Prescriptive Analytics
Prescriptive analytics is an advanced form of business analytics that uses AI-driven decision models and techniques like graph analysis, simulation, machine learning, and recommendation engines to analyze data, factor in constraints and objectives, and directly recommend optimal actions to decision-makers across various industries for improved efficiency and goal achievement.
What is prescriptive analytics?
Prescriptive analytics is a relatively new and more powerful field of business analytics compared to descriptive, diagnostic, and predictive analytics. It leverages artificial intelligence (AI) in its data analysis to directly recommend actions to decision-makers to achieve specific goals.
Prescriptive analytics uses decision models built on mathematical and statistical algorithms that factor in constraints and objectives to analyze a given problem. Organizations can use predictive insights based on past and present data for more informed decision-making in complex situations.
How does prescriptive analytics work?
According to Gartner, prescriptive analytics answers questions such as “What should be done?” and “What can be done to make ______ happen?” It is based on specific goals established by decision-makers. Techniques used in prescriptive analytics include:
- Graph analysis: Optimizing resources and understanding relationships between entities
- Simulation: Assessing risk, predicting outcomes of decisions, and planning amidst uncertainty
- Complex event processing: Understanding how a sequence of events leads to certain outcomes
- Neural networks: Recognizing patterns and trends, making future forecasts
- Recommendation engines: Suggesting actions to decision-makers
- Heuristics: Recognizing patterns and supporting good decisions with incomplete information
- Machine learning: Identifying patterns in large data sets, predicting future events, and automating some decisions
These solutions can handle various data types and sources and have applications across industries such as healthcare, logistics, manufacturing, finance, and retail.
What are the benefits?
Organizations that leverage prescriptive analytics can realize several benefits:
- Increased efficiency in decision-making by leveraging AI to prioritize tasks, identify potential issues, and provide prescriptive guidance
- Improved scalability and accuracy in decisions by recognizing patterns, trends, and correlations to inform the best possible options
- Reduced risk of making wrong or inaccurate decisions by identifying potential problems beforehand
- Increased financial savings by streamlining processes that lead to cost savings
- Reduced time to market through automation and process optimization, enabling quicker iterations
The benefits depend on the types of data available and the capabilities of the analytics tools used.
Prescriptive analytics and decision intelligence (DI)
Self-service analytics tools with low-code and no-code interfaces are making prescriptive analytics accessible to non-technical personnel. These capabilities are characteristic of modern decision intelligence (DI) environments. DI represents the next generation of business intelligence (BI) solutions, democratizing data access beyond data scientists and technical personnel. This allows decision-makers at all levels to access prescriptive analytics to support their roles. With proper governance, universal access to prescriptive analytics can drive business value.
How can Pyramid Analytics help?
Pyramid Analytics is a leader in decision intelligence, helping organizations leverage prescriptive analytics as part of a robust analytics environment. Their DI platform delivers data-driven insights and other capabilities to various personnel in a governed, self-service manner.