Business Glossary vs Data Catalog: What’s the Difference?
Today, data fuels good business decisions. That, in turn, can positively impact customer experience, revenue, and competitive advantage. If you don’t have the right data quality and data governance mechanisms in place, your business users will lose confidence. Data catalogs play a critical role in your organization’s ability to establish and maintain trust in your data. You often hear the terms “business glossary” vs “data catalog” used somewhat interchangeably. In fact, there are some similarities, yet the two are fundamentally different.
What Is a Data Catalog?
A data catalog is a repository of metadata that provides an inventory of all the data assets within a specific scope, regardless of its location or source. The data catalog includes information about key elements such as name, description, type, size, schema, and other relevant attributes.
The primary objective of a data catalog is to locate and leverage metadata, data, models, and data elements within an organization to extract business value. It supports data quality because it helps to identify redundancies, inconsistencies, and other anomalies. It also provides a foundation for elements of data management such as data stewardship, data lineage and impact analysis.
What Is a Business Glossary?
A business glossary is a repository of business terms and definitions. It provides a collection of terms and definitions used by a specific organization and its industry. The business glossary includes information about business terms such as name, description, type, and other relevant attributes that may require subject matter expertise to fully understand.
The primary goal of a business glossary is to build alignment between business and technical use – or even between business users that have different interpretations of common terms. By providing a common language for discussing data and business concepts, the business glossary helps reduce confusion and ambiguity. It also enables data governance processes such as data stewardship and data lineage tracking.
A business glossary vs data catalog is focused on common terminology, a data catalog provides a foundation for much more. It’s the first step toward creating a structured approach to data quality and data governance that creates a foundation for confidence in business analytics.
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Why Is a Data Catalog So Important?
Companies are processing and storing higher volumes of data than ever before. They’re also grappling with a diversity of software systems that can make it difficult to understand an organization’s data holistically. A data catalog brings order to that chaos by mapping the data landscape and providing a kind of universal translator for the people who use that data to drive key decisions.
A data catalog answers these key questions: What data do I have? Where is it located? What are the attributes in this data set?
A data catalog vs business glossary provides a central repository for an organization’s metadata. This includes where the data is located, how and when it was created, and how it is used. In addition, a data catalog can help to assess the quality of data by providing transparency into its origins and lineage. This visibility helps build trust in the data, which is essential for making decisions based on data. A data catalog can help organizations better understand and manage their data assets by addressing these three key questions.
The data catalog also plays a key role in maintaining high levels of data quality. In this respect, a data catalog plays a critical role in overall data integrity.
Start with Clear Business Objectives
Data governance must serve a purpose and be aligned to clear business objectives. It’s not about data management for its own sake. Rather, it’s about improving business processes and decisions. A data catalog is one part of a broader data governance framework that must also define roles and responsibilities, set standards for data quality, and establish processes for data management.
To illustrate how a company might use its data catalog, imagine your company is aiming to improve its customer experience. Here is what a five-step process might look like to determine the metadata, goals, and metrics that you would want documented in your data catalog:
Step 1: Define the desired outcome. What decisions do you need data to help you make? What questions might you be able to answer by using data more effectively? In this case, you might want to better understand the demographic profile of your customers. That, in turn, might reveal opportunities for developing brand messages that speak more directly to your target audience.
Step 2: Discover what customer data you have and need. This step is all about data discovery. Following the example, you need to understand what you already know about your customers, where that information came from, and how it’s currently being used. You might also identify opportunities to add value with data enrichment or location intelligence. Demographic enrichment can tell you more about key lifestyle characteristics of your customer base. Mobile data can reveal important consumer behaviors that help you cater your products and messages to specific target audiences.
Step 3: Locate the data you need. This is about data acquisition and data integration. Identify the data sets you need, then implement reliable, scalable systems to integrate and harmonize the data from different sources. For customers, that might entail integrating transactional data from ERP, service and support information from CRM, clickstream analytics from the web, and third-party demographic data.
Step 4: Verify if the data is usable and trusted. Check for completeness, accuracy, and consistency. Completeness means that all relevant data has been included, and nothing is missing. Accuracy is about the correctness of the data. Consistency means that the data is logically congruent and does not include contradictory information. Customer data is notorious for its tendency to degrade over time as people relocate, undergo name changes, or pass away. Data quality must be proactive, ongoing, and must be supported by the right data quality tools.
Step 5: Assess your business outcomes. The final step is to determine whether or not your business users are successful in achieving the desired outcomes. If not, why? Were you asking the wrong questions? Were certain data elements missing from the picture? Were there data integrity problems that weren’t identified and proactively addressed previously?
A data catalog serves as a single source of truth, providing a comprehensive roadmap to all the data your organization owns and uses. Every effective data quality program and data governance initiative is built upon a clear understanding of that roadmap. This is why a good data catalog solution so critical.
To learn more about Precisely’s data integrity solutions, including our data catalog capabilities, download our free ebook, Looking for a Data Catalog?