Types of Data Analytics: Understanding the Basics Through a Simple Story
The Journey of a Coffee Shop Owner
Meet Lisa, a small coffee shop owner who wants to grow her business. She loves making coffee, but she knows that understanding data can help her make better decisions. However, she’s not sure how to use it.
One day, she learns that data analytics can be broken down into four types: Descriptive, Diagnostic, Predictive, and Prescriptive Analytics. Let’s follow Lisa’s journey as she applies each type to improve her coffee shop.
1. Descriptive Analytics – "What Happened?"
Lisa starts by looking at her sales records for the past month. She notices that more customers visit her shop in the morning than in the afternoon. She also sees that her best-selling drink is cappuccino.
This is descriptive analytics; it helps Lisa understand past events by organizing and summarizing data.
Example: "Last month, I sold 500 cups of coffee, and 60% of them were cappuccinos!"
2. Diagnostic Analytics – "Why Did It Happen?"
Now that Lisa knows what happened, she wants to understand why. She digs deeper into the data and realizes that many customers buy cappuccinos because of a new “Buy 1 Get 1 Free” promotion she ran last month. She also finds that fewer customers visit in the afternoon because there’s another popular coffee shop nearby.
This is diagnostic analytics; it helps Lisa find the reasons behind past trends.
Example: "Oh! The cappuccino sales increased because of my promotion. And my afternoon sales are low because of competition!"
3. Predictive Analytics – "What Might Happen in the Future?"
Now, Lisa is curious. She wants to know what will happen next month. She looks at past trends and sees sales usually increase when the weather gets colder. Since winter is coming, she predicts that more people will buy hot drinks.
This is predictive analytics; it uses past data to make future predictions.
Example: "If the weather gets colder, I might sell more hot drinks!"
4. Prescriptive Analytics – "What Should I Do?"
Lisa now knows what might happen, but she wants to take action. She decides to introduce a special winter drink and offer discounts on hot beverages in the afternoon to attract more customers.
This is prescriptive analytics; it helps Lisa decide what steps to take based on predictions.
Example: "If I introduce a winter special and give afternoon discounts, I can boost sales!"
The Result? A Smarter, More Successful Business!
By using these four types of data analytics, Lisa is able to make better decisions, attract more customers, and grow her coffee shop.
Now, the next time you see numbers and trends, think like Lisa, ask yourself:
1. What happened? (Descriptive)
2. Why did it happen? (Diagnostic)
3. What might happen next? (Predictive)
4. What should I do? (Prescriptive)
Data analytics isn’t just for big companies—it’s for everyone, including small business owners like Lisa… and maybe even you!
Understanding data analytics doesn’t have to be complicated. It’s all about using information to make better decisions. Whether you’re running a business, tracking your fitness goals, or planning your budget, these four types of analytics can help you make smarter choices.
“Data are just summaries of thousands of stories; tell a few of those stories to help make the data meaningful”