In a digitally transformed world, the combination of data and analytics is critical to maintaining a competitive advantage and business relevance. To achieve this goal, enterprises collect vast volumes of data and derive valuable insights from them. This knowledge can be anything from ascertaining customer satisfaction to identifying operational discrepancies.
The capability of business intelligence and analytics is continually evolving. In a highly competitive business world, analytics plays a key role in identifying trends and patterns to make quick and informed business decisions. Predictive and prescriptive analytics are two important methods in business-analytics solutions. Mordor Intelligence research suggests that the predictive and prescriptive analytics market (valued at $8.14 billion in 2019) is expected to grow at a compound annual rate of 22.53% to reach $27.57 billion by 2025.
As artificial intelligence (AI) and machine learning evolve and play a more significant role in data and analytics, smart algorithms can now pull both prescriptive and predictive insights from the data. Both approaches give insight and foresight to enable smart decision-making; they incorporate data mining, machine learning and statistical modeling to deliver deep insight into customers and overall operations.
Business managers use predictive analytics, as the name suggests, to predict the future by analyzing and identifying patterns in historical data with ML and statistical models. The insights derived from this data are then used to make better decisions and improve outcomes. The keystones of predictive analytics are predictive modeling, decision analysis and optimization and transactional profiling. This approach exploits patterns in historical and transactional data to identify opportunities and associated risks. Predictive analytics leverages AI and machine learning algorithms to build predictive models. These models are then used to analyze data sets to identify underlying patterns to anticipate future outcomes.
For example, banks analyze mortgage applicants' data, including their credit score, employment status, income and savings-to-debt ratio to predict if they would be low- or high-risk borrowers. These "predictions" also determine the size of the loans and the financial institution's interest rate. Furthermore, AI and machine learning algorithms are also used to identify patterns that possibly indicate fraud. On the other hand, prescriptive analytics is often described as the final phase of business analytics because it helps tweak predictive analytical models that predict what will happen if companies maintain their current course of action. It's a type of data analytics that factors data about available resources, possible situations, past performance, current performance and more to suggest a course of action or strategy.
Prescriptive analytics goes beyond predicting options to suggest a range of prescribed actions and the potential consequences of each action. It can also recommend the best course of action for each specified outcome. A self-driving car, for example, drives on its own by making millions of calculations during each trip. That's prescriptive analytics in action.
Unlike predictive analytics, prescriptive analytics is an abstract form of data analytics that helps companies explore "what if" scenarios and infer outcomes based on multiple variables. For example, airlines leverage prescriptive analytics to set airline ticket prices based on several possible factors. While relatively complex to administer, prescriptive analytics is worth the effort, as it can have a significant impact on your bottom line. Whether it's to boost production, streamline operations or optimize customer experiences, prescriptive analytics helps organizations make "smart" decisions.
Both predictive and prescriptive analytics are advanced forms of analytics that have a role to play in business intelligence.
