During my time as a data analytics professional and data engineer, one thing that has always struck me as very interesting is the way that different businesses define the same terms. Things such as revenue, cost of goods sold, a lead, and a won opportunity can all be defined in numerous unique ways. In fact, one of the biggest pain points for a business is when folks use different definitions within the same business – this causes headaches and a lot of confusion.
I’ve found that the simplest and most effective way to combat this is to build a data glossary.
This glossary doesn’t need to be technical. It doesn’t even need to be pretty. But what it does need is a list of the terminology your business uses and its definitions. These terms should include everything in your reporting, and should absolutely contain terms used when creating goals for departments and individuals.
A case for clearly defined metrics, data assets, and terminology
What really is revenue? When an invoice is created and sent? Or is revenue captured when a payment is made? These are questions that your business needs to answer in order to properly report on the progress of goals and the success of your operations.
Defining these metrics and then sharing them with your business is not only going to help you and reduce questions about the data, but it will also encourage data literacy amongst your teams. This provides a few benefits: first, your teams will know how their work impacts the company at large. Second, it equips them with information necessary to make decisions and how those decisions might affect a goal or metric.
When a team member is able to clearly understand how a metric or goal is being measured, they are more likely to develop an innovative solution to the problems at hand. Knowing that revenue is generated when a payment is made will encourage team members to chase outstanding invoices in order to achieve goals.
Everyone needs to speak the same language
No two businesses are alike and neither are their definitions. As team members come and go, and your business shifts and pivots, you need to be very clear about how the business defines terminology when it comes to tracking performance and reporting.
I’ve found that sales teams struggle the most with terminology; what is a lead? When are they considered qualified? When are they considered converted? How do we measure conversion rates? These are questions that should be answerable with clear definitions in a data glossary.
As a business grows and begins to split into departments (and sub-departments and teams), keeping key business terminology in sync is the key to maintaining organization-wide focus on performance. Getting lost in the weeds discussing what a definition is or isn’t is completely avoidable.
Avoid discrepancy in metrics and analyses
When you clearly communicate what a metric means, how it is derived, and what filtering is being applied it becomes very difficult to manipulate those numbers. A classic saying in the data world:
Torture data enough, and it’ll tell you what you want.
I’ve seen businesses change their definitions year to year if the numbers don’t quite match their expectations. Even worse, they want to share not-so-accurate data with their investors and are happy to fudge the numbers through technicalities.
An example of this would be monthly reporting. Choosing between “Last 30 Days” and “Last Month” can and will result in different data. Each filter has their use cases, but without a clear set of standards and definitions for your reports, who’s to say you can’t change these around to make your data look as good as possible?
Leveraging a glossary will minimize the chance of manipulation and instill reliance in your reporting.
Interested in building your own data glossary? Grab a free template here.