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Enterprise Data Catalog: Key Concepts & Best Practices

The volume and complexity of data generated by organizations continue to increase, making the need to manage and utilize data effectively more critical than ever. Enterprise data catalogs have emerged as a solution to this problem, providing organizations with a centralized and searchable repository of metadata about their data assets.

What is a data catalog?

A data catalog is a tool that enables organizations to discover, organize, and manage their data assets. It provides a complete inventory of an organization’s data assets and products, including information on their structures, locations, and relationships to other data products. The catalog helps users find and understand data by offering a centralized metadata repository about the organization’s data sources, complete with descriptions, tags, and other attributes.

Enterprise data catalogs are an essential tool for modern data management. In addition to aiding discovery, interpretation, and leveraging the power of data assets to elicit compelling insights, they can also be utilized for data governance and compliance. Users can tag sensitive data, discover relationships between different data sets, and determine data lineage, which aids in ensuring compliance with regulatory requirements and mitigating the risk of data breaches. The catalog also promotes collaboration among departments and teams.

In this article, we will explore enterprise data cataloging best practices in detail. By following these best practices, organizations can ensure that their data is discoverable, accessible, properly governed, and well managed, so they can drive data-driven decision-making. 

Steps to implement a successful data catalog strategy

Best practice Description
Define clear goals Clearly define and communicate the goals of the enterprise data catalog to ensure that they align with the overall business strategy.
Involve all stakeholders Involve all stakeholders in the implementation process to ensure that the data catalog meets their needs. This includes the chief data officer (CDO), governance officer, data engineers, business users, IT teams, data scientists, data analysts, and data stewards.
Ensure data quality Establish data quality processes and standards to ensure that the data in the catalog is accurate, complete, and up-to-date.
Establish data governance Implement top-down and bottom-up governance policies.
Continuously update and maintain the catalog Regularly update and maintain the catalog to ensure that it remains relevant and up to date with the latest information.
Facilitate self-service data discovery Provide tools and capabilities to enable business users to easily find and access the data they need for their analytical and reporting needs.
Continuously improve Continuously evaluate and improve the enterprise data catalog and associated processes to ensure that they remain effective. Assess the data catalog routinely, integrating user feedback and analyzing usage patterns to refine features and ensure alignment with organizational objectives.

Data catalog best practices 

At its core, a data catalog is a comprehensive inventory of all data assets that an organization possesses. This inventory typically includes information about each asset, such as the data’s source, format, purpose, and usage. It also includes tags and articles, which arise from collaboration and the enrichment of data information. 

A data catalog can be a powerful tool for any organization that wants to effectively manage and leverage its data assets. However, creating and maintaining a high-quality data catalog is not an easy task. To ensure that your data catalog meets the needs of your organization, it is important to adopt the best practices outlined below.

Define clear goals

Clearly understanding the organization’s data goals and ensuring that they align with the business strategy is crucial for creating a data catalog that meets the organization’s needs. Without clear goals, data catalogs may become incomplete, or irrelevant. 

The goals of the data catalog will vary depending on the organization’s priorities. Here are some examples: 

  • Improving data discovery: Enabling users to easily find the data they need by providing comprehensive and accurate information from the connected data assets.
  • Increasing data usage: A data democratization technique that encourages more users to access and use the data by providing clear and accessible information about the data sources and their intended uses. Data catalogs often include data dictionaries that describe the contents, formats, and structures of data stores and the relationships among them. In this context, Nexla’s contribution stands out: Nexla’s data products promote increased data usage by organizing data products based on descriptions, tags, schemas, etc., and allowing users to curate, enrich, and share them seamlessly across the organization.
  • Reducing data-related risks: Mitigating the risk of data breaches or data privacy violations by providing information about data security and access controls and by enforcing data governance policies.
  • Supporting data-driven decision-making: Disseminating necessary data to support business decisions by providing accurate and up-to-date information and metadata about the makeup of data assets, including data sources, relationships, and quality.
  • Enforcing data governance: Implementing data governance policies is critical. This includes maintaining data lineage, tracking where data comes from, and understanding how it’s transformed across the system, all of which aids in compliance and ensures trust in data. Data that is accurately cataloged, properly used, and securely managed will meet regulatory requirements and internal policies. A robust data catalog contributes to effective data governance by providing a centralized metadata repository, enabling consistent data definitions, and tracking data lineage.

Involve all stakeholders

To ensure that your data catalog meets the needs of your organization, it is important to involve all stakeholders in its creation and maintenance. Doing so can provide several benefits: 

  • Meeting organizational needs: Stakeholder involvement makes certain that the catalog accurately reflects the needs of the organization. Different stakeholders have different requirements for the data they use, and involving them in the cataloging process can help ensure that those requirements are met. 
  • Improving catalog quality: Involving stakeholders can help improve the quality of the data catalog. Maintaining data quality is essential to preserving the relevance of the data catalog as the business progresses and expands over time. Additionally, by bringing in different perspectives and subject matter experts, stakeholders can identify errors, inconsistencies, or omissions, enabling enhancements through their recommendations and reviews.
  • Promoting buy-in and adoption: Involving stakeholders can help promote buy-in and adoption of the data catalog. When stakeholders feel like they have been heard and their needs have been addressed, they are more likely to use the catalog and promote it to others across the organization. This not only ensures that the data catalog is used but also supports the democratization of data, something many organizations are attempting to accomplish.
  • Promoting collaboration: Stakeholders can contribute vital recommendations and reviews, thereby enriching the data catalog. Their involvement and feedback not only enhance the quality of the data but also cultivate a collaborative environment. This active collaboration and stakeholder contribution significantly aids data democratization, which many organizations value.

The following are some standard approaches for involving stakeholders in the creation and maintenance of a high-quality data catalog.

Identify the stakeholders

The first step is to identify all stakeholders who may have an interest in the data catalog. This may include business and functional users, C-suite executives, data analysts, data scientists, IT staff, compliance officers, information and security personnel, and others. 

Define roles and responsibilities

Once the stakeholders have been discovered, their roles and responsibilities in the cataloging process need to be identified. This may include determining who will be responsible for creating and maintaining the catalog, who will review and approve changes, and who will steward and own the various data products managed by the catalog itself.

Establish clear communication channels

Effective communication is key to ensuring that all stakeholders are informed and engaged in the cataloging process. Establishing clear communication channels, such as regular meetings, scrums, or email updates, can help keep stakeholders informed of progress and changes to the catalog. These two-way channels also instill a sense of ownership of the catalog, increasing the likelihood that it will be adopted by its users. 

Provide training and support

Not all stakeholders may be familiar with the process of cataloging data, so it is important to provide training and support as needed. This may include training on the tools or processes used to create and maintain the catalog as well as support for any technical issues that may arise.

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Incorporate feedback

Stakeholders are a valuable source of feedback and expertise, so it is important to provide opportunities for stakeholders to voice their feedback and subsequently incorporate that feedback into the data catalog. This may include suggestions for new data elements, improvements to existing elements, or changes to the catalog’s structure. By incorporating feedback, you can ensure that the catalog remains relevant and useful to the organization.

Ensure data quality

A data catalog is only as good as the data it contains. Poor data quality can undermine the value of the entire catalog, so it is essential to establish a set of standards for data quality. This set of standards should be agreed upon by all stakeholders, including data owners, data stewards, data consumers, and IT and security staff. 

The standards should address key areas, such as completeness, accuracy, consistency, timeliness, and relevancy. They should also define the process for data validation, cleansing, and enrichment. These can include rules on naming conventions and taxonomy, data deduplication methodologies, and standard date formats that are consistent with the location of your organization. 

Next, data profiling should be conducted regularly to ensure that the data meets the established standards. Data profiling is the process of examining data to identify inconsistencies, inaccuracies, and gaps. The results of data profiling should be used to identify data quality issues and develop corrective actions. A well-equipped tool like Nexla can significantly enhance data quality monitoring and lineage tracking efforts. 

Leverage data profiling results for data quality

Nexla’s data monitoring capabilities ensure consistent, high-quality data. Using data profiling can let you identify data quality issues and develop corrective actions. Nexla’s Data Validator Engine is designed to automatically scan data pipelines for errors or inconsistencies based on predefined rules and business standards.

Establish data lineage for quality control

Understanding the journey of your data, from its origin to its current state, is crucial for maintaining high data quality. This process, known as data lineage, helps identify potential sources of data quality issues, enabling you to act appropriately.

With Nexla’s sophisticated tools, you can establish practical data lineage. Each record in Nexla inherits a unique tracker ID, allowing you to track a record’s journey through the Nexla platform seamlessly. 

Metadata management is another key element of ensuring data quality in a data catalog. Metadata provides context and structure to data, which is essential for data discovery and understanding. This includes information about the data’s source, structure, format, and other key descriptive attributes. 

Metadata management ensures that metadata is complete, accurate, and up to date. For example, if a data catalog contains customer data, metadata management can ensure that each customer record includes all the necessary attributes, such as name, address, phone number, and email address. Metadata management can also check that the data is consistently formatted and labeled so that data consumers can easily find and understand it.

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Establish data governance

Data governance is the process of defining policies, procedures, and responsibilities for managing data across an organization. It ensures that data usage is effective, efficient, secure, and compliant with industry standards. A robust data governance framework provides effective management and accuracy of the cataloged data in a data catalog context.

Guidelines, which are part of the data governance framework, can define data classification, labeling, and organization. They also establish requirements for data quality, security, and privacy. For instance, if an organization stores personally identifiable information (PII) for email marketing, data governance policies may stipulate that only essential personnel can access this PII.

However, adequate data governance should be more than just top-down. Instead, it should empower trusted users to guide the stewards, fostering a data ownership and accountability culture throughout the organization. This balance of top-down and bottom-up governance ensures a broader perspective and enhances the quality and usability of the data catalog.

Nexla’s Nexsets can leverage data governance features. A Nexset is a data management solution that simplifies data integration and transformation, enabling users to create reusable and shareable data sets called “Nexsets.” By utilizing Nexsets, businesses can significantly reduce data preparation efforts and enhance workflow efficiency. 

The following image provides a visual representation of how a Nexset operates in the context of data integration and management.

Data governance policies dictate the masking of PII and the personnel that query this data. (Source)

Data governance policies dictate the masking of PII and the personnel that query this data. (Source)

Continuously update and maintain the catalog

To effectively utilize a data catalog and ensure perpetual value from the assets it manages, it is crucial to continuously update and maintain it. Here are some reasons why:

  • New data assets: As new data assets are created, acquired, or retired within an organization, they must be added to, updated within, or deprecated in the data catalog to ensure that users can easily find and understand them. Without regular updates, the data catalog can quickly become outdated, leading to confusion and wasted time. A solution to this challenge can be found in Nexla’s seamless integration with cataloging data providers like Alation. This feature enables users to keep their catalogs current by publishing Nexsets with a single click, streamlining the update process and minimizing the risk of having an outdated catalog.
  • Metadata changes: Metadata for existing data assets may change over time; for example, the schema of a database may be updated, or new data elements may be added or altered. These changes must be reflected in the data catalog to ensure that users have access to the most up-to-date information.
  • Data quality: Regularly updating and maintaining the data catalog can improve data quality, so data users can make better decisions and avoid errors arising from using outdated or inaccurate data.
  • Compliance: Many organizations must comply with regulations and standards that require the proper management and documentation of data assets. New or amended regulations, particularly data privacy laws, are constantly being introduced, and organizations are obligated to cooperate. A well-maintained data catalog can help ensure compliance by providing an auditable record of data assets and their metadata.

By keeping the data catalog up to date, organizations can improve data quality, ensure compliance, and avoid wasted time and confusion for data users.

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Facilitate self-service data discovery

This best practice involves empowering data users to discover and access data on their own without relying on data engineering, IT, or other technical staff. This approach encourages data democratization and can increase the speed and efficiency of data-driven decision-making.

Here are some key aspects of facilitating self-service data discovery through a data catalog:

  • User-friendly interface: The data catalog should have a user-friendly interface that is easy to navigate and search. The interface should provide users with relevant and accurate information about each data asset, including its description, purpose, and quality.
  • Centralized repository: Erroneous decision-making, reduced data quality, and limited collaboration are all potential outcomes of decentralized and siloed data. The data catalog should be a centralized repository of metadata that provides a comprehensive view of all data assets within the organization. This allows users to easily discover and access relevant data from one location, ensuring a single source of truth.
  • Data profiling: As mentioned earlier, the data catalog should include data profiling capabilities that allow users to understand the quality and structure of data assets and the relationships among them. This information helps users determine the suitability of the data asset for their business requirements.
  • Collaborative features: The data catalog should include collaborative features that enable users to share their knowledge and insights about data assets. Users can contribute comments, ratings, reviews, and articles, which can help others make informed decisions. Furthermore, the catalog should include a notification system for updates on assets that users have shown interest in, as staying informed about changes is a crucial part of collaboration and data management.
  • Access controls: The data catalog should have access controls that ensure data security and privacy. Users should only have access to the data assets that they are authorized to view.

Nexla’s Marketplace for Data Products is a private marketplace within your organization that enables data owners and data consumers to discover, request, or search for the data they need using a shopping-like interface. The Marketplace enables self-serve data through its centralized repository, where ready-to-use data products are organized by business area, department, and user.

By facilitating self-service data discovery, organizations can increase the efficiency and effectiveness of their data-driven decision-making. Users can easily find and access relevant data assets, reducing the time and effort required to make informed decisions. This approach also promotes collaboration and knowledge-sharing among users, leading to improved outcomes.

Continuously improve

Continuously improving is a data cataloging imperative that involves the ongoing evaluation, optimization, and enhancement of the data catalog to meet evolving business needs and user requirements. This best practice involves the following key aspects:

  • User feedback: Soliciting and incorporating user feedback is critical to continuously improving the data catalog. Users should be encouraged to provide feedback on the catalog’s functionality and usability as well as the relevance of its content.
  • Regular maintenance: Regular maintenance of the data catalog is essential to ensuring that it stays up to date and accurate. This includes updating metadata, removing outdated or irrelevant data, and addressing any technical issues or bugs.
  • Performance monitoring: Monitoring the performance of the data catalog helps highlight areas for improvement, such as slow search times or inadequate user adoption. Regular performance monitoring can also help identify new features or functionality that would be beneficial to users.
  • Collaboration: Collaboration with stakeholders across the organization, including IT, data analysts, and business users, can help with identifying new requirements and prioritizing improvement efforts.
  • Tech and tooling upgrades: Technology upgrades can help improve the functionality and performance of the data catalog. For example, implementing new machine learning algorithms can improve search results and provide more accurate and relevant recommendations.
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​​Following data catalog best practices is critical for organizations to effectively manage their data assets and support data-driven decision-making. These practices not only promote discoverable, well-managed data but also encourage collaboration, foster innovation, and drive business growth. 

By defining clear goals, involving all stakeholders, ensuring data quality, establishing data governance, facilitating data discovery and continuously improving, organizations can unlock the full potential of their data, gain a competitive advantage, and ultimately achieve their strategic objectives. With the rapid pace of technological advancement and increasing importance of data in today’s business landscape, adopting these data catalog best practices can help organizations to adapt to evolving data needs and foster innovation.

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