Data Analysis -Predictive and prescriptive analysis

Step into the future of data-driven decision making.

Posted by Judith Winkler MBA on January 28th, 2024

Descriptive analysis helps business owners to understand what has happened in their business by studying historical data. Predictive analysis help organizations predict and understand what could happen in the future. Predictive analytics is the next step in data analysis since it will use the patterns and trends found through descriptive analysis and customer insights to predict what might happen in the business. Predictive analysis is based on probabilities; it focuses on forecasting possible future outcomes and the likelihood of events.

Predictive analytics can forecast events, help business owners make informed decisions, and improve different business departments such as customer service, inventory management, and risk for example. However, business owners should remember that predictive analysis is not an exact science since it is based on probabilities.

Predictive Analytics examples:

  • Forecast future cash flow.
  • Determining staffing needs.
  • Forecast sales trends.
  • Preventing malfunctions.

Prescriptive analysis recommends business owners what should be done by considering what the company has learned from descriptive and predictive analysis. “What if” scenarios allowing decision-makers to understand upfront what might happen according to those different outcomes and make an informed decision by taking into consideration not only the analysis results but also their business knowledge.

Prescriptive analytics examples:

  • E-commerce – it can be used to predict customers' behaviors, and preferences, to recommend products to customers, for example, Amazon.
  • Sales – predicting the likelihood of leads to be converted into customers.
  • Banking- predictive analytics can be used for fraud detection- for example, an algorithm analyzes patterns in the client transactional data, alerts the bank if anomalies occur, and provides a recommended action.
  • Product Management: Development and Improvement by analyzing data collected by managers from surveys, customer behaviors, and market research among others to determine what items need to be added or changed from a product.

I have a small favor to ask, if you find this information useful, I ask that you share this blog with other business owners that might find this content useful as well. I will be setting a lot of effort towards posting regular content to help share knowledge about all things related to business and how data analytics can be used to improve companies. Thank you!