Prescriptive Analytics

What is prescriptive analytics?

Prescriptive analytics is a relatively new, more powerful field of business analytics compared to past descriptive, diagnostic, and predictive forms of analytics. Instead, prescriptive analytics leverages artificial intelligence (AI) in its data analysis to directly recommend actions to decision-makers to achieve specific goals.

It uses prescriptive decision models built on mathematical and statistical algorithms that factor in constraints and objectives to analyze a given problem. Using prescriptive analytics, an organization can leverage predictive insights based on past and present data for more informed decision-making when faced with complex situations.

How does prescriptive analytics work?

As Gartner describes, prescriptive analytics answers the questions “What should be done?” and “What can be done to make _________ happen?” It is based on specific goals established by decision-makers in various organizations. By Gartner’s definition, it is characterized by several techniques, including:

• Graph analysis: for optimizing resources and understanding relationships between entities
• Simulation: for assessing risk, predicting outcomes of decisions, and planning amidst uncertainty
• Complex event processing: for understanding how a sequence of events leads to certain outcomes
• Neural networks: for recognizing patterns and trends as well as making future forecasts
• Recommendation engines: for suggesting actions to decision-makers
• Heuristics: for recognizing patterns and supporting good decisions with incomplete information
• Machine learning: for identifying patterns in large data sets, predicting future events, and automating some decisions

These solutions are fit for any variety of data types and sources. They have various applications across industries, including healthcare, logistics, manufacturing, finance, and retail.

What are the benefits?

Organizations that leverage it can realize several benefits, including:

• Increased efficiency in decision-making by leveraging AI to help prioritize tasks, identify potential issues, and provide prescriptive guidance on how problems can best be solved
• Improved scalability and accuracy when making decisions by recognizing patterns, trends, and correlations to inform decision-makers of the best possible options
• Reduced risk of making wrong or inaccurate decisions; it helps identify any potential problems beforehand
• Increased financial savings as it can help streamline processes that lead to cost savings
• Reduced time to market as it can be used for automation and process optimization, which leads to quicker iterations

Ultimately, benefits are limited by the types of data available and the power and accessibility of the analytics tools delivering prescriptive analytics capabilities to decision-makers.

Prescriptive analytics and decision intelligence (DI)

Increasingly, self-service analytics tools that feature low-code and no-code interfaces make prescriptive analytics available to non-technical personnel. These capabilities are characteristic of modern decision intelligence (DI) environments. DI is the next generation of business intelligence (BI) solutions, where DI platforms democratize data access beyond the realm of data scientists and other technical personnel alone. In these ways, decision-makers at all levels of an organization can access prescriptive analytics to support decisions in their roles. With the right governance, universal access to prescriptive analytics drives business value in these capacities.

How can Pyramid Analytics help?

Pyramid Analytics is the world leader in decision intelligence, helping organizations leverage prescriptive analytics as part of a more robust analytics environment. Our unique DI platform delivers data-driven insights and other capabilities to various personnel in a governed, self-service way.

Contact us directly for guidance on prescriptive analytics or to learn more about our decision intelligence platform.