The Road to Data Excellence: Exploring the Analytics Maturity Model

Learn about the Analytics Maturity Model, a framework designed to assess and improve your analytics capabilities. Explore the six stages of the Analytics Maturity Model from ‘Ad Hoc’ to ‘Cognitive’ and understand their key characteristics. Elevate your data-driven decision-making with this comprehensive guide.

Wherever you are at with analytics right now, you are on a journey. Analytics never stands still and is always moving forward.

It’s important to take stock of where you are right now, and set a course for where you want to try to get to, constantly adjusting course as you go.

That’s why the Analytics Maturity model exists. The Analytics Maturity Model is a framework that can be used to help you assess your current level of analytical capabilities and sophistication. It allows you to take stock of your current analytics practices and establish a roadmap for how to improve.

There are numerous versions of the analytics maturity model, and some of my favourites include this one from Altexsoft and this one from Phdata, but in this blog post I wanted to share a version that I’ve used and tweaked over the years which has helped me to build out my own analytics roadmaps in my various roles.

The model that I’ve used contains 6 distinct stages of analytics maturity:

1. Ad Hoc
2. Descriptive
3. Diagnostic
4. Predictive
5. Prescriptive
6. Cognitive

In this post, I will tell you what each of these stages are, highlight some of their key characteristics and list some of the signs that you could be at this stage on your own digital analytics maturity journey.

1) Ad Hoc

What is it?

The first half of the ‘foundational’ stage, the “Ad Hoc stage” refers to the earliest phase in your journey towards analytics maturity. This stage is characterised by a basic or informal approach to data analysis, where ad hoc processes and tools are used to answer specific business questions or solve immediate problems. There’s no structure to speak of at this stage and no one is really asking data-driven questions.

Key characteristics:

• Limited Structure: At this stage, there is usually no formal analytics framework or defined processes for data analysis. Instead, data is handled on a case-by-case basis, with little thought for standardisation or consistency.
• Manual Effort: Data analysis is often performed manually, using tools like spreadsheets or simple querying tools. There won’t be any automated processes or sophisticated tools for data processing or visualisation.
• Decentralised Analysis: Data analysis is typically conducted by individuals seeking to answer their own questions, without any coordination or collaboration. Different departments will probably handle their data analysis independently and people work within silos, often unaware of each other.
• Focus on Immediate Needs: The main objective of the ad hoc stage is to address immediate business needs or specific questions as they arise. There’s unlikely to be a strategic, long-term vision for analytics at this point.
• Limited Data Integration: Data sources may not be fully integrated, and data may be stored in separate systems, making it challenging to gain a comprehensive view of all the company’s data.
• Informal Reporting: Reports and analysis are often shared through informal channels, like emails or printed documents, rather than centralised reporting systems or dashboards.
• Limited Data Governance: Data governance practices won’t be well-established at this stage, leading to potential data quality and security issues e.g. your entire company database is just a spreadsheet.

Signs your organisation could be at this stage:

• Decentralised data analysis
• Limited or no analytics capabilities
• Data in silos
• Reporting shared through informal channels
• Data governance practices are not well-established
• No defined roadmap or strategy for analytics
• Limited use of data visualisation tools and interactive dashboards
• Decision-making based on intuition or limited information

2) Descriptive

What is it?

The “Descriptive” stage is the next phase of the analytics maturity journey, and the 2nd half of the foundational stage. In this stage, we start to develop more structured data analysis practices, moving away from purely ad hoc approaches. Descriptive analytics involves using historical data to understand what has happened in the past and gain insights into current performance. While still relatively basic in the grand scheme of things, the descriptive analytics stage marks progress towards becoming more data-driven and developing a deeper understanding of data.

Key characteristics:

• Foundational Structure: In the descriptive analytics stage, organisations start to establish the foundations for consistent and replicable data analysis. There is an increased focus on standardisation and consistency in data handling and analysis.
• Semi-Automation: Data analysis processes may become partially automated, with the adoption of more advanced tools beyond basic spreadsheets and querying tools.
• Centralised Reporting: Reports and analyses are more centralised and formalised compared to the ad hoc stage. There may be a move towards dashboards and reporting systems.
• Growing Data Integration: Efforts are made to integrate data sources to achieve a more comprehensive view of your company’s data. Silos start to break down.
• Emerging Data Governance: While still not fully mature, data governance practices begin to take shape to address data quality and security concerns.
• More Defined Analytics Strategy: You can start to develop a clearer roadmap and strategy for analytics, aligning with your overarching business goals.

Signs your organisation could be at this stage:

• Standardised data analysis
• Partial automation
• Centralised reporting
• Growing data integrations
• Emerging data governance
• Developing analytics strategy
• Expanding data literacy
• Basic descriptive reports

3) Diagnostic

What is it?

The Diagnostic Analytics Maturity Phase is the 3rd stage in the analytics maturity journey and follows the Descriptive Analytics Stage. In this phase, organisations progress further in their data-driven capabilities, moving beyond merely understanding what has happened in the past (descriptive) to gaining insights into why certain events occurred and identifying the factors influencing performance.

Diagnostic analytics involves conducting deeper analyses, exploring correlations, and uncovering root causes to provide a more comprehensive understanding of business processes and performance drivers. This focus on trying to understand the reasons behind past events is what makes it distinct from the descriptive stage as we can now strive to develop a more comprehensive understanding of business processes and performance drivers.

Key characteristics:

• Root Cause Analysis: Root cause analysis (RCA) is a systematic method to identify the underlying causes of observed changes, enabling you to address the core issues and implement solutions to prevent recurrence.
• Advanced Tools Usage: The adoption of more advanced analytics tools and techniques, such as statistical analysis software and forecasting, becomes more prominent.
• Predictive Insights: Diagnostic analytics results may lead to the identification of patterns that can be used to make predictions about future outcomes.
• Data Mining: Organisations may start carrying out data mining activities to discover hidden relationships and insights within their data.
• Collaborative Approach: Cross-functional teams collaborate to conduct in-depth analyses and address more complex business questions.

Signs your organisation could be at this stage:

• Root cause analysis to Identify core issues
• Forecasting future outcomes
• Data mining to extract hidden insights
• Cross-functional teams conducting collaborative analysis
• Usage of more sophisticated analytics tools
• Advanced reporting and visualisation
• Start exploring prescriptive analytics
• Data-driven decision-making and insight-based choices

4) Predictive

What is it?

Crystal ball time. This is now quite an advanced phase where organisations take a significant leap in their data-driven capabilities, moving beyond understanding past events (diagnostic) to actually anticipating future outcomes and making data-driven predictions.

Predictive analytics involves using historical data to forecast future trends and make strategic actions based on predictive insights. The focus shifts towards using data to anticipate business scenarios, enabling more informed decision-making.

This stage marks a transition from understanding the reasons behind past events to developing a forward-looking approach. With the use of advanced techniques, organisations can leverage data to predict trends, optimise processes, and drive business growth. The integration of predictive analytics further strengthens an organisation’s data-driven culture and empowers teams to make data-backed decisions with confidence.

Key characteristics:

• Predictive Modelling: Organisations use statistical models and algorithms to forecast future trends and outcomes, enabling them to anticipate potential opportunities and risks.
• Advanced Data Processing: Data processing becomes more automated and sophisticated, leveraging tools like machine learning to handle large datasets efficiently.
• Integrated Data Sources: Efforts are made to integrate data from multiple sources to gain a comprehensive view, enhancing predictive accuracy and relevance.
• Collaborative Approach: Cross-functional teams collaborate to conduct predictive analysis, combining domain expertise to derive deeper insights.
• Strategic Decision-Making: Predictive analytics guides strategic planning, empowering organisations to make proactive decisions aligned with long-term goals.
• Visualisations and Dashboards: Interactive visualisations and dashboards become prominent for conveying predictive insights effectively to stakeholders.
• Data Governance Strengthening: Data governance practices improve, ensuring data quality, security, and compliance as predictive analytics becomes more critical for decision-making.
• Shift towards Prescriptive Analytics: The organisation explores prescriptive analytics, which suggests optimal actions based on predictive insights, aiming to enhance decision optimisation.

Signs your organisation could be at this stage:

• Adoption of predictive modelling techniques to anticipate future trends and outcomes.
• Usage of machine learning and other advanced tools for data processing and analysis.
• Integrated data from various sources to get a holistic view of business operations.
• Cross-functional teams working together on predictive projects.
• Using predictive insights to inform long-term strategies.
• Utilisation of interactive dashboards and visualisations for data exploration and presentation.
• Improved data governance practices
• Considering prescriptive analytics to optimise decisions based on predictive insights.

5) Prescriptive

What is it?

The Prescriptive Analytics Maturity Phase represents a pivotal stage when an organisation can transcend the confines of understanding past events, as seen in the Diagnostic Analytics Stage, and progress into the realm of anticipating future outcomes through data-driven predictions and recommendations for action.

Predictive analytics is very much the cornerstone of this phase, as organisations need to draw from historical data, harness the power to predict future trends and then proactively make strategic decisions rooted in those predictive insights. The focus totally shifts from retrospection to anticipation, enabling businesses to confidently take a forward-looking approach.

This transition is underpinned by the deployment of advanced analytical techniques. Machine learning algorithms, statistical modelling, and data mining become essential tools in deciphering the potential of data-driven prediction. Organisations gain the capability to identify emerging trends, optimise processes, and fuel business growth through prescriptive analytics. As predictive analytics becomes ingrained, it fortifies the data-driven culture within the organisation, empowering teams to make informed decisions guided by data-backed foresight.

Key characteristics:

• Predictive Power: In the prescriptive stage, organisations leverage sophisticated predictive models that consider historical data, real-time inputs and other factors to forecast future trends and outcomes.
• Actionable Insights: Prescriptive analytics recommends specific actions or strategies to optimise outcomes based on data-driven insights and guide decision-makers toward the most favourable results.
• Automation: Automated decision-making systems that can swiftly respond to changing conditions and make real-time decisions based on predictive and prescriptive analytics are hallmark of this stage e.g. supply chain systems that autonomously adjust inventory levels based on demand forecasts.
• Data Integration: Integration solutions ensure that data from diverse sources, both structured and unstructured, is readily available for analysis.
• Cross-Functional Collaboration: Data scientists, analysts, and business leaders work closely together to develop and implement prescriptive analytics solutions that align with business goals.
• Risk Management: Prescriptive analytics aids organisations in identifying and mitigating risks effectively. By simulating various scenarios and evaluating potential outcomes, businesses can make informed decisions to minimise risks and seize opportunities.

Signs your organisation could be at this stage:

• Your organisation employs advanced predictive models capable of making accurate forecasts.
• Automation plays a prominent role in operations, with systems making real-time data-driven decisions
• You take proactive actions based on predictive insights to capitalise on opportunities and mitigate risks.
• Cross-functional teams routinely collaborate on data analysis and complex decision-making.
• Emphasis on optimising outcomes and continuously seeking ways to improve processes
• Data-driven decision-making is ingrained in your organisational culture, guiding key choices.
• You use a suite of sophisticated analytics tools and techniques to extract value from data.
• Reporting and visualisation tools are advanced, providing decision-makers with clear, actionable insights.
• Prescriptive analytics gives you a competitive edge by identifying trends and market shifts ahead of your competitors.

6) Cognitive

What is it?

Now this is where things get truly scary. The Cognitive Analytics Phase is the pinnacle of the analytics maturity journey, where organisations elevate their data-driven capabilities to incredible heights. This stage empowers organisations to not only predict and prescribe actions but also to think, reason, and learn like never before.

At the heart of this phase lies the fusion of predictive and prescriptive analytics with artificial intelligence (AI) and machine learning (ML). Organisations harness the power of AI algorithms to gain insights that transcend human capability. It’s not just about predicting future trends; it’s about the ability to continuously learn from data and adapt in real-time.

AI-driven models consider historical data, real-time inputs, and an array of complex variables to forecast future outcomes with unprecedented accuracy. These predictive insights serve as the foundation for a new era of data-driven decision-making.

Key characteristics:

• Cognitive Computing: AI and ML technologies are at the forefront, enabling machines to understand, reason, and learn from data autonomously.
• Autonomous Decision-Making: Automated systems make real-time data-driven decisions, enhancing operational efficiency and agility.
• Continuous Learning: Cognitive analytics systems continuously learn from data, adapting and improving predictive models over time.
• Complex Problem Solving: Organisations tackle intricate challenges by harnessing cognitive computing’s problem-solving capabilities.
• Data-Driven Innovation: Data is the driving force behind innovation, enabling organisations to uncover new opportunities and possibilities.
• Real-Time Adaptation: Businesses respond dynamically to changing market conditions, guided by cognitive insights.
• Cross-Disciplinary Collaboration: Collaboration between data scientists, AI experts, business leaders, and domain experts fuels cognitive analytics initiatives.

Signs your organisation could be at this stage:

• AI and ML are integral components of your analytics infrastructure.
• Your organisation deploys autonomous systems that make real-time data-driven decisions.
• Learning from data is embedded in your organisational culture, driving continuous improvement.
• Cognitive analytics addresses intricate business challenges, unlocking new opportunities.
• Data-driven innovation is a core driver of your business strategy.
• Your organisation adapts swiftly to market changes, guided by cognitive insights.
• Cross-functional collaboration fuels cognitive analytics initiatives, drawing expertise from diverse domains.

TLDR: Key Takeaways

Ad Hoc Stage

• Basic and informal approach to data analysis.
• Limited structure, manual effort, and decentralised analysis.
• Focus on immediate needs with little data integration or governance.

Descriptive Stage

• Transition to structured data analysis.
• Greater standardisation, partial automation, and centralisation.
• Growing data integration, emerging data governance, and development of an analytics strategy.

Diagnostic Stage

• Progress to understanding the reasons behind past events.
• Root cause analysis, advanced tools, and predictive insights.
• Data mining, collaboration, and data-driven decision-making.

Predictive Stage

• Advanced phase focusing on anticipating future outcomes.
• Predictive modelling, automation, and data integration.
• Emphasis on collaboration, strategic decision-making, and data visualisation.

Prescriptive Stage:

• Focus shifts from retrospection to anticipation of future outcomes.
• Leverages predictive analytics, advanced techniques, and data integration.
• Promotes collaboration, risk management, and data-driven decision-making.

Cognitive Stage:

• The pinnacle of analytics maturity with AI and continuous learning.
• Involves autonomous decision-making, complex problem-solving, and data-driven innovation.
• Real-time adaptation, cross-disciplinary collaboration, and AI integration are hallmarks.

This Article was originally published on analytics-activation.co.uk