What is a Data Catalog and Its Use Cases in the Enterprise 

What is a Data Catalog and Its Use Cases in the Enterprise 

27 June 2023

Often, as companies grow and become reliable on data and its data-driven processes, a few questions get thrown around more frequently than people like to admit.

  • Where is the data and how do I get access to it?
  • How do I understand this data in front of me? 
  • I am generating ‘X’ GB of data daily. Is it even used? 
  • Is there any new interesting data generated that I should be aware of? 

…and so on.

If you have asked any of the above questions more than once, then it’s time to modernize your data processes. Let me introduce you to the data catalog.

What is a data catalog?

A data catalog is a centralized inventory or repository that provides detailed information about the available data assets within an organization. It serves as a detailed and organised metadata management tool that enables all data enthusiasts within an organisation to discover, understand and utilize the data within the data ecosystem.  

A data catalog includes metadata about the data assets, such as type, descriptions, data lineage, data quality information, data source details, data owners, and access permissions. It also provides search and discovery capabilities, allowing users to search for specific datasets or browse through different categories and tags. It provides social capabilities to comment and discuss, tag, like and share data information among peers, which helps in building a healthy data ecosystem.

What problems does a data catalog solve and why implement one?

Within an organisation, there are several problems related to managing and utilizing data. Oftentimes, data is lost, misunderstood or not used for the intended purpose. Here are some frequently occurring problems and why implementing a data catalog offers several benefits when dealing with large datasets.

  • Data discovery: One of the primary challenges is finding relevant and trustworthy data within an organization. A data catalog, i.e. a centralized inventory of data assets within an organization, helps data consumers and analysts to easily discover and locate the data they need for their analyses or projects. This saves effort and time and helps teams be more productive. 
  • Data understanding: Data can be complex and often lacks proper documentation, making it difficult for users to understand its structure, content, and context. A data catalog maintains metadata information such as data lineage, data quality, and data definitions. This helps data enthusiast build business context, understand the meaning, structure and relationships of the data, leading to improved data interpretation and analysis.
  • Data governance and compliance: Maintaining data governance is crucial for ensuring data quality, security, compliance, and regulatory adherence. A data catalog facilitates data governance by providing visibility into data assets and their ownership. It helps establish data policies, standards, and guidelines, ensuring that data is managed and used in a compliant manner. Data catalogs can also support regulatory compliance efforts by providing documentation of data usage, lineage, and privacy requirements. 
  • Data lineage and impact analysis: Understanding the origin, transformation, and usage of data (i.e. the data lifecycle) is critical and crucial for all organisations. A data catalog helps you maintain the data lifecycle by allowing you to catalog data lineage. This helps maintain the integrity of data, aids troubleshooting, and supports compliance requirements. Data lineage also enables users to understand and analyse the impact of changes on downstream systems or analyses. Oftentimes, it also helps us predict changes required on upstream/downstream systems to drive impact in a certain direction. 
  • Data quality and trust: Questions regarding data reliability and quality are often unanswered in a large organisation with complex datasets and ingestion pipelines. A data catalog allows you to capture many metrics which speak to the quality and health of the data. These data profiling and qualitative metrics promote data usability and trustworthiness and encourage informed decision making. 
  • Collaboration and knowledge sharing: Data catalogs promote teams and stakeholders to collaborate on data assets, share knowledge, and provide feedback. This promotes data democratization, where different teams and individuals can access and contribute to the collective knowledge about the organization’s data. 

Overall, implementing a data catalog helps organizations improve data discovery, understanding, governance, collaboration, and decision-making. It promotes efficiency, reliability, and trust in data usage, leading to better data insights and better data-driven practices. Hence organizations derive more value from their data assets while reducing risks and improving efficiency. 

When is the right time to implement a data catalog?

The right time to implement a data catalog can vary depending on the specific needs and circumstances of an organization. However, there are several common situations that indicate it may be beneficial to implement a data catalog: 

  • Data growth and complexity: When an organization’s data assets start growing in volume and complexity, it becomes increasingly difficult to manage, discover and understand relevant data. Implementing a data catalog can help organize and classify data. 
  • Data integration and consolidation: When an organization is in the process of integrating data from multiple sources or consolidating data from different system, a data catalog becomes crucial and helps you create a centralized platform and a unified view, with easy governance, standardisation and collaboration. 
  • Data collaboration and sharing: When multiple teams or departments within an organization need to collaborate and share data, a data catalog provides a central repository to discover, understand, and access relevant data. It promotes data democratization and eliminates silos. 
  • Data discovery and self-service analytics: If your organization aims to enable self-service analytics or empower business users to explore and analyse data independently, a data catalog becomes essential. It allows users to search, explore, and understand available datasets, fostering a data-driven culture. 
  • Data governance and compliance: If your organization needs to adhere to data governance policies, regulatory requirements, or data privacy regulations, a data catalog can play a crucial role. It helps document data lineage, metadata, access controls, and data usage, enabling better compliance management.
  • Data quality and trust: Poor data quality can have a significant impact on decision-making and business operations. A data catalog helps document data lineage, quality metrics, and validation rules, facilitating data quality management and building trust in the data.
  • Data migration or modernization: During data migration or modernization of projects, a data catalog can help identify redundant, obsolete, or outdated datasets. It aids in understanding data dependencies and mapping data elements between different systems. 
  • Metadata management: If your organization struggles with inconsistent or incomplete metadata across different data sources, implementing a data catalog can help establish a unified and consistent view of metadata. It improves data understanding and reduces the risk of misinterpretation.
  • Data monetization: If your organization is in the process of monetization of data, a data catalog becomes crucial for end users to find and understand your data easily. 

Ultimately, the right time to implement a data catalog is when the benefits it provides align with your organization’s specific data management needs, such as data organization, governance, collaboration, discovery, quality, or metadata management.

It’s essential to assess your organization’s maturity, data landscape, and goals to determine if a data catalog is a valuable investment. 

What steps are key to building a data catalog? 

Building a data catalog involves several key steps to ensure its effectiveness and usefulness. Here at diconium we have the following important steps to help you build your data catalog: 

  1. Identify the goals and objectives: We come together to understand why you need a catalog and what your end goals are, helping you define and document the purpose of the data.  
  2. Define metadata requirements: We define the metadata elements you want to capture and manage in your data catalog. This includes information like data source, data type, data quality, owner, description, and any other relevant attributes that will help users understand and discover the data. 
  3. Inventory your data assets: We identify all the data sources and the corresponding data assets within your organization. This includes databases, files, APIs, data streams, and other sources. We document the location, structure, and dependencies of each data asset. 
  4. Choose a cataloging tool: We select a data catalog tool or platform that aligns with your organization’s requirements. There are various commercial and open-source options available. we consider factors such as scalability, ease of use, integration capabilities, and the ability to customize and extend the catalog as needed. 
  5. Metadata Modelling: Based on the metadata requirements and cataloging tool, we design and document the underlying model design and organisation structure. 
  6. Implement metadata management processes: We establish processes and workflows for capturing, documenting, and updating metadata. Determine who will be responsible for managing metadata and ensure that these processes are integrated into existing data governance and data management practices. 
  7. Populate the catalog: We begin populating the data catalog with metadata for your data assets. This may involve manual entry, automated extraction from data sources, or a combination of both. Ensure that the metadata is accurate, up-to-date, and relevant. Automated extraction of data sources may require addition work and scope management. We mandate multiple approaches and try to facilitate smooth and iterative processes. 
  8. Establish data lineage and relationships:  We help document and catalog in the data catalog the lineage and relationships between different data assets. This includes understanding how data flows from its source to its destination and identifying dependencies between datasets. This information helps users trace the origin of data and understand its impact on downstream processes.
  9. Govern access and permissions: We help define access controls and permissions for the data catalog based on user roles and responsibilities. Ensure that sensitive data is appropriately protected and that only authorized users can access and modify the catalog. 
  10. Promote adoption and usage: We encourage users to utilize the data catalog by providing training, documentation, and ongoing support. Communicate the value and benefits of the catalog to stakeholders and demonstrate how it can enhance data-driven decision-making and productivity. 
  11. Continuously maintain and update the catalog: We help set up guidelines to regularly review and update the catalog to reflect changes in data assets, metadata, and data usage patterns. We establish processes for ongoing data governance, metadata management, and catalog maintenance. We establish processes and guides for a self-serviced future.

The Butterfly Effect

To summarise, data catalogs are extremely important in our modern data-centric and data driven ecosystem. They help understand the data, allow knowledge sharing and drive impact. As a starting point in modernising a data ecosystem, a data catalog plays a vital role in designing a better tomorrow. 

Here are some of the opportunities that becomes possible once you have a working data catalog system:

  1. Data Moderation System: A moderation system is an opportunity that allows you to identify obsolete, duplicate, redundant, bloated and unused data in the data ecosystem. Cleaning and streamlining your data will help you manage resources and time and put them to better use.
  2. Data Observability Platform: A data catalog captures metadata about the data. A data observability platform data captures the metadata of your data and its data pipelines and processes. A data observability platform maintains lineage, dependencies and timelines of each data pipeline which helps you streamline your data generation process. Detail monitoring can help you setup alerts/notifications to inform about downtime and failure before they happen. This helps improve data reliability and trust.
  3. Data Marketplace: A data marketplace is your data catalog, data authorisation system and a data delivery system working together to provide data to the end-user as quickly as possible. This system helps you to showcase your data’s metadata internally and/or externally. Data can be requested on a self-service platform where the request will be vetted, internal multilevel authorisation process will grant/deny the request quickly and, finally, a data delivery system will provide actual data or access rights to the data source. This system helps reduce waiting time to get access to data and promotes easy data acceptance and usage.

Compliance Platform, Data Innovation Platform and many more systems and platforms can now be introduced into the system.  

All things considered, you can safely say that a data catalog is a “door that allows you to open many other doors”. You will feel the effects of this tiny butterfly for a long time, eventually evolving into a well-informed, easy to use, impact-driven data ecosystem. 

What is a Data Catalog and Its Use Cases in the Enterprise 
Back to top