Analytics is probably the most important tool a company has today to gain customer insights. This is why the Big Data space is set to reach over $273 Billion by 2023 and companies like Microsoft, Amazon and Google among so many others are so heavily invested in not only collecting data, but enabling data for the enterprise.
As AI and machine learning continue to develop, the way we use analytics also continues to grow and change. While in the past, businesses focused on harvesting descriptive data about their customers and products, more and more, they’re about pulling both predictive and prescriptive learnings from the information they gather. So—what is the difference between descriptive, predictive analytics and prescriptive analytics? And do you need the latter in your company?
If you’re new to the data analytics field, let’s do a quick overview:
● Descriptive analytics: data that provides information about what has happened in your company. Think about a monthly sales report, web hit numbers, marketing campaign rates, etc. They give you insights on how a project performed. This is the most basic form of analytics. (Think “analysis” vs. “analytics.”)
● Predictive analytics: data that provides information about what will happen in your company. Pulling on more complex machine learning and AI processes and algorithms, predictive analytics help you determine what will happen—how well a product will sell, who is likely to buy it, which marketing to use for the greatest impact.
● Prescriptive analytics: data that provides information on not just what will happen in your company, but how it could happen better if you did x, y, or z. Beyond providing information, prescriptive analytics goes even one step further to recommend actions you should take to optimize a process, campaign, or service to the highest degree.
To best honest, there is still a lot of confusion between what constitutes predictive and prescriptive analytics, and you may see them used interchangeably in some circles. Regardless, descriptive, predictive, and prescriptive analytics all play important roles in our organizations today. We don’t always need complex algorithms running on our data. Sometimes we just want to know where our financials stand or how much traffic our social media pages are getting. However, in those instances where we do want to improve efficiencies and optimize performance, prescriptive analytics are playing an increasingly important role.
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