Data analytics maturity levels are a crucial framework for organisations looking to assess and improve their data analytics capabilities. These levels provide a roadmap that guides businesses from basic data analysis to advanced, strategic data-driven decision-making. By understanding these maturity levels, organisations can identify their current position, set goals for improvement, and ultimately leverage data analytics to drive innovation and competitive advantage. In this post, we’ll explore the various data analytics maturity levels, their characteristics, and how organisations can progress through these levels to enhance their data analytics capabilities.
What Is Data Analytics Maturity
Analytics maturity reflects an organisation’s ability to effectively utilise analytics. It acts as a yardstick for gauging how well an organisation uses data historically and currently, guiding future data strategies. This concept is crucial for quantifying the return on investment (ROI) of analytics initiatives and optimising future strategies.
Analytics maturity is significant because, despite recognizing the importance of data analytics, organisations often struggle with where to invest and how to become more analytically mature. Analytics maturity is correlated with company performance.
Analytics maturity is based on four dimensions. Understanding and advancing through these dimensions can lead to higher levels of ROI and improved decision-making:
Data Maturity: Access to high-quality data is essential. Organisations must store data effectively, ensure its quality, and make it easily accessible.Organisational Dynamics: This includes strategic investments, talent, and processes that support data analytics plans.Analytics Team Dynamics: This involves how well analytic teams work with each other and key stakeholders across the organisation.Usage and Technology: This encompasses the tools, techniques, architectures, and practices that connect analytic teams to the rest of the organisation.
What Are Data Analytics Maturity Levels
Analytics maturity is a model that describes how organisations progress through stages of data analysis over time, moving from basic to more advanced types of analysis. The model suggests that more complex analytics provide greater value. The progression is not about moving from one type of analysis to another, but about adding additional types within an organisation.
The four types of analytics maturity are:
Descriptive: Answers the question, “What happened?”Diagnostic: Answers the question, “Why did it happen?”Predictive: Answers the question, “What is likely to happen?”Prescriptive: Answers the question, “How can we make something happen?”
How To Improve Your Data Analytics Maturity Level
Improving data analytics maturity involves several key steps that can be valuable for those who may not have a deep understanding of the process. By following these steps, organisations can enhance their data analytics maturity and drive informed decision-making across the board:
Improving Data Quality: Centralise your organisation’s data and automate reporting processes. Ensure your analytics team has diverse skills, including data science and engineering.Enhancing Organisational Dynamics: Focus on small-scale, high-value analytics projects to demonstrate the value of analytics to leadership. Make it clear that data is a core business pillar and provide employees with access to relevant dashboards.Enhancing Analytic Team Dynamics: Assemble cross-functional teams for early analytic projects and offer learning opportunities to address skills gaps. Seek scalable platforms that provide advanced analytics and machine learning capabilities.Enhancing Usage and Technology: Plan to scale and build your analytics infrastructure, considering cloud-based analytics and integration with your existing tech stack. Explore predictive and prescriptive analytics options, and ensure usability and accessibility of your technology.Democratising Analytics: Upskill knowledge workers across the organisation to enable them to work on smaller analytics projects. Provide awareness, training, and tools to support this democratisation process.
Advancing through data analytics maturity levels is crucial for organisations to unlock insights, drive informed decisions, and stay competitive. To advance in analytics maturity, organisations should prioritise building a strong foundation with descriptive and diagnostic analytics before moving on to more advanced predictive and prescriptive analytics. It’s crucial to choose the right tools for each persona to achieve success in analytics maturity. Reach out to us today at specialists@edenai.co.za or get in touch via https://edenai.co.za/get-in-touch to learn how Eden AI can assist you in upgrading your data analytics maturity levels.
This article was enhanced from these sources:
D. Sweenor (2022) What Is Analytics Maturity and Why Does It Matter?
https://www.linkedin.com/pulse/what-analytics-maturity-why-does-matter-david-sweenor/
A. Christensen (2022) What is Analytics Maturity Framework?
https://www.phdata.io/blog/what-is-analytics-maturity-framework/
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