Must-Have Features in a Data Catalog Tool

In modern business, what separates the leaders from the laggards? In a word, it’s about data. Today, enterprises that can successfully understand, analyze, and use the data they gather boast a unique competitive edge. One need only look to leaders such as Kroger for proof of this. The grocery chain has invested heavily in data science and purchased the remaining 50% of its analytics arm 84.51 in 2015. Today, with yearly sales exceeding $132.5 billion, Kroger is one of the world’s largest retailers.

However, data practitioners tasked with transforming data into insights and action face an enormous challenge. On average, they spend 80% of their time simply finding data they can use. That leaves just 20% of their time for analysis and other business-critical tasks. 

Data catalogs have emerged as indispensable tools for data search and discovery, accelerating time to insight by as much as 99% for some organizations while saving hundreds of thousands of analyst hours. As data catalogs have evolved into data intelligence platforms, a comprehensive catalog should support a range of tasks housed beneath data management, from governance to usability and security, as well as search. 

For this reason, modern data leaders today are no longer asking, “Why buy a data catalog?” but “Which data catalog should we choose?” To help guide them, this blog will explore the must-have features that make a data catalog effective and valuable, including data governance and stewardship capabilities, data lineage, intelligent search functionalities, user activity tracking, data quality tool integration, metadata management, privacy capabilities, and an intuitive interface for non-technical users.

Data governance and stewardship features

What is data governance, and why is it important?

Data governance is the backbone of effective data management. It ensures that data is managed according to established policies and standards, enhancing data quality, security, and compliance. A robust data catalog tool should provide comprehensive data governance features that support these goals. A critical goal for any data governance program is trust. By establishing standards around data quality, management, and analysis, such a program not only democratizes data access with appropriate controls, it builds trust across data user communities with visibility. 

According to Forrester Research, a data governance solution is “A suite of software and services that help you create, manage, and assess the rules for data acquisition, access, and use, including data definitions, policies, quality, regulatory requirements, ethical considerations, use rights, privacy and security, and end-to-end lifecycles. Integrated tools enable collaboration and education across the organization.” For these reasons, a data governance solution must act as a platform for data users, effectively integrating with other data tools to support a range of use cases that sit beneath the governance umbrella. 

Stewardship capabilities

Data stewardship describes the activities associated with curating and governing data so that others can find, understand, trust, use it, and ask questions should they arise. A good data catalog should empower data leaders to identify data stewards based on work that is already being done, assign stewards to relevant assets, and set clear responsibilities for them. This fosters accountability and ensures that data is handled correctly throughout its lifecycle.

Due to high data volumes, many platforms are increasingly offering automated stewardship capabilities. These features support use cases such as bulk curation, which empowers stewards to curate data at scale, assign policies, or classify data in aggregate.

A robust business glossary is another key stewardship tool leaders should seek in their data catalog. This central repository lays out clear, data-backed definitions of terms like “profit” with business metrics to ensure communications are consistent across departments.

Benefits of data governance and stewardship for the business

Because data governance impacts how data is managed across an entire organization,  implementing data governance and stewardship features in a data catalog leads to numerous benefits. These include improved data quality, enhanced compliance with regulations, and increased accountability. By ensuring that data is managed according to best practices, organizations can build trust in their data and make more informed decisions.

Data lineage

What is data lineage, and why is it important?

Data lineage refers to tracking the flow of data from its origin to its final destination. It provides a detailed map of how data moves and transforms within an organization, offering insights into data sources, transformations, and dependencies. 

A clear picture of data lineage is critical for data pipelines. When breaks in these complex systems occur, data engineers often refer to lineage to troubleshoot the issue and identify the root cause through impact analysis. 

Data lineage is crucial for enhancing transparency and understanding data flow within an organization. It supports compliance efforts by providing a clear record of data transformations, which is essential for audits and regulatory reporting. Additionally, data lineage aids in troubleshooting data issues by pinpointing where errors or discrepancies occur in the data pipeline.

Data lineage features to seek

When selecting a data catalog, look for features that automate data lineage tracking and provide visual representations of data flow. Automated data lineage tracking reduces manual effort and errors, while visual representations make it easier to understand data flow and dependencies.

Not all data lineage tools are created equal. Those surveying the data catalog landscape for a partner should consider the value of lineage features which are friendly for non-technical users. A data catalog that offers lineage with embedded data health and policy information will be useful to a wider audience, as this valuable metadata can guide a wider audience to compliant, trusted data.

Intelligent search

What is intelligent search, and why is it important for data users?

Intelligent search refers to advanced search capabilities within a data catalog that enable users to find relevant data assets quickly and efficiently. This includes features like natural language search (or the ability to search in business terms), search filters by domain, and relevance ranking.

In large organizations with vast amounts of data, finding the right data can be challenging. Intelligent search capabilities make it easier for users to locate and access the specific data they need, from the appropriate domain. This ability improves productivity and data utilization.

Search features to seek

A data catalog should offer natural language search to allow users to search using everyday language, making it accessible to non-technical users. Search filters help narrow down results, while relevance ranking ensures that the most pertinent results appear first.

How vast is your data landscape? For most enterprises, particularly those with a global footprint, or those which have expanded through mergers and acquisitions, a decentralized data landscape is the norm. In other words, data is spread across silos, databases, lakehouses, file systems, and more. To address this, those in the market for a data catalog should seek out a tool that offers a universal search bar, making it easy to search all sources at once. (A data catalog that integrates with other top tools in the modern data stack is also key to supporting this).

Advanced search is another helpful feature to prioritize. By giving data seekers tools to sort by object type, custom tags, data quality, or domain, leaders can accelerate the time it takes for data users to find and comprehend the data they need.

User analytics and activity tracking

User analytics describes a dashboard that shows how data assets are used across the organization, when, and by whom. This includes tracking user activity, data usage patterns, and engagement with data assets.

Understanding how data is used within an organization provides valuable insights into data utilization and user engagement. It helps identify popular data assets, underutilized data, and potential areas for improvement in data management. 

Similar to this is the need for consumption tracking. As organizations migrate their data to the cloud, leaders need a view into consumption patterns in order to optimize usage and reduce the likelihood of activities that cause costs to skyrocket. A consumption tracker provides insights into data usage patterns, optimizing query executions, and identifying power users. This tool not only accelerates cloud migrations by offering a clear strategy but also ensures data governance and quality, ultimately enabling organizations to become more data-driven and AI-ready. With detailed monitoring of data migrations and visibility into query activities, a consumption tracker helps control costs and enhances overall efficiency.

User analytics features to seek

Look for a data catalog that offers comprehensive usage analytics, user activity tracking, consumption tracking, and reporting capabilities. These features enable organizations to monitor data usage effectively and make data-driven decisions to optimize data management processes.

Seek out a data catalog that enables admins to monitor metrics like total assets curated, total active users, and top queries. Not only do these key performance indicators inform superior data management, but they also provide crucial insights into how an organization’s data management program is performing and delivering on projected goals. 

Data quality tool integration and support

Simply put, data quality builds trust. When the appropriate processes are in place to ensure the delivery of quality data, data scientists can trust the outputs of their models are reliable. Data analysts can trust that the information they’re analyzing will generate accurate projections. And business leaders can trust that these analyses are built upon a stable foundation. 

Indeed, data quality is critical for ensuring the accuracy, completeness, and reliability of data. In this way, high-quality data is key for making informed decisions, driving business outcomes, and safeguarding trust in data assets and the analyses they inform. 

Furthermore, any organization that seeks to drive AI initiatives must prioritize data quality, as high-quality data is a critical component of successful AI. 

Different industries have distinct data quality demands, which require a tailored approach. For this reason, the data catalog you select should seamlessly integrate with the data quality tools most pertinent to your mission. This not only streamlines data quality management, but enables organizations to manage data quality issues directly within the data catalog, reducing the need for separate systems and processes. By delivering a single view into data quality across the organization, leaders can build trust across departments and up-level data literacy. 

Integrating data quality tools with a data catalog provides a centralized platform for managing data quality. This leads to improved data trustworthiness, streamlined data quality management processes, and enhanced overall data management.

Metadata management

What is metadata management and why is it important?

Metadata is information about data. Details like top users, common uses, sources, attributes, and relationships are all examples of metadata. Curation describes the activity of managing this metadata to make the parent asset more accessible and easily understood by those seeking to use it. Simply put, metadata provides context and helps users understand and utilize data assets effectively.

Effective metadata management enhances data discoverability, data governance, and understanding. It allows users to quickly find and comprehend the data they need, improving productivity and data utilization.

Metadata management features to seek

A data catalog tool should offer automated metadata extraction to ensure metadata is up-to-date and accurate. Customizable metadata fields allow organizations to tailor metadata to their specific needs, while metadata versioning tracks changes and ensures data integrity.

A data catalog that offers behavioral analysis of metadata gives leaders valuable insight into who uses data and how. A view into the most popular assets is a useful proxy for a range of use cases, including data migration and process improvements. By showing newcomers to data how that asset is most frequently leveraged, and by whom, those newcomers can more quickly grasp the value of an asset, alongside the experts who can guide their own usage.

Leaders should also be on the lookout for a data catalog that offers bi-directional exchange of metadata. Modern data ecosystems are complex, with many tools to choose from; a business leader may prefer Tableau, while a data analyst may spend most of his time in Excel. Bi-directional exchange of metadata delivers valuable information about data from the catalog to other popular tools, like Slack, Tableau, and Microsoft Excel. This ensures that even those not using the data catalog are benefiting from its curated metadata.

Privacy and compliance capabilities

What is data privacy compliance, and why is it important?

Data privacy is essential for protecting PII (personally identifiable information) and sensitive data; it’s also essential for complying with regulations such as the GDPR and CCPA. Ensuring data privacy builds trust with stakeholders and mitigates the risk of data breaches.

Privacy and compliance features to seek

A data catalog should offer data masking, access controls, and compliance reporting to support data privacy. Data masking protects internal unauthorized access of sensitive information by obscuring it, while access controls ensure that only authorized users can access certain data. Compliance reporting helps organizations demonstrate adherence to privacy regulations.

Seek out a data catalog with a robust policy center feature to make the policies impacting data privacy easier to create, organize, and manage. This feature should also enable privacy experts to set rules, define ownership, and link to associated data within the catalog.

As regulations around data policies grow, leaders need a means to scale policy application across their entire data estate. A catalog that offers PII data discovery eases this task. Features that enable data leaders to apply policies at scale with rules and automation (and use filtering to spotlight the data subject to policies) are essential for any organization that takes compliance seriously. 

Benefits of privacy and compliance

Implementing privacy capabilities in a data catalog tool enhances data security, supports regulatory compliance, and builds trust with stakeholders. It ensures that sensitive data is protected and handled according to established policies and regulations. It also enables data users to comply with requests from consumers to remove their personal data – a right enshrined into law by the CCPA and GDPR.

Intuitive interface for non-technical users

For a data catalog tool to be effective, it must be accessible to all users, regardless of their technical expertise. An intuitive interface ensures that non-technical users can easily navigate the data catalog and find the information they need.

Data catalog interface features to seek

Look for data catalog tools with a user-friendly design, drag-and-drop functionalities, and guided workflows. These features make it easy for non-technical users to interact with the data catalog, increasing adoption and usage across the organization.

Features that surface details about data’s relative trustworthiness and quality, such as trust flags, quickly signal to newcomers what’s safe to use, and what’s best let alone. Data experts should have the ability to endorse data, marking it as trustworthy, or deprecate data, marking it as untrustworthy. 

An intuitive interface fosters data democratization by making data accessible to a broader audience. It increases user engagement and ensures that more users can leverage data to drive business insights and decisions.

Conclusion

Data catalogs are essential tools for modern enterprises, providing a centralized platform for managing data assets. Key features to look for in a data catalog tool include data governance and stewardship capabilities, data lineage tracking, intelligent search functionalities, consumption tracking, data quality tool integration, metadata management, privacy capabilities, and an intuitive interface for non-technical users.

Organizations should evaluate their current data catalog tools and consider upgrading to one with these essential features. A comprehensive data catalog tool not only improves data management but also enhances compliance, security, and overall data utilization.

Book a demo with us to see how Alation delivers these critical features – and much more.