Prescriptive analytics

Prescriptive Analytics automatically synthesizes big data, mathematical sciences, business rules, and machine learning to make predictions and then suggest decision options to take advantage of the predictions. Prescriptive analytics is the third phase of business analytics (BA) which includes Descriptive, Predictive and Prescriptive analytics.

The most traditional of business analytics is Descriptive analytics, and it accounts for 90% of all business analytics today. Descriptive analytics looks at past performance and understands that performance by mining historical data to look for the reasons behind past success or failure. Almost all management reporting such as sales, marketing, operations, and finance, uses this type of post-mortem analysis.

The next phase is Predictive Analytics. This is when historical performance data is combined with rules, algorithms, and occasionally external data to determine the probable future outcome of an event or a likelihood of a situation occurring.

The final phase is Prescriptive Analytics. Prescriptive Analytics goes beyond predicting future outcomes by also suggesting actions to benefit from the predictions and showing the decision maker the implications of each decision option. Prescriptive Analytics not only anticipates what will happen and when it will happen, but also why it will happen. Further, Prescriptive Analytics can suggest decision options on how to take advantage of a future opportunity or mitigate a future risk and illustrate the implication of each decision option. In practice, Prescriptive Analytics can continually and automatically process new data to improve prediction accuracy and provide better decision options.

History

Prescriptive Analytics has been around since 2003-2004. The technology behind Prescriptive Analytics synergistically combines data, business rules, and mathematical models. The data inputs to Prescriptive Analytics may come from multiple sources, internal (to a corporation) and external (also known as environmental data). The data may also be structured, which includes numerical and categorical data, as well as unstructured data, such as text, images, audio, and video data, including big data. Business rules define the business process and include constraints, preferences, policies, best practices, and boundaries. Mathematical models are techniques derived from mathematical sciences and related disciplines including applied statistics, machine learning, operations research, and natural language processing.

Applications in Healthcare

Multiple factors are driving healthcare providers to dramatically improve business processes and operations as the United States healthcare industry embarks on the necessary migration from a largely fee-for service, volume-based system to a fee-for-outcome, value-based system. Prescriptive Analytics is playing a key role to help improve the performance in a number of healthcare areas.

Prescriptive Analytics can benefit healthcare strategic planning by using analytics to leverage operational and usage data combined with data of external factors such as economic data, population demographic trends and population health trends, to more accurately plan for future capital investments such as new facilities and equipment utilization as well as understand the trade-offs between adding additional beds and expanding an existing facility versus building a new one.

In Provider-Payer Negotiations, providers can improve their negotiating position with health insurers by developing a robust understanding of future service utilization. By accurately predicting utilization, providers can also better allocate personnel.

Applications in Energy and Utilities

Natural gas prices fluctuate dramatically depending upon supply, demand, econometrics, geo-politics, and weather conditions. Gas producers, transmission (pipeline) companies and utility firms have a keen interest in more accurately predicting gas prices so that they can lock in favorable terms while hedging downside risk. Prescriptive Analytics can accurately predict prices by modeling internal and external variables simultaneously and also provide decision options and show the impact of each decision option.

Applications in Telecommunications and Cable

Telecom and cable companies have large field service organizations to install, repair and resolve issues at customer sites (home or business). Mastering field service operations is critical because field services impact customer satisfaction, cost and churn. Prescriptive Analytics enables telecom and cable providers to dramatically improve effectiveness and efficiency of field service operations.

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