The Analytics Journey - Navigating the Four Stages of Data-Driven Decision Making

As the volume of data continues to increase, the demand for effective analytics solutions has never been higher. Gartner’s Analytics Ascendancy Model provides a roadmap for organisations looking to navigate the complex world of data analysis and turn insights into action. This powerful model categorises the analytics process into four stages - Descriptive, Predictive, Diagnostic, and Prescriptive - each offering unique advantages and opportunities for businesses of all sizes and industries.

Think of the four stages of analytics as a journey from a traditional map to a GPS system. Just like a map, descriptive analytics provides a basic understanding of the terrain, highlighting what has happened in the past. Predictive analytics, on the other hand, is like a map with added information, providing forecasts and early warning signals for potential issues. Diagnostic analytics dig deeper, helping organisations understand the “why” behind past events, much like a detailed road map that shows the root cause of traffic congestion. Finally, prescriptive analytics act as a GPS system, offering actionable recommendations (such as an alternative route option, or a detour) and guiding organisations to the optimal destination by using data-driven insights. The four stages of analytics provide a comprehensive view of the data landscape, helping organisations make informed decisions and drive growth in their operations.

Descriptive Analytics: A Starting Point

Descriptive analytics answers the question of “what happened?” It is the first step of analytics to show you what has happened in the past. This stage of analytics involves exploring data, summarising data, generating reports, and creating visualisations to help organisations better understand “what happened” in their business through their data. Descriptive analytics is the starting point for many organisations, and many projects. It is often used to identify trends and patterns in order to make sense of what has happened. With this information, organisations can make data-driven decisions and develop a clearer understanding of their operations. Descriptive analytics is like looking through a rear-view mirror, providing insight into the past to inform decisions for the future. It allows organisations to observe what has been and use it as a guide for what could be.

I don’t see how an organisation can live today without measuring what happens in their business. For example, in retail, you have to know where every product is, at any moment throughout the supply chain, and then after the fact explore the data to see how products moved over time, where they were late and where they were early.

Diagnostic Analytics: Understanding the “Why”

Diagnostic analytics take things a step further by exploring the root cause of specific events. This stage of analytics helps organisations understand why things have happened and how they can prevent similar issues, or create similar opportunities in the future. With the help of diagnostic analytics, organisations can identify and address problems at the source, improving their overall operations. This is akin to a doctor who diagnoses the root cause of a patient’s illness instead of simply treating their symptoms. With the right diagnosis and treatment, the patient can be healed and future issues prevented.

Predictive Analytics: Forecasting the Future

In most operational projects, the ability to see what happened and explore why it happened is enough for teams to operate effectively. They can use those insights to develop policies and playbooks to improve their future operations. When it comes to planning and being proactive, the next step is to move from insight to foresight. To use predictive analytics.

In predictive analytics, various statistical techniques, such as data mining, predictive modelling, and machine learning, are used to analyse current and historical facts in order to make predictions about upcoming events.

Predictive analytics helps organisations make informed decisions by forecasting trends and providing early warning signals for potential issues. With the help of predictive analytics, organisations stay ahead of the curve and proactively respond to potential risks. Predictive analytics is like having a crystal ball - it can provide organisations with a glimpse into the future and arm them with the knowledge of what possible outcomes are likely to occur. This allows organisations to be better prepared for the future, allowing them to make smarter decisions and take proactive steps to mitigate potential risks.

For example, the ability to forecast sales for specific product categories in specific regions enables retailers to deal with the scale of their operations. Instead of painstakingly following every trend of every product, or waiting to see how it sells, a well-built forecast can identify early on whether a product is going to sell as they expected or perform badly and may be in need of interventions such as future markdowns. With accurate sales forecasting, those out-of-the-ordinary cases can be identified early on and interventions planned, allowing retailers to be proactive.

Prescriptive Analytics: Optimising Outcomes

Prescriptive analytics takes data analysis to the next level by suggesting specific actions to take in order to optimise outcomes and improve decision making. This stage of analytics extends beyond simply providing insights, and instead offers actionable recommendations based on the data analysis. With the help of prescriptive analytics, organisations can make informed decisions and drive growth in their operations. Prescriptive analytics is like having a personal adviser or coach who is familiar with the data and can provide informed guidance about the best course of action. In this situation, the data is the source of information, while the advice is the actionable recommendation.

Not a lot of companies, or projects at companies, make it to this level of analytics. The complexity of implementing optimisations, empowering users with the right tooling and providing them with automated recommendations is a lot higher than predictive modelling. Yet, when executed well, it can have a dramatic impact on the performance of any department.

In conclusion, the Gartner Ascendancy Model provides a valuable tool for organisations to approach data analytics in a structured and informed manner. By categorising the process into four stages - Descriptive, Predictive, Diagnostic, and Prescriptive - it allows organisations to balance the cost of implementing analytics solutions against the value they will provide. By taking a step back and assessing which stage of the model will bring the most benefit to the organisation, businesses can make data-driven decisions that drive growth and success while avoiding unnecessary costs. The Gartner Ascendancy Model is a valuable resource for organisations looking to maximise the value of their data and stay ahead of the competition. So take a minute to think about it - do you really need a full navigation system when a detailed map will suffice?