Data dictionaries, inventories, and catalogs are terms often used interchangeably. While they are all critical to effective data management systems, they are not the same. What are the differences between these structures and their functions?
- What is a data inventory?
- What is a data dictionary?
- What is a data catalog?
- Data Inventory vs. Data Dictionary
- Data Catalog vs. Data Inventory
- Key Factors in Creating Data Catalogs, Inventories, & Dictionaries
What Is a Data Inventory?A data inventory is a centralized metadata collection, indicating all of the datasets an organization collects and maintains. This document (or collection of documents) pinpoints each dataset's location and the type of data it contains. On a practical level, this inventory allows data analysts to determine what data is available to them and how it is accessed. Data stewards maintain these data inventories and define the relevant data access policies for each dataset. Conducting a data inventory can be a mammoth task, especially when an organization undertakes this task for the first time. Here, it is important to distinguish between a data inventory and a data catalog. While these terms are often used interchangeably, they are not the same and perform different functions. A data inventory is a unique set of data detailing the location and type of each data point in a company's collection. A data catalog allows users to locate those datasets by referencing them in various categories. While each entry in a data inventory is unique, a data catalog can refer to the same data point in various entries. For this reason, a data inventory is far more granular, detailed, and technical than a data catalog.
What Is a Data Dictionary?Data dictionaries outline information about naming and defining data assets. These are stored as repositories and serve to support data engineering operations. In these dictionaries, we can find names, settings, and any other important attributes of data assets found in specific databases or data pipelines. These datasets offer a high-level understanding of the elements involved, allowing for more effective interpretation and guidance. This information helps define the scopes and application rules of each dataset, as outlined by various stakeholders. When employed effectively, data dictionaries help prevent inconsistencies and conflicts in data assets when they are used in projects. They further allow for precise, uncomplicated definition conventions and consistency in enforcing roles and uses. Additionally, data dictionaries often hold centralized definitions for terms surrounding data assets, relationships, and metadata on the origin, use, and data schema. In short, data dictionaries explain how specific types of data fit into the bigger picture. They are closely related to data warehouses, relational databases, and data management systems.
How to Create a Data Dictionary?There are two types of data dictionaries:
- Active Data Dictionary
- Static Data Dictionary
Data Dictionary ExampleData dictionaries typically contain the following elements:
- Data asset name
- Format type
- Relationships with other data entities and assets
- Reference data
- Data quality rules
- Elemental data asset hierarchy
- Datastore location
- System-level diagrams
- Reference data
- Missing data and quality-indicator codes
- Business rules (data quality validation and schema objects)
- Entity-relationship diagrams
What Is a Data Catalog?A data catalog is a centralized metadata repository that organizations use to manage their data. Here, a company outlines information about the organization, use, and management of data resources. This catalog supports functions of data engineering, analytics operations, and science.
Why Have a Data Catalog?When executed effectively, data catalogs facilitate improve data management. They provide high-level, categorized information on the datasets available in an organization, thus offering high-level insights and analytics. This asset enables stakeholders to efficiently find relevant datasets of any type stored in various locations, such as data lakes, warehouses, and other databases. Data catalogs underpin data engineering operations by keeping track of data schema changes to facilitate transformations and aggregations in data pipelines. Here, the data catalog helps data engineers check that incoming data adhere to the expected schema by triggering alerts when changes occur. Without effective data catalogs in place, these changes would likely occur unnoticed, resulting in silent failures. This results in the common problem where gaps exist between data and metadata in pipelines where data from various sources are processed. When the data changes unexpectedly, the data pipeline can subsequently fail or provide an incorrect output. This centralized, up-to-date repository allows organizations to track data assets efficiently and lets stakeholders quickly and easily find relevant datasets while adapting to the changing data landscape. Here, data teams can uncover previously unknown potential benefits, effectively apply data governance policies, and ensure regulatory compliance.
How to Build a Data Catalog?Data catalogs are generally stored separately from the datasets to which they refer, either in data warehouses or in data lakes. While building a data catalog, make sure you keep the primary goal in mind: data catalogs make data management easy and effective, sharing knowledge and information on the data collected and stored in your organization. It outlines the data flow in various pipelines and offers a birds-eye view of your data landscape. Building a data catalog can be time-consuming, especially when done manually. Here are five steps to follow when building an effective data catalog:
- Data Capture
- Assign Data Owners
- Knowledge Documentation
- Regular Updates
Data CaptureIn the data capture phase, first determine which metadata is relevant and how it should be captured. Answering these questions helps develop the shape and structure of your data catalog by giving you an understanding of your data's shape, structure, and semantics. First, determine the data most relevant to your company's growth, such as:
- Customers who have made at least one purchase
- Calculating acquisition costs
- Understanding which downstream processes would be impacted if you update a specific data pipeline.
- Knowing where PII (personally identifiable information) goes after it enters the data lake.
Assign Data OwnersOnce the data is captured, the organization must assign ownership over this data. This distinction provides a contact person for data users who require additional information, and it assigns someone the responsibility for ensuring that the data and documentation are complete and accurate. The most important data owners are the data stewards and technical owners. The data steward manages and addresses business-related queries, while the technical owner is responsible for resolving technical issues.
Knowledge DocumentationData documentation, even at a small scale, is potentially overwhelming. It is often not feasible to catalog all of your data at once. For this reason, you should follow a practical, logical approach, ensuring that the most important data is cataloged first, followed by the second most important data, and going down the hierarchy from there. Typically, one of three methodologies is followed:
- Document it when you find it. Here, everyone is responsible for updating the data catalog when they learn something new that has not been previously documented.
- Update documentation when you change the code. In this case, updating the data documentation is part of the protocol followed when releasing new features.
- Regularly set aside time. Set aside an hour per week, or 15 minutes per day, during which each team member documents the data assets they are familiar with or researches one they do not know well.
Regular UpdatesDatasets change constantly. Identifying these changes and updating your data catalog accordingly is essential. This process should ideally be automated to save time, but a fair portion of user interaction remains essential to ensure that all updates are sensible. Here, governance actions automatically prompt user intervention when appropriate.
OptimizationA data catalog is a tool that enables your teams to interact with your data effectively. Understanding these teams' needs and optimizing your standards and norms accordingly paves the way for optimal data interactions. To this end, develop the following norms:
- Standardize documentation formats for all in-house databases, schemas, fields, and data lineage.
- Identify essential learning plans, such as new employee onboarding and tag the relevant assets.
- Reinforce norms surrounding the data catalog and embed it into your organization's data culture.