{"id":545070,"date":"2025-07-14T06:40:47","date_gmt":"2025-07-14T06:40:47","guid":{"rendered":"https:\/\/www.capgemini.com\/au-en\/?p=545070&preview=true&preview_id=545070"},"modified":"2025-07-24T06:44:32","modified_gmt":"2025-07-24T06:44:32","slug":"genbg-how-to-generate-an-effective-business-glossary","status":"publish","type":"post","link":"https:\/\/www.capgemini.com\/au-en\/insights\/expert-perspectives\/genbg-how-to-generate-an-effective-business-glossary\/","title":{"rendered":"GenBG \u2013 How to generate an effective Business Glossary"},"content":{"rendered":"\n

GenBG \u2013 How to generate an effective Business Glossary<\/h1><\/div><\/div><\/div><\/div>
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Ralf Teschner<\/h5>
Jul 10, 2025<\/h5><\/div><\/div>
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Customer Status. Supplier Type. Asset Value. Employee Date of Birth. Accounts Payable\u2026 Every large organization has tens of thousands of such data elements, even if less than 5% of those should be considered as \u2018critical\u2019.<\/p>\n\n\n\n

It is for these Critical Data Elements (CDEs) that you need detailed business term definitions, typically of a page long. This provides clarity on their meaning, avoids confusion and drives consistency across departments, systems, countries and business units. Use cases include transformation programmes, cloud migrations, data monetization projects, management dashboards, data migrations, agile decision-making, and data mesh\/fabric ambitions.<\/p>\n\n\n\n

But developing such detailed definitions is fiendishly difficult. Why? Because you need to agree on common terminology and taxonomy for all affected systems and teams. You\u2019ll need deep understanding of the business and IT landscape. You\u2019ll want to capture current data issues that require resolution. You\u2019ll need editing skills that pulls all this together concisely. And you better have strong diplomatic skills to facilitate compromise formulas. Most organizations therefore do not have a high-value, effective Business Glossary.<\/p>\n\n\n\n

The Top 11 Myths around Business Glossaries<\/strong><\/p>\n\n\n\n

1. \u201cA business term definition is just a paragraph! And you can find it on Google, or from the SAP\/Oracle\/Azure data model!\u201d<\/em>
If only it was that simple. A short sentence may be enough for simple concepts such as \u2018Postcode\u2019. But a single-sentence approach is insufficient for CDEs that are core to your primary applications, sit at the interface to other key systems, feed into management KPIs, often have a prescribed list of values or suffer from data quality issues.<\/p>\n\n\n\n

2. \u201cA Business Glossary (BG) is the same as a Data Dictionary (DD).\u201d<\/em>
Not true. There\u2019s a DD for each system, listing every single field with lots of technical details. The BG is enterprise-wide, provides the business context, focuses on CDEs only, and effectively sits on top of all DDs.<\/p>\n\n\n\n

3. \u201cOnly the department entering the data needs to write the definitions as they know best.\u201d<\/em>
But what about all those departments that are heavy users of this data. They are heavily affected by how the data is described, so should have a say in its definition.<\/p>\n\n\n\n

4. \u201cA BG is just a list of acronyms.\u201d<\/em>
Many long policy documents end with a list of all the abbreviations used in the text and then call it a Glossary. But that\u2019s insufficient for a wide variety of BG users.<\/p>\n\n\n\n

5. \u201cExcel is fine.\u201d<\/em>
An effective BG allows for Wikipedia-style links between definitions, policies, systems etc. to show their relationship. A BG tool is far more effective in governing definitions, auditing their history, sustaining its content and communicating it enterprise-wide. Excel can\u2019t reliably do any of this. You need a tool, which typically comes as part of a Data Catalog.<\/p>\n\n\n\n

6. \u201cA BG is used only by business folks.\u201d<\/em>
We find an almost even split between Business and IT stakeholders in using the content of a BG, including Data Analysts, Business Analysts, Data Architects, Data Engineers, Data Stewards and Data Scientists.<\/p>\n\n\n\n

7. \u201cA definition is needed for every single data element, so also for the 95% of data elements that are not CDEs.\u201d<\/em>
You can, if you want, insert a definition for House Number, Transaction Currency, or Credit Card Expiry Date, but there is very little value to it, so nobody would look for a definition for such obvious concepts.<\/p>\n\n\n\n

8. \u201cGenAI will take care of it.\u201d<\/em>
If only. But there are two major ways where GenAI can indeed help build your BG. More on that later.<\/p>\n\n\n\n

9. \u201cData Quality and data policies have nothing to do with a BG.\u201d<\/em>
Well, if you want to measure data quality, you need to know what exactly to measure it against. And your data policies, processes and procedure documents make frequent reference to core data elements, so readers of these policies need to understand exactly what these concepts mean.<\/p>\n\n\n\n

10. \u201cA definition is a passive document.\u201d<\/em>
On the contrary, a well-composed definition usually provides several prompts for change actions, e.g. improving data quality, aligning terminology between departments, standardizing the list of values between systems, installing additional data entry controls etc.<\/p>\n\n\n\n

11. \u201cWe can call it Data Glossary, Business Dictionary, Data Lexicon and data definitions.\u201d<\/em>
Try not to make up your own language. Standard worldwide industry lingo says they are called a Business Glossary and business term definitions. So it\u2019s best to stick with the norms used by all tool vendors, Gartner, consultancies, partners, regulators etc.<\/p>\n\n\n\n

What makes a good business term definition?<\/strong><\/p>\n\n\n\n

So what should a good definition contain? Obviously a high-level synopsis sentence that is good enough for the superficial user. Then sample data values, or even the full list of values if it\u2019s a reference data standard with a drop-down menu. The definition should describe exceptions as well as synonyms and homonyms. It must show the business lineage (if this hasn\u2019t already been created by your Data Catalog).<\/p>\n\n\n\n

And then there are a whole range of critical questions to answer:<\/p>\n\n\n\n