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A data governance business glossary is an essential data literacy tool and crucial for understanding the data in your organization and undertaking effective analytics. Without a business glossary, companies are often overwhelmed by the sheer number of conflicting terms and definitions used. When there is no standardization, organizations will encounter hurdles that impede critical business processes, across the board.
A business glossary enables users to find common terms and definitions, collaborate more easily on data assets, and move forward fluidly with data-driven growth initiatives. It’s not easy to build a business glossary, and you’ll need a dedicated tool to help you with the process, but by the time you finish this article you’ll have a clear vision on how to progress.
Read on to learn how to create a business glossary and implement the resource into your organization using the right technology for your needs.
A business glossary pulls together data-related terms and definitions and displays them clearly and logically so everyone in an organization can access them. Standardization is one of the most significant components of data literacy and is the key driver of developing a business glossary in any organization.
The trouble is, it is easy to come up with a definition but difficult to coordinate with multiple individuals and departments to establish common terms from the get-go. That's why individual users and departments tend to adopt conflicting terms.
Over time, as more and more terms are used, a company’s data assets become very difficult to navigate, making it almost impossible for users to collaborate and innovate using data assets they are unfamiliar with. A business glossary clears up this confusion by introducing standardized terms and definitions that everyone in an organization can understand.
Although a business glossary has the core goal of standardizing terms in an organization, there are many elements and use cases that constitute it. These include:
However, it is vital to note that every organization will have specific use cases that they wish to fulfill by implementing a business glossary. Even if you only identify with one or two of the elements we have covered above. The likelihood is, there will be more that are specific to your company.
Business Glossary is often confused with, but NOT…
Many users may find it difficult to distinguish between a business glossary and a data dictionary. However, although the two are similar, they are not the same resource.
In short, the role of a business glossary is to define terms so users can easily identify and collaborate using them. On the other hand, a data dictionary is designed to enable the smooth operation of databases by setting and enforcing various data standards, documenting origins, formats, and relationships.
Some users may also confuse a business glossary with a data catalog. Again, although there are some similarities, there are significant differences too. Essentially, a data catalog supports the creation of a business glossary.
As we will cover in greater detail later on in this article, a data catalog crawls all of a company’s data sources and consolidates these data assets in a centralized, searchable database. It is this consolidation that makes it easy for data teams to find conflicting terms and definitions.
There are several crucial business benefits to establishing a business glossary in your organization. Below are the most important points.
With an active business glossary, users can quickly and easily find definitions for terms that make it easier to understand data sets. For example, if a business user needs to access a report from another department that includes terms unknown outside of the department, they can look up the term in the company’s business glossary.
Imagine if a business user from R&D wants to access financial records in order to streamline costs and make their annual budget stretch further. If they access data held by the finance department there are likely to be many terms used that are uncommon to them, making the task of deciphering the data almost impossible. When these terms are standardized and listed in a business glossary the whole process is made far simpler.
Effective communication is crucial for data-driven innovation. A business glossary makes it easy to communicate with other departments whilst avoiding confusion about particular data terms.
For example, in the healthcare sector, there is no standard definition to calculate the “length of stay” in hospitals, something every hospital reports in their annual balance sheet. When every hospital collects data differently and calculates the length of stay differently how can this data be aggregated?
This point makes it very difficult for mergers to take place. One hospital might define the length of stay as the moment a patient enters the hospital to when they leave, while another hospital might define it as the time spent on a particular ward, or from the first meeting with a doctor.
A business glossary addresses operational issues from the top-down. Without this tool in place, deciphering conflicting terms can be incredibly time-consuming, expensive, and impactful on every department.
When terms are standardized, the inconsistencies diminish and everyone can access and use data more efficiently.
This alleviates pressure from the data team because they aren’t responsible for working out the relationships and definitions of conflicting data terms. In turn, the day-to-day operation of a business is streamlined, from a data perspective.
When users trust data they are more likely to use it to make better, more productive business decisions. However, when there are conflicting terms used in an organization, users not only avoid accessing data assets, they fail to understand what they are actually accessing.
A business glossary clarifies these issues and also takes the pressure off data teams because business users can access this information independently through self-service. The result is productivity gains, company-wide.
For example, if a user decided to access customer data related to their department and make a concerted effort to improve service, they may well be put off when the data they find is full of conflicting information. Standardizing terms makes it easier for users to become data literate because the information they need is clearly defined. The result is more innovation using company data assets.
A key aspect of data governance is identifying data owners and establishing responsibility for data assets. This has several critical outcomes. When you know who data belongs to you can contact data custodians directly and gain access to the data more quickly.
When users know they are responsible for data they can work to ensure its quality on an ongoing basis, which is ultimately beneficial for an entire organization. Thirdly, establishing data ownership makes data assets easier to categorize and organize. A business glossary enables you to document data owners quickly and easily and update any changes to this ownership status.
The first step to building a business glossary is to ask if multiple terms are in use. The ideal scenario is one where standard terms already exist, but this usually not the case. When different departments are using different terms and definitions to explain the same thing, a bottom-up approach is required.
Moving forward, whenever a new data element is added, it should be signed off by the governance group who will create a standard definition.
If you have no existing data terms and definitions in an organization, you can adopt a top-down approach to building a business glossary. When there are no conflicting terms and definitions in use, the process of building a business glossary is far easier.
Unfortunately, most organizations are not in this position. There are likely to be different terms in use, especially if the company is going through a period of merger and acquisition.
By far the best way to implement a business glossary is through the use of an automated data governance tool. In fact, in organizations that must commit, as most will, to a top-down implementation approach, the process is almost impossible if you fail to do so.
The minimal costs of implementing a data catalog far outweigh the time and money spent attempting this rigorous process manually.
A comprehensive data catalog, like OvalEdge, includes all the tools you require to set up a business glossary in your organization. It uses AI to find, index, and summarize all of the data assets in your organization so when it comes to building a business glossary you have all of the information you need at your fingertips.
The role of adopting a data catalog is best left to the data governance group. The data catalog crawls all the reports in an organization, finds the terms, and catalogs them, whilst also building relationships between conflicting definitions.
Using a data catalog, you can quickly identify the core data owners and stakeholders in your organization. Without a data catalog, the process is unlikely to work because conflicting terms are created and updated continually.
Building a business glossary is essential if you want to encourage data-driven decision-making across your organization. Usually, there will be multiple terms and definitions in use, and the process is somewhat complicated. That’s why it’s essential to initiate a data catalog to make the process run more smoothly.
The responsibility of creating a business glossary falls to your data governance group. Because of this, the people you choose to join the group must be representative of stakeholders from across your organization. One of the most significant barriers to implementing a successful business glossary is individual or departmental bias. For this reason, you must have a data governance group that represents everyone in your organization.
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