Notes: 4 Types of Data Analytics

Summary

4 Key Types of Data Analytics

Archived from Harvard Business School: “4 Types of Data Analytics to Improve Decision-Making” by Catherine Cote on October 19, 2021. Full credit to the original author.

1. Descriptive Analytics

Descriptive analytics is the simplest type of analytics and the foundation the other types are built on. It allows you to pull trends from raw data and succinctly describe what happened or is currently happening.

Descriptive analytics answers the question, “What happened?”

For example, imagine you’re analyzing your company’s data and find there’s a seasonal surge in sales for one of your products: a video game console. Here, descriptive analytics can tell you, “This video game console experiences an increase in sales in October, November, and early December each year.”

Data visualization is a natural fit for communicating descriptive analysis because charts, graphs, and maps can show trends in data—as well as dips and spikes—in a clear, easily understandable way.

2. Diagnostic Analytics

Diagnostic analytics addresses the next logical question, “Why did this happen?”

Taking the analysis a step further, this type includes comparing coexisting trends or movement, uncovering correlations between variables, and determining causal relationships where possible.

Continuing the aforementioned example, you may dig into video game console users’ demographic data and find that they’re between the ages of eight and 18. The customers, however, tend to be between the ages of 35 and 55. Analysis of customer survey data reveals that one primary motivator for customers to purchase the video game console is to gift it to their children. The spike in sales in the fall and early winter months may be due to the holidays that include gift-giving.

Diagnostic analytics is useful for getting at the root of an organizational issue.

3. Predictive Analytics

Predictive analytics is used to make predictions about future trends or events and answers the question, “What might happen in the future?”

By analyzing historical data in tandem with industry trends, you can make informed predictions about what the future could hold for your company.

For instance, knowing that video game console sales have spiked in October, November, and early December every year for the past decade provides you with ample data to predict that the same trend will occur next year. Backed by upward trends in the video game industry as a whole, this is a reasonable prediction to make.

Making predictions for the future can help your organization formulate strategies based on likely scenarios.

4. Prescriptive Analytics

Finally, prescriptive analytics answers the question, “What should we do next?”

Prescriptive analytics takes into account all possible factors in a scenario and suggests actionable takeaways. This type of analytics can be especially useful when making data-driven decisions.

Rounding out the video game example: What should your team decide to do given the predicted trend in seasonality due to winter gift-giving? Perhaps you decide to run an A/B test with two ads: one that caters to product end-users (children) and one targeted to customers (their parents). The data from that test can inform how to capitalize on the seasonal spike and its supposed cause even further. Or, maybe you decide to increase marketing efforts in September with holiday-themed messaging to try to extend the spike into another month.

While manual prescriptive analysis is doable and accessible, machine-learning algorithms are often employed to help parse through large volumes of data to recommend the optimal next step. Algorithms use “if” and “else” statements, which work as rules for parsing data. If a specific combination of requirements is met, an algorithm recommends a specific course of action. While there’s far more to machine-learning algorithms than just those statements, they—along with mathematical equations—serve as a core component in algorithm training.