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What Is a Data Dictionary? Definition and Benefits - DATAVERSITY
A data dictionary describes data in business terms, including information about the data. It includes elements like data types, structure details, and security restrictions.
This support references high-quality metadata that describes data platform attributes and their relationships. Engineers and other workers use this information to build, troubleshoot, maintain, and improve a data solution’s foundation.
As a source, data dictionaries document a physical data model covering how a technical entity works. That way, engineers understand how to integrate its components better.
As the data platform code changes, many data dictionaries update and align with these changes, leveraging automated tools. Changes can include at least one of three categories, as classified by The International Organization for Standardization (ISO). They include:
Business Concepts: Entries with business semantic meaning, including
Associations
Components
Constraints
Elements
Roles
Data Types: Unambiguous specifications about the valid values of a business element or a message element
Message Elements: Dictionary items used in message definition including:
Message Components
Constraints
Message Elements
See the figure below for a conceptual structure with data dictionary business concepts, data types, and message elements. It also includes the relationships among all three components.
The conceptual structure of the data dictionary courtesy of the ISO.
Visit the ISO site for more details on each component and its meaning.
Data Dictionary Defined
Many alternate definitions frame data dictionaries as useful metadata for business and technical purposes. The US Geological Services (USGS) considers data dictionaries metadata storage and communication tools about data in a database, a system, or data used by applications. They clarify business construction, such as a list of database names and definitions, and the technical pieces, such as the data types of these constructions.
To dive deeper, the UC Merced Library describes the metadata in the data dictionary as a collection that includes different data elements that a database acquires or uses. The National Library of Medicine narrows metadata in data dictionaries to a variable’s content, structure, and meaning. This information expands on what values are collected, allowed, and specified.
Splunk and data.world refer to standardization as an important aspect of data dictionaries, essential for data analysis and reproducibility. As structured repositories, data dictionaries provide a common language. This advantage simplifies the contextual understanding around each data point.
As data dictionaries collect useful metadata and standardize communication around data, they function well as a reference guide on a dataset. Like any source, a data dictionary works best when it addresses the technical problems the organizations want to solve.
How Do Data Dictionaries Differ from Data Catalogs?
While data dictionaries and catalogs overlap in their contents and definitions, they serve different purposes, audiences, and focuses. Data dictionaries provide technical instructions to build, update, use, and maintain data architectures. This information is most relevant to engineers who do activities like integrating datasets between systems.
A non-technical businessperson would find a data dictionary cumbersome with details irrelevant to their questions. So, data catalogs, while built off data dictionaries, present a user-friendly interface that makes it easier to search and retrieve relevant data sets. For example, a business user may use a catalog to locate datasets about coffee consumption in the northeast of the USA.
Is a Data Dictionary the Same as a Data Model?
While a data dictionary is a type of model – a physical data model – it does not mean the same thing as a data model. Data models diagram document different aspects of a data solution for different purposes.
Conceptual data models describe business needs at a high level, defining the database’s structure and organization. Logical models cover how to meet those requirements. The physical data model describes the technical implementation to meet the requirements.
A data dictionary is only one type of physical data model. Entity relationships, JavaScript Object Notation (JSON), and flow charts may represent a physical data model.
Unlike other physical data models, data dictionaries are more comprehensive. Dictionaries go beyond attributes and activities to describe the type, format, and mandatory values of each entry in the database system.
Key Data Dictionary Benefits
Organizations need data dictionaries to get a shared understanding of their metadata and the system implementation of their data solutions. This standardization helps direct discussions on clarifying technical terminology, so it bridges with what the business needs.
Moreover, the data dictionary ensures efficient Data Architecture engineering. It accomplishes this goal by aligning any fixes and improvements to the original design and purpose. The last thing a company wants is a string of fixes and updates that leave a trail of confusion about what code changed and the reasons for those changes six months later.
The tool reduces Data Management and engineering redundancies that occur down the line when issues arise, get fixed, reoccur later on because of other fixes or updates, and then have the same fix applied as the first time.
Companies gain Data Quality benefits with their data dictionary when it’s used and updated from one place. Furthermore, they have an easier time improving and making future data infrastructure decisions when researching from a standardized dictionary version.
What Is the Function of a Data Dictionary in Data Governance?
A data dictionary informs Data Governance (DG) — the activities that formalize technical data roles and processes and handle metadata management. Details about business concepts, data types, and message elements suggest technical stewards, formalized roles accountable and responsible for critical technical metadata.
Moreover, data dictionaries show data lineage, where data entities originate, get transformed, and arrive. With precise technical and business metadata details, dictionaries provide a crucial foundational component of a data catalog, informing its selection, needs, and use.
Simultaneously, a data dictionary relies on Data Governance processes and activities for the Data Quality to make it a valid reference. Data Governance solidifies what data dictionary version is the standard current one, where to find it, who or what systems can update it, and who has access to what sections.
Also, Data Governance gives authority to data dictionary access, security, and other compliance components. DG services ensure system updates and changes, as the data dictionary reports, align with business changes.
The Evolution of Data Dictionaries
Data dictionaries came with creating the first database management systems (DBMS) in the 1960s. Organizations created them to know what and how their data was structured.
These references started as manual tools on paper, a hard copy, or in some static format, like a word processor or spreadsheet. The 1990s saw the beginning of automated functionality within data dictionaries.
Around 2020, data dictionaries started using Machine Learning to identify patterns among data elements from different systems and enhance functionality. As data dictionaries become more sophisticated, generative AI automatically enriches technical metadata.
Consequently, the context around the metadata provided by data dictionaries may be different. For example, some larger financial institutions use mainframe systems from legacy 1960s development. So, deep diving to understand data elements and their lineage may require locating a hard copy reference in a back-office or accessing details through a command line interface.
Types of Data Dictionaries: Active and Passive
Data dictionaries come in active and passive forms.
Active Data Dictionary: The DBMS typically offers an active or integrated data dictionary. This reference automatically updates as changes are made to each piece of data, providing the most up-to-date data definitions. Gartner describes the active data dictionary as a dynamically accessible and modifiable information storage.
IT usually manages this kind of dictionary because its interactive interface requires more advanced technical knowledge. Engineers use this tool to explore data structures and to ensure accuracy and consistency across the database.
An active data dictionary prohibits code executions by either a person or a system that compromise data integrity. For example, developers would find a warning or an error if changing the name of a critical attribute.
Additional automated features allow users to interact and perform data operations. Technicians use this functionality carefully to keep the database operation and structure intact.
Passive Data Dictionary: A passive data dictionary is a metadata reference where updates and maintenance happen outside the DBMS. This kind of tool requires manual intervention to keep it up to date.
Users access passive data dictionaries through an application with a friendly user interface or a static document, like a PDF or a binder full of paper. Organizations may create passive data dictionaries before starting a new database or system to communicate what to develop.
For example, a city may mandate an inventory of all surveillance equipment each bureau uses for transparency. Since no technical system exists, city leaders must start from scratch and write a proposal, including a data dictionary, to build it.
Typically, organizations do not use a passive data dictionary as a sole source of truth. Since updates in a passive data dictionary are manual, there could be a significant lag in reflecting the changes.
This situation happens because the responsible person may not have the time to update the dictionary immediately after a change is implemented. This delay can lead to discrepancies between the dictionary and the current state of the data.
Businesses Use Data Dictionaries to:
Ensure agreement between the business-facing content and technical-facing physical data
Reduce the risk of downstream errors and rework
Provide valuable reports and dashboard components
Assure smoother database upgrades
Guarantee more meaningful metadata
Data Dictionary Use Cases
The USGS documents its data dictionary and provides public access to promote sharing of its common data structures. This activity allows groups working with similar data to refer to the same elements, fostering collaboration and efficiency.
Medicare data dictionaries play a crucial role in communicating information about patient deaths. Beneficiaries and researchers analyze the data to identify patterns among those with chronic conditions and improve the outcomes.
Developers find data dictionaries invaluable for new functionality or troubleshooting fixes. By utilizing the dictionary, programmers gain a better understanding of a variable, its relationships, and valid values. This knowledge improves efficiencies and reduces errors during software delivery.
MicroStrategy’s data dictionary includes performance metrics and objects related to its intelligence server. This resource assists with troubleshooting performance issues and finding solutions to optimize server execution, ensuring efficient data processing.
The American College of Surgeons (ACS) created a National Trauma Data Standard (NTDS) data dictionary to standardize the reported information. This consistency ensures accuracy in the data collected, leading to improved patient assessment and better quality of care.
As the cloud computing trend continues to grow, data dictionaries play a critical role in ensuring the successful integration of relational databases in the cloud. They facilitate data transformation and delivery, particularly within complex data architectures.
