{"id":38738,"date":"2025-07-18T09:04:19","date_gmt":"2025-07-18T07:04:19","guid":{"rendered":"https:\/\/www.oneword.de\/?p=38738"},"modified":"2025-10-06T13:34:37","modified_gmt":"2025-10-06T11:34:37","slug":"cleaning-up-terminology-databases","status":"publish","type":"post","link":"https:\/\/www.oneword.de\/en\/cleaning-up-terminology-databases\/","title":{"rendered":"Terminology database: from uncontrolled growth to valuable resource in five steps"},"content":{"rendered":"

25\/06\/2025<\/small><\/p>\n<\/div>

Terminology database: from uncontrolled growth to valuable resource in five steps<\/h1><\/div>

A well-maintained terminology database is the foundation for consistent and cost-efficient corporate communication. But in practice, it’s often a different story: databases that have grown over the years, merged lists from different departments and inconsistent or missing standards lead to a proliferation of data that is difficult to utilise. Therefore, cleaning up these databases systematically pays off directly in terms of time and cost savings in text creation, translation and communication.<\/p>\n<\/div>

Read more here<\/span><\/i><\/a><\/div>
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The challenge: Confusion in terminology databases<\/h4><\/div>
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Terminology management plays a key role in the company, as a well-maintained terminology database ensures that technical terms are used consistently across all departments, documents and languages. It serves as a central reference for text creation, is integrated into authoring systems and translation tools and forms the basis for high-quality machine translations.<\/p>\n

However, as soon as terminology management begins, more and more entries tend to be added. Different databases are merged, and new terms are extracted<\/a> from documents and defined during product development. This results in uncontrolled growth, especially if standards are only established late in the process.<\/p>\n<\/div>

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The four main causes of unclean terminology data:<\/h6><\/div>
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  1. The database takes over: More and more language data is continually accumulated.<\/li>\n
  2. Lack of clean-up routines: The active collection of data is not accompanied by regularly checking and cleaning up the data.<\/li>\n
  3. Inconsistent procedures: There are no uniform standards for filling and maintaining the database.<\/li>\n
  4. Several editors: Different people, departments or service providers work on the database, which can mean that no one has a clear picture of the whole thing.<\/li>\n<\/ol>\n<\/div>
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    The five-step approach to systematically cleaning up your data<\/h4><\/div>
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    To counteract these causes, terminology databases should be cleaned up in a structured way. A funnel approach has proved successful here: the quantity is first reduced by eliminating superfluous entries, and then the details are worked on.<\/p>\n<\/div>

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    \"Cleaning<\/a><\/span><\/div>
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    A top-down funnel approach to cleaning up terminology (source: oneword GmbH)<\/em><\/p>\n<\/div>

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    Level 1: Reducing the volume of data by analysing the database<\/h6><\/div>
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    Therefore, the first and most important step looks at the number of entries. Analyses from our own projects show that, in some databases, only 20 to 30 per cent of the terminology is active. The proportion of inactive terminology is often alarmingly high, especially in the case of large databases with over 5,000 entries.<\/p>\n

    Therefore, it makes sense to perform a database analysis to determine what proportion of the database is active. The aim of this analysis is to separate the terminology actually used from unused information. To do this, we compare your terminology database with current corporate texts \u2013 for example, with document collections from various divisions and departments or with translation memories<\/a>. For each term from the database, we check whether and how often it occurs in the reference texts. The analysis not only shows which terms are actively used, but by determining the frequency, also makes it possible to prioritise the database for subsequent steps, such as definition or glossary creation.<\/p>\n

    For small databases, this analysis can be carried out manually or using the search function. However, extensive databases should be analysed using scripts. The result also identifies inactive terminology, i.e. terms that do not appear in the text corpus and are therefore not used in the company. As there is usually a great deal of scepticism around deleting data once it has been created, it is advisable to mark the terms or entries as “inactive” as a first step. This enables active and inactive data to be displayed separately and specifically filtered. If a term is then used after all, it can be reactivated by removing the label. After a defined period of time, inactive entries can and should be permanently deleted.<\/p>\n<\/div>

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    Level 2: Cleaning up the structure to assign information clearly<\/h6><\/div>
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    The structure of a terminology database must provide a unique location for each piece of information. There are separate entry, language and term levels where fields and information are added.<\/p>\n

    The following questions can be asked to check the structure:<\/p>\n