AI vs Traditional Analytics - What's the Difference and When Should You Use Each?

The best way to explain traditional analytics is like looking in the rearview mirror. But AI-powered analytics on the other hand? That’s your navigation for helping you to see what lies ahead.

But the question is, when should you be looking in the rearview, versus looking ahead, and when?

Let’s start with traditional analytics, which by the way, are great for reporting, but not predicting.
This is the type of data that most businesses already know and use to work out, what happened, when it happened and who did what.

For example, traditional analytics will show you that your sales dropped 10% last month. You can then usually  track which products sold, how your ads performed, and how your audience responded. These analytics do exactly what you need for reporting results and understanding the past.

The downside? you have to ask the right questions and rely on human interpretation. And of course it can take a lot of time to build dashboards, write SQL queries, and manually dig through data to find what you need.

When Traditional Analytics Works Best

Traditional analytics is ideal when you need clear, explainable results, when you’re reporting to stakeholders, when you want full control and transparency or when you’re dealing with compliance or audits.