Business Intelligence Designer Tasks Kentucky – What is the difference between a business glossary, a data dictionary, and a data catalog, and how do they play a role in modern data management?
When is it necessary to use a business glossary, data dictionary or data catalog? Although the terms sound similar, they are very different tools that can help your company manage and use your data strategically.
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Data is a critical asset for any business. We know. No matter the size of an organization (large, medium or small), its data is essential to making business decisions and remaining competitive. We also know that as the volume of data continues to grow, companies must make managing their data a priority if they want to understand what happened in the business, answer questions about why it happened, and make informed decisions in the future.
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Data management should be part of the overall business strategy so that everyone in the organization understands data and uses it in the same way. But where do you start? We recommend three tools that will help you stay organized and improve your data management strategy: a business glossary, a data dictionary, and a data catalog.
All three tools (business glossary, data dictionary, and data catalog) can help an organization better manage its data. Here is a list of pros and cons of each.
Although related, these tools are actually very different tools that your organization can use for different purposes. In this blog, we’ll define all three (business glossary, data dictionary, and data catalog) and discuss what it takes to build and govern each, as well as the pros and cons to consider.
A business glossary contains concepts and definitions of business terms frequently used in daily activities within an organization (across all business functions) and is intended to be a single, authoritative source of commonly used terms for all business users. It is the entry point for all organizations that have some type of data initiative in play. A business glossary is the common thread that connects business terms and concepts to the policies, business rules, and associated terms within the organization. When creating a business glossary, you must have:
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While you don’t need a data governance program to create, use, and maintain an enterprise glossary, you should still have a governance strategy for the enterprise glossary itself. To achieve cross-functional consensus, you need stakeholders from across business functions whose responsibility it is to meet regularly to discuss terms and concepts that might overlap departments. This will allow for approval and documentation of definitions, which is important, especially if two departments define the same metric differently. It is okay to have two different definitions as long as stakeholders have verified that it is an acceptable deviation and it is documented and accessible to business users who need it. In some cases, there may be a tie-breaker, such as a CEO, who chooses one definition over the other.
Once the business term or concept is defined and approved, designated stakeholders must ensure that the definition is used consistently throughout the organization. An enterprise glossary is a key artifact for any data-driven organization and will help establish future data initiatives as the company’s analytical needs mature. Here’s what to consider when creating a business glossary:
As noted above, a business glossary is the starting point for any data initiative, but it is also a prerequisite for creating a data dictionary.
Alation’s business glossary enables the creation of definitions, policies, rules, and KPIs through a rich, easy-to-use interface. A business glossary can be started with Microsoft Excel or Google Sheets to start the process and ensure it works correctly. Photo: Alation
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A data dictionary is a more technical and comprehensive documentation of data and its metadata. It consists of detailed definitions and descriptions of data dimensions and measure names (in databases, data tables, etc.), their calculations, their types, and related information. While with a business glossary you provide definitions of terms and concepts, in a data dictionary you provide information about the type of data you have and everything that is related to it. This information is often useful to technical users working on the backend of their systems and applications so that they can more easily design a relational database or data structure to meet business requirements. When creating a data dictionary, you should have:
Watch iFit’s CTO talk about how having a data dictionary empowered their data teams and eliminated data engineering bottlenecks:
Unlike a business glossary, a data dictionary will likely require you to have a more formal data governance program with a governance committee made up of people from both the business and IT sides.
The business team should be responsible for requesting changes to the definition of a metric, while the IT team should be responsible for implementing the change and communicating it to the organization. Establishing lines of communication between the two groups will promote trust. Here’s what to consider when creating a data dictionary:
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A data dictionary is a subset of a business glossary, but both are necessary to create a data catalog.
Whether your data is stored in a data warehouse, data lake, or lake house, running dbt docs will propagate table and column definitions to create an automated data dictionary. dbt font
A data catalog is the path (or bridge) between a business glossary and a data dictionary. It is an organized inventory of an organization’s data assets that informs users (both business and technical) about available data sets on a topic and helps them locate it quickly. Users have a clear, accessible view of what data the organization has, where it comes from, where it is now, who has access to it, and what risks or sensitivities may be involved, all in one central location. When creating a data catalog, you must have:
In terms of governance, you should follow the same structure as with a data dictionary. However, you should have another committee (a subset of people) made up of people who have technical and business competencies working alongside the data governance committee created for a data dictionary. The best way to maintain a data catalog is to integrate it as naturally or intuitively as possible with the existing processes in place, for example, every time a new data source is added, updating the data catalog should be part of any process that is underway. place to do that work.
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A data catalog is an organized inventory of data assets and provides insight into all aspects of metadata. Users can access a data catalog without accessing the data asset itself. This helps save time and improves employee productivity, as well as promoting data transparency and trust.
Although the terms (business glossary, data dictionary, and data catalog) seem similar, they serve very different functions within your organization. Each of them is valuable, but not completely necessary for every organization, at least not immediately. It depends on where you are with your analytical maturity and how much time and resources you have to dedicate to building and maintaining each artifact. As you consider your options, start with:
Christina Salmi Christina leads the data strategy service line, helping our clients think and act strategically on data and analytics. Do you want to develop a data strategy but aren’t sure where to start? Don’t get caught in an endless cycle that leads nowhere. The key is to start with a data strategy assessment so you can build a data strategy roadmap to success.
In this blog, we describe what a data strategy assessment is, how to approach it in three steps, and how it ultimately leads to a data strategy roadmap. We also provide examples of successful data strategy evaluations.
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All successful and thriving companies have one thing in common: they are able to make sense of their data and use it strategically to transform and drive their business. But how do they do it? It’s simple: they have a defined data strategy that acts as the foundation for their data and analytics practices.
A good strategy is more than just data and technology: it’s a defined plan that outlines the people, processes, technology, and data your organization needs to achieve its data and analytics goals. A good data strategy answers exactly what you need to use data most effectively; what processes are required to ensure data is high quality and accessible; what technology will allow data to be stored, shared and analyzed; and the data required, where it comes from and whether it is of good quality.
Before you can answer any of these questions and develop a successful data strategy, you must start with a data strategy evaluation.
A data strategy assessment is an in-depth assessment of various factors within your organization that impact the quality of your analytics and your ability to make data-driven decisions. During an assessment, you review where you are today, map out where you would like to go, and develop a plan for how to get there.
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The goal: At the end of an assessment, you will have a defined data analysis.
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