Prescriptive analytics for high added value decision-making

What is prescriptive analytics?

« Prescriptive analytics is the application of logic and mathematics to data to specify a preferred course of action. While all types of analytics ultimately support better decision-making, prescriptive analytics outputs a decision rather than a report, statistic, probability or estimate of future outcomes ». (Gartner)

Other analytics domains are based on processing, querying or simulating historical data to describe or diagnose a past situation, or even predict a future situation. Prescriptive analytics takes those insights to the next level by suggesting the best way to handle a future situation. It can be - but doesn’t need to - nurtured by other types of analytics: they perfectly complement each other.

Prescriptive analytics helps to provide multiple insights to suggest different decision options, mitigate future risks, simulate various scenarios, optimize the customer experience and much more.

As shown in the overview on the Figure 1 above, prescriptive analytics answers to the question « what should I do? » and can be applied to strategic, tactical and operational decisions.

Why integrate prescriptive analytics in your organization?

The awareness of the value of analytics and data-driven decision-making is gathering momentum across all industries. « Predictive and prescriptive analytics is attracting 40% of enterprises’ net-new investment in business intelligence and analytics. » Gartner estimates.

Here are 4 trends enhancing the need of prescriptive analytics:

1. Automation

With billions of possible solutions, evolving and numerous constraints, various objectives and mutual dependence between each other, problems are getting more and more complex. Decision automation offers the ability to find optimal solutions and simulate scenarios.

2. Scope for improvement of operational performance

Rather than making decisions based on what it appears to be a good solution, prescriptive analytics aims to specify with certainty what is the best solution. Significant increase in productivity can be expected from mathematical optimization.

3. Need of robust and adaptable models

The covid-19 crisis has highlighted the deficiencies of prediction-based models. Robust and adaptable recommendations are key to face effectively dynamic complex problems.

4. Technological maturity

The success of software editors like Toucan Toco or Dataiku shows how much organizations are equipping with descriptive and predictive analytics. Now it’s time to climb the next step of the analytics ladder with prescriptive softwares!

How to move up to prescriptive analytics?

The technological complexity curb

Prescriptive analytics leverages on Operations Research, a decision science at the crossroads of Mathematics, Computer Science, and Management, applied to solve business problems. It mainly combines several optimization techniques to constitute a solver for decision-making.

Initially reserved for a limited circle of professionals because of the high level of expertise required, optimization is now starting to spread among organizations thanks to rising awareness and pre-implemented software modules.

DeepTech made easy with tailored & scalable optimization modules

Because optimization problems need by nature a tailored approach, while being handled by accessible tools, Atoptima’s application solvers are designed to deliver powerful prescriptive solutions.

As your DeepTech partner for complex operations planning issues, Atoptima provides embedded cutting-edge SaaS optimization solvers with an ergonomic user interface, high-level modeling tools and a scientific expertise.