Data Analytics

The next IT section is on data analytics. Data analytics involves taking data or information to analyze something.

There are four main purposes for using data analytics:

• Descriptive

• Diagnostic

• Predictive

• Prescriptive

Let’s talk through each purpose of data analytics. When you’re using Excel and sorting information into a table, you’re engaging in data analytics. You’re structuring data and trying to reach meaningful conclusions.

The first two terms for data analytics are both rooted in retrospection. We have descriptive analytics and diagnostic analytics. For this discussion, let’s pretend we are a company that sells chocolate bars.

Descriptive analytics describes what happened in the past. For instance, “We lost 20% of sales last year” is a descriptive analytic. Diagnostic analytics, on the other hand, asks why the change happened (e.g., a significant competitor entered the market). Descriptive and diagnostic analytics both focus on past events.

The next two terms are forward-looking: predictive analytics and prescriptive analytics. Predictive analytics estimate future outcomes. For instance, “We will gain 10% of market share” is a form of predictive analytics. Prescriptive analytics, on the other hand, shows how we can achieve a specific result in the future. If we want to attain 10% of the market share, we might need to increase our marketing spend. It prescribes a course of action. Predictive and prescriptive analytics look toward the future. Importantly, data analytics can be used in any area of a business.

Study Tip: Descriptive analytics and diagnostic analytics focus on the past. Predictive analytics and prescriptive analytics focus on the future.