What’s in a name? Terms like business intelligence, analytics, Big Data, artificial intelligence, and dashboards are thrown around in management meetings every day.
But nobody agrees on what they mean. So some people believe that precise definitions are vitally important. They regularly create rants along the lines of: “Without clear terms, nobody will know what you’re talking about! Everybody should use my definition! All the others are wrong! And all the confusion is the fault of vendors’ marketing!”
These polemics serve one useful purpose for the people who write them: They drive traffic to blog posts where people get to argue with each other in the comments section. (For example, check out all the reactions to my own post on Business Intelligence vs. Business Analytics — still on the list of my most popular posts, seven years later.)
But such arguments are a pointless waste of everybody’s time.
First, let me agree with at least part of the premise: yes, the terms are vague, and yes, confusion can be dangerous. But trying to come up with “the” definition isn’t going to help. The terms are vague—and always will be vague, because there’s considerable disagreement that no amount of discussion is ever going to fix.
More importantly, the definition of terms at an industry level is NEVER the real problem in a real-life implementation. So stop reading the blog posts and instead make sure that there are no misunderstandings when it comes to the goals of a particular analytics or dashboard or BI project. Be clear about precisely what information is going to be made available, and why, and how.
Replace the concerns associated with misused terms into a discussion of what you’re trying to achieve. For example, translate the arguments over whether it’s “really” a dashboard or not into a debate about whether the goal of the project is at-a-glance views of operations, or deep, holistic views of corporate strategy from multiple perspectives. Instead of discussing what Big Data means, talk about what new data sources are available and what architecture changes you might need to process that data.
Now—who disagrees with me, and why?
For more insight on the power of data analytics, see The “Purpose” Of Data.