Automated Business Glossary: Ensuring company-wide consistency in seconds

Whether you’re on the business side of your organization or the IT side, you know that a Business Glossary is key to speaking a common data language. Problem is, building a Business Glossary is a HUGE a huge project that no one wants to take on. Automation changes all of that.

The Business Challenge

Imagine working for a company where every department—and sometimes groups within departments—speaks a different language. We’re not talking different geographies here, we’re talking about groups in the same building.


It would be chaotic, wouldn’t it? Every cross-functional meeting would need the support of an army of translators. Meeting minutes would be dozens of pages long to accommodate a version in every language. And even then, there would be misunderstandings and poor decisions.


This is actually what it’s like in many organizations today. Sure, all departments might technically speak the same language, but some important business terms have different meanings to different people, and sometimes different terms are used to represent the same idea. A term like “adjusted gross margin” might mean different things in different reports (or even in the same report).


This is why every business needs a Business Glossary, to make sure everyone in the organization is on the same page.

The BI Challenge

The most important business terms describe things that can be measured or have a number attached to them, so it makes sense that a good starting point for a business glossary is a company’s reports and data assets. Thus, it usually falls to the BI team to start building the business glossary.


The trouble is that organizations rarely end up building a business glossary as it can be an enormous undertaking, especially in medium to large organizations with complex, diverse data environments. It simply requires more time and resources than the BI team can spare and still get their jobs done.


How BI Worked Before Octopai

Before Octopai’s Automated Business Glossary became available to BI teams, the exercise of building a business glossary looked something like this:


1. The BI team would start by listing terms and definitions in a document, most likely a spreadsheet or some other tool, such as a wiki.

2. Months of work would ensue. Every term in every report and data asset had to be examined to determine its meaning. Relationships among different terms had to be identified and documented. The exercise involved conducting interviews and digging up old design documents (if they existed).

3. Because the process was manual, errors were made. Subtle differences in meanings were missed. Overlaps in meaning among different terms were left unresolved. Calculations and transformations were recorded incorrectly.


After all that work, the endless meetings, and the frustrating searches down myriad rabbit holes, the organization was not much closer to a unified business glossary than when they started.

How Octopai Empowers BI Groups

Octopai’s Automated Business Glossary puts an end to this tedious, time-consuming, and error-prone process. The grunt work is eliminated by automatically finding and linking related terms and accurately recording transformations and calculated columns. With an Automated Business Glossary, you can forget about the concept of “building”  a business glossary, as it is generated automatically with the enterprise’s metadata, without endless manual data entry.

Value to the Organization

A solid business glossary provides a single source of truth to the organization regarding company-standard terminology so that everyone is on the same page and miscommunications and misunderstandings are minimized. But with an automated business glossary, the enterprise in general and the BI team, in particular, realize significant benefits, such as:


– Time and money savings by eliminating manual processes. The business glossary is automatically built saving several team members months of tedious work.

– A standard process that can be repeated any time new data sources are added, old ones are retired, or existing ones are modified, or any time business conditions warrant.

– More accurate reporting, because eliminating manual work reduces errors completely. And more accurate reporting leads to smarter business decisions.

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