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The science of making better decisions: prescriptive analytics explained

If you’ve been following along, then you know that our last three posts have been focusing on each stage of the analytics maturity framework. This post will discuss the final stage, prescriptive analytics, in detail. In this stage, organizations use optimization techniques to make data-driven decisions. Optimization algorithms are used to determine the best course of action based on constraints, goals, and objectives. This stage requires a high degree of analytics maturity and a deep understanding of the business processes.



Imagine you are playing a game and you're not sure what move to make next. A smart robot helper could look at the game board and all the possible moves and tell you which one is the best to make. That's what prescriptive analytics does - it looks at all the data from the past, predicts what might happen in the future, and then tells you what you should do next to get the best outcome.

Some common questions that you may seek to answer during this stage:

What is the best pricing strategy for our products or services?

Which products should we focus on to maximize profitability?

What is the optimal allocation of resources for a given project or initiative?

What is the best distribution strategy for our products?

What is the most effective way to allocate our marketing budget?

What is the best way to reduce customer churn?

What is the optimal inventory level for a given product?


By answering these questions and using techniques such as optimization modeling, simulation analysis, or decision trees, organizations can identify the best course of action to achieve a desired outcome. This helps organizations make strategic decisions, allocate resources more effectively, and achieve better business outcomes.


An organization that is performing prescriptive analytics operates in a highly advanced analytics landscape. This landscape includes a range of analytics techniques, technologies, and tools that enable the organization to derive insights and make informed decisions based on those insights. At the heart of this landscape is a robust data infrastructure that enables the organization to capture and store vast amounts of data from a variety of sources. This data is then processed and analyzed using advanced analytics techniques such as machine learning, artificial intelligence, and statistical modelling. In addition to these techniques, the organization may also use sophisticated analytics tools such as data visualization software, predictive modelling platforms, and optimization algorithms to help interpret and make sense of the data.


The prescriptive analytics landscape also includes a team of highly skilled data scientists, data analysts, and subject matter experts who work together to design, develop, and implement prescriptive analytics solutions. These professionals are responsible for identifying key business problems, defining relevant KPIs, developing and testing models, and deploying solutions that deliver measurable business value.


Katherine Chiodo

Technical Writer and Data Operations


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