations such as bar charts and graphs.

The average organization has a maturity score of 2.2, so there’s plenty of opportunity for an organization at this stage. You can gain a competitive advantage with a few changes and investments.

At stage 2, data is:

• In functional or process silos
• Managed by department or requires permission to access
• Both organized and disorganized
• Both unprepared and prepared for use by data analysts

At stage 2, analytics are:

• Siloed to departments
• Shared as requested
• Rarely used to make decisions
• Mainly descriptive and used for reporting; mostly ad-hoc

Stage 3: Analytical Aspirations

At stage 3, your organization is making appropriate changes to utilize data but needs to align data analytics across departments. Initiatives are underway to centralize data sources, workflows, and assets, but adoption and everyday use need to advance.

You may be adopting tools and platforms to help democratize data and analytics but seeing mixed results. You may also have the functionality for data scientists and data engineers to develop predictive models. To leap forward, you should look for ways to increase analytic use and accessibility.

At stage 3, data is:

• Becoming centralized and organized
• In the process of being governed and assigned access permission levels
• Becoming organized and discoverable
• Mostly prepared and ready for analysis by most people

At stage 3, analytics are:

• Becoming centralized
• Becoming shareable and self-service
• Partially used to drive decision making
• Expanding beyond descriptive to predictive analytics

Stage 4: Analytical Companies

If your organization is at stage 4, you have the data and tools to make decisions. Your organization is implementing solutions everyone can use and benefit from, including automation. However, you may need to take additional steps to see full organizational adoption and use.

Most of your decisions will be data-driven, and you should also use analytics for benchmarking. All that’s left for you is to make data and analytics the foundation for all decisions.

At stage 4, data is:

• Centralized, organized, and enhanced with third-party data sets
• Governed with access controls established
• Discoverable by anyone who has permission to use it
• Prepared and ready for analysis by anyone and everyone

At stage 4, analytics are:

• Centralized and accessible
• Shareable and self-service
• Primarily used to drive decisions
• Increasingly powered by machine learning and artificial intelligence

Stage 5: Analytical Competitors

If you’re at stage 5, congratulations! Organizations at this stage successfully use analytics across their organization and have implemented best practices for data management and governance. Analytics serve as a competitive differentiator for you and guide your business strategy.

Achieving this score is rare and requires a dedicated organizational effort to provide resources that support everyone.

At stage 5, data is:

• Centralized, organized, and enhanced with third-party sources
• Governed with access controls established
• Discoverable by anyone who has permission to use it
• Prepared and used for analyzing by everyone

At stage 5, analytics are:

• Centralized and accessible
• Shareable and transparent
• Used to drive all decisions
• Data science, machine learning, and artificial intelligence are widely used

What Does an Analytics Maturity Model Measure?

To produce your score, an analytics maturity model measures four core areas:

1. Data Maturity
2. Organizational Dynamics
3. Analytics Team Dynamics
4. Usage & Technology Dynamics

Here’s a short explanation for each of those four areas.

Data Maturity

Data maturity describes the readiness of your data to deliver value to your organization. It includes factors such as quality, optimization, and accessibility.

Most organizations have plenty of data readily available for their organization. However, most of it is locked behind siloes and needs to be cleaned up before it can be used.

Organizational Dynamics

Organizational dynamics describes the approach your organization takes to analytics. Your approach includes the analytics strategy you’ll use to gain organizational alignment, build a data-driven culture, and meet business objectives.

Analytics Team Dynamics

Analytics team dynamics describe how each department or team focuses on analytics, including leadership. The organizational design of your analytics plays a vital role in the strength of your team dynamics. Centralization is key.

Usage and Technology Dynamics

Usage and technology dynamics describe the platforms, tools, technologies, and infrastructure your organization uses to enable analytics and data governance. This metric also considers how employees interact with the analytics available to them.

Understanding Analytics Maturity

A common misconception is that analytics maturity is related to the progression in the types of analytics your organization performs.

Under this theory, your organization becomes more mature as you move from descriptive analytics to diagnostic analytics, from diagnostic analytics to predictive analytics, and so on.

But this idea isn’t correct. To paraphrase the opening of this piece, analytics maturity is about how effectively your organization uses its data across departments to deliver actionable insights—and results.

You can have all the platforms and tools you need, but if you’re not seeing results or if your organization isn’t using your data effectively to drive decisions, then it’s not analytically mature.

Common differences between analytics maturity and analysis progression include:

• Having descriptive and diagnostic reporting capabilities but not using them to assess performance and drive positive change
• Having predictive and prescriptive capabilities but not using them to inform decisions and deliver results
• Investing in analytical tools but not enabling everyone in your company to leverage metrics

Of course, as your organization becomes more analytically mature, it will most likely use more advanced analytics, machine learning algorithms, and artificial intelligence to make decisions. That’s part of the process.

However, these advanced analytical capabilities exist in many organizations—but they’re not being used.

You can have the most advanced capabilities possible at the ready within your organization but still need to score higher on an analytics maturity assessment because of the analytical roadblocks your organization faces.

Why Analytics Maturity Matters and What You Can Do To Improve Yours

Every decision you make now has a long-term, compound effect on your organization’s long-term income, revenue, profit, and more—both positive and negative.

As we mentioned at the beginning of this piece, an organization at Stage 4 of the analytics maturity curve will see 6x more revenue after 10 years than a Stage 2 organization. That same Stage 4 organization will also see nearly 4.8x more operating income.

By taking steps now to increase your analytical maturity to the next level—or more—you can set your organization up for long-term success.

When you take an analytics maturity assessment, you’ll answer a series of questions about how data and analytics are used at your company.

Once you’ve answered all the questions, you’ll receive a report with your score that rates your level of analytics maturity compared to your peers.

As part of your report, you’ll also receive a roadmap of steps you can take to increase your maturity.