Organizations are looking to improve the decision-making process which is getting increasingly complex each day. Data analytics can give you deep insights into your business and help you make smarter decisions, but only if you use analytics to its full potential. In this article, we explore the three different types of analytics – descriptive, predictive, and prescriptive – to understand how each type of analytics can be used to improve an organization’s operational capabilities.
- Descriptive analytics tells you what happened in the past
- Predictive analytics tells you what could happen in the future
- Prescriptive analytics tells you how you should react to possible future outcomes
Descriptive analytics is a preliminary stage of data analytics that gives you insights into what happened in the past. It presents a summary of historical data to provide useful information and allows the data to be used for further analysis.
Descriptive analytics provides visibility into metrics that can be used to measure a company’s performance and inform management’s strategy. The vast majority of the statistics we use fall into this category. For example, financial reports such as sales numbers, profit and loss statements, cash flow statements, revenue per customer, etc.
Predictive analytics is a form of advanced analytics that uses both new and historical data to forecast future events. Predictive analytics uses data mining, statistical analysis, and machine learning algorithms to identify the likelihood of future events. The patterns found in historical and transactional data can be used to identify future risks and opportunities.
Predictive analytics empowers organizations by anticipating behaviors and outcomes based on actual data and not on assumptions. Its applications include personalizing online advertisements, analyzing customer behavior to identify buying patterns, flagging suspicious financial transactions, identifying patients at risk of developing certain medical conditions, and generating credit scores.
Prescriptive analytics focuses on finding the best course of action in a scenario given the available data. Prescriptive analytics takes inputs from both descriptive and predictive analytics and applies them to the decision making the process. It can also be used to measure the impact of a decision on multiple possible future scenarios.
Prescriptive analytics can take processes that were once expensive and difficult, and complete them in a cost-effective and effortless manner. Potential applications of prescriptive analytics include financial services to offer personalized financial advice to consumers based on their goals, in retail to optimize product sales and pricing, in the travel industry to analyze consumer demand and optimize airline ticketing.
A combined approach to descriptive, predictive and prescriptive analytics enables organizations to proactively make decisions that improve outcomes. The key is to combine the type of analytics capabilities based on the nature of the problem to be solved and the complexity of its solution.
Often, the processes and data needed to support advanced analytics may not be in place. In such cases, you can start with solutions that work with existing data to gain immediate insights while simultaneously putting into place the technologies and processes to support more complex analytics.