The 16 Essential Features of a Data Catalog

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TLDR - Access the Data Catalog RFI/RFP Template here:

There is no denying it, data catalogs have established themselves as powerful tools for effortless search and discovery of enterprise data. They help business and technical users alike find, understand and make use of their data assets.

In recent years, the demand for comprehensive and collaborative data catalogs has extensively grown. With many data catalog solutions emerging on the market today, it is essential to narrow your search down to find catalogs which best cater to your company’s needs and goals.

Here is our take on the 16 features and capabilities to consider when choosing a data catalog in 2023.

Understanding Data Catalogs

Before delving into the many layers of data catalog features, let us establish what exactly is meant by ‘data catalog’:

For those who may be unfamiliar with the term, a data catalog is essentially a detailed inventory of all of the data assets across an organization. Much like a library neatly organizes its books shelf by shelf, aisle by aisle, genre by genre, data catalogs achieve the same but for data. Think of data catalogs as a resourceful library where your data can be effortlessly located and trusted.

Although books and modern computers can hardly be compared, the users’ needs are fundamentally identical. How can I find what I need when I need it with so much data – or books – in front of me?

This centralized solution uses metadata (as do libraries) to classify and document digital resources, enriching data assets with context which in turn, allows for frictionless data search and discovery. Whether for business or technical users, data catalogs are geared towards enhancing the discoverability, searchability, and understandability of your data.

The Role of Data Catalogs in Modern Enterprises

Businesses are increasingly relying on data for BI and to drive business decisions. With data scattered across diverse sources, many data professionals lose valuable time in locating the relevant data they are after. Data catalogs help combat this by merging data assets under one single, unified truth. By housing diverse data resources in a centralized repository, catalogs effectively democratize data use across your whole organization.

Data Catalogs provide greater visibility and control over data assets, allowing organizations to better manage and leverage their data. They also facilitate collaboration between different teams and departments, helping to break down silos and promote knowledge sharing.

Another important aspect of a data catalog is its role in data governance. Data catalogs help organizations ensure that they comply with data privacy and security regulations, by providing a clear view of data lineage. They also help organizations manage data quality by providing a clear understanding of data definitions, validation rules, and metadata.

Essential Features of a Data Catalog

Here are 16 must-have features of data catalogs that you should consider when evaluating and choosing a data catalog solution:

1. Data Ingestion and Discovery

The first step to an effective data catalog solution is being able to connect to most (if not all) of the company’s systems- whether that be applications, databases, files, data lakes, data warehouses, etc.

Ingesting this scattered data from various sources and formats, and merging them together into one single, unified source of truth are the second and third steps to data ingestion. This offers data users a full view of the data assets flowing through the organization and provides a complete, accurate, and up-to-date dataset for business intelligence and data analysis.

Take a look for yourself, and check out all the platforms we integrate with at CastorDoc!

2. Search functionality

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With a google-like search interface, modern data catalogs enable users to effortlessly browse through and search for their data. Like Google, the modern data search feature leverages metadata to deliver the best results (meaning the most popular data sets are typically promoted to the top of the search page). This is why search is a key functionality of the data catalog: it bumps up data discovery and empowers users to find the most relevant data, where “relevance” is based on the actions of the community.

3. Business Glossary

Business glossaries, also referred to as data glossaries, consist of a comprehensive collection of terms accompanied by their definitions and explanations; all of which are specific to different organizations. Think of it as a single source of truth for business definitions, where the aim is to align everyone around company metrics.

Almost like a reference guide for employees and stakeholders to understand business-related terminology, business glossaries help organize knowledge within an enterprise.

A business glossary is key to fostering a collaborative culture within your organization and establishing a common understanding of key terms and concepts across different departments. It helps eliminating confusion and miscommunication by ensuring consistency in the usage of business terms.

For a more in-depth analysis of data glossaries, head to this blog article.

4. Metadata management

If metadata is ‘data about data’, then it’s perhaps not surprising that metadata management refers to how metadata is managed. Easy enough, don’t you think? Now that we’ve gotten that out of the way, let’s explore why it is a vital feature of modern data catalogs.

In many ways, metadata management is essential to any data management system as it helps stakeholders learn what data they possess, where it resides, and how it is organized.

The many perks of metadata management include:

• Enhanced understanding of your data assets (both internal and external).
• Greater ability to mitigate risks associated with human error or intentional data interference.
• Reduced likelihood of compliance breaches due to incorrect classification or inappropriate sharing.
• Accessibility of data to users who need it, right when they need it.

5. Artificial Intelligence

Naturally, modern data catalogs have not been spared by the AI wave which has heavily struck in recent years. Much to the contrary, data catalogs have embraced such innovations, and the best catalogs seek to leverage generative AI to fortify their data governance strategies.

From generating on-demand documentation for data assets, automating data access management, or flagging anomalies in data, AI’s magical touch is a welcome addition to all data catalogs. In fact, in some, you can even approach the AI with your specific data inquiries, ultimately enhancing collective understanding and data manipulation.

6. Data Quality Monitoring & Anomaly Detection

Users may be wary of using certain data assets, particularly if they are unsure if they have the right source or if the quality is dubious. Effective data catalogs have data quality tests embedded directly inside them that can help settle any doubts. This makes it easy for users to identify issues and flag unhealthy data.

Monitoring your company’s data health is indispensable as unhealthy data can lead to uninformed decisions, missed opportunities, and financial losses.

AI can be used to detect anomalies or sudden changes in data and notify users about such events, allowing errors to be corrected continuously.

Now that your data is housed in scalable infrastructure and is accessible and understandable to everyone, it's essential to ensure that its content is of high quality. This is why so many data observability and reliability tools were born in the last five years. Data observability is the general concept of using automated monitoring, alerting, and triaging to eliminate data downtime.

Some catalogs developed integrations with these observability tools, allowing data consumers to check the results of data quality tests directly on the data catalog. If you’re curious about how data catalogs and data observability tools interact, check out our piece on the topic.

7. Out-of-the box (OOTB) Connectors

Out-of-the-box (OOTB) connectors play a role in data management by enabling smooth integration between different data sources and the data catalog. These ready-to-use connectors are designed to function after installation without requiring any customization or configuration. They act as bridges connecting the data catalog to databases spreadsheets, cloud storage, or other repositories. The value of these connectors lies in their efficiency and user-friendliness allowing organizations to quickly consolidate data without the need for connection building. This ensures a user-friendly experience, which is essential for making timely and accurate data-driven decisions.

8. Vast number of APIs

Another way to ensure effortless integration with the rest of your data stack is through Application Programming Interfaces (APIs). Unlike OOTB connectors, APIs offer a more flexible and customizable solution to connect and interact with different data sources, applications, or platforms.

APIs provide greater control and customization, enabling developers to tailor integrations to specific needs and requirements. On the downside, these demand a higher level of technical expertise and are more time-consuming.

For those looking to explore these capabilities further and find a solution that fits their unique integration needs, you can delve into the extensive possibilities offered by Castor's API.

9. Understanding Data without SQL Knowledge

Traditionally, querying data required users to be familiar with SQL and have a good command of its language. But modern data catalogs can now help even non-technical, business users run SQL queries with the help of a low-code/no-code interface and intelligent automation.

By getting everyone on board with the data experience, this feature promotes engagement and collaboration from business and data teams alike, enabling both to find the data they need, ask questions and share their insights.

With CastorDoc, you get a tool built for viral adoption with an intuitive UI for data teams and business teams alike.

10. Embedded Collaboration

Simply put, embedded collaboration is about work happening where you are, with the least amount of friction.

All modern data catalogs should be built on the premise of embedded collaboration and draw some inspiration (or even borrow) from Notion, Slack, Figma and other modern tools.

For instance, you want to use a data catalog that makes it easy to connect tools that people use every day like Slack or Gmail to eliminate the need for switching between different tools and teams. Unlike standalone tools, an embedded collaboration data catalog seamlessly integrates into daily workflows. Conversations, support requests, Slack threads, tagging, and asset sharing coalesce within this platform. This feature eradicates app-switching, allowing data teams to streamline their processes efficiently.

11. Data lineage

Data lineage is key to retracing the lifecycle of data over time, providing a clear picture of the provenance of data, the transformations it has undergone and its mobility over time across the data pipeline. Data lineage is essential for efficient data management, without it data teams can lack clarity on how data is related, how changes impact related assets, and may end up with inefficient or redundant workloads.

12. Automated Lineage Creation

As opposed to being recorded manually like traditional data lineage, automated lineage is compiled and managed automatically.

This is a much faster, more efficient approach to creating and managing data lineage, providing complete end-to-end visibility into the lineage mappings of an organization’s data assets. This means users can effectively gain a more comprehensive view of how their data moves across their organization, how data sources are related and even see the downstream impact when data sources are modified.

Automated data lineage tools provide advantages, including the ability to map data asset relationships automatically, and conduct impact analysis effortlessly. These tools offer benefits such, as improved root cause analysis, enhanced visibility into data, and increased autonomy in managing data.

13. Column-level lineage

In the past, data teams have relied on table-level lineage to understand the cause of data incidents and their impact on downstream dependencies. But, while this can provide some much-needed help, it fails to provide the granularity data teams need to fix these data problems.

In the context of data pipelines, column-level lineage traces the relationships across and between upstream source systems (i.e., data warehouses and data lakes) and downstream dependencies (e.g., analytics reports and dashboards) to illustrate how the data changes—and the effect systems changes will have at the column level.

Column-level lineage can be used to massively reduce both time-to-detection and time-to-resolution of data quality issues, reducing the time to root-cause data pipeline and helping data engineers spend more time working on their data platform and less time working on their data problems.

14. Active metadata

Active metadata essentially refers to a dynamic and continuous approach to metadata management. It promotes continuous access to, and analysis of metadata across an organization’s entire data stack, fetching users where they are and providing them with relevant data context.

Active metadata is continuously updated, processed and incorporated in existing tools/ platforms used by the company. This effectively means the right metadata is constantly being pushed in the right workflows, meeting (the right) stakeholders where they are. This breeds a culture of collaboration, open-communication within an organization, and bridges the data literacy gap between business and tech users.

Activating metadata shows an understanding that, for a data catalog to be effective, metadata must be shared and comprehensive. Activate metadata is almost like connective tissue to data catalogs, guiding users to more data understanding, informed analysis, and valuable insights.

For a more in-depth discussion on active metadata, check out the piece we have written on the topic.

15. Intelligent Automation

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Now what exactly do we mean by Intelligent Automation?

Intelligent automation uses smart technology such as AI and machine learning to help users streamline and optimize processes of data management . Whether incorporated in routine and non-routine functions or helping with high volume repetitive tasks, intelligent automation requires minimal human intervention and is self-improving.

Your team should be able to access customizable intelligent automation that not only allows users to tailor automation rules but also get intelligent suggestions on how processes can be optimized. Automating tasks and leveraging AI to improve processes will lead to better data-driven insights and better business outcomes.

The Importance of Choosing the Right Data Catalog

If you have made it this far,  you now hopefully have a clearer understanding of how to evaluate a data catalog. The time has now come to make the selection for the data catalog best suited to your company’s needs and drivers.

The first step to selecting a data catalog is to determine what your company’s needs are, and then you can deal with finding the data catalog solution that addresses those needs best.

Identifying your pain points is a good place to start for instance. What are the top challenges that affect your team's productivity? What data catalog features can help remediate this?

If you need help in the process, check out our guide for evaluating data catalogs and our request access to our RFI template.

How CastorDoc Fulfills These Essential Features

CastorDoc successfully incorporates all of these essential features in an easy-to-use, collaborative and friendly tool designed to involve tech and business teams alike.

Our data catalog provides organizations with real business benefits, including improved productivity of data teams, trust and compliance, and self-serve dashboards that everyone can access and collaborate on.

Highlighting CastorDoc's Unique Features

Our goal is to make sure you capture and harness all your data knowledge, from every corner of your organization. We do this by connecting to your entire data stack with an easy to use interface so everyone finds exactly what they need to make data-driven decisions powering business impact.

So what exactly sets us apart from other data catalogs out there?

I can confidently say that one of CastorDoc’s true differentiators in the world of data catalog systems is its phenomenal user experience and ease of use.

CastorDoc is built for anyone, regardless of their data literacy level, department, or technical prowess, to use. It has a “Google-like” search engine, allowing users to quickly find what they need, with no technical expertise required. It can be set up very quickly and provides value from the very first day of use.

Our ultimate goal is to make working with data as easy as possible - even fun! (Yes, you heard me)

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About us

We write about all the processes involved when leveraging data assets: from the modern data stack to data teams composition, to data governance. Our blog covers the technical and the less technical aspects of creating tangible value from data.

At Castor, we are building a data documentation tool for the Notion, Figma, Slack generation.

Or data-wise for the Fivetran, Looker, Snowflake, DBT aficionados. We designed our catalog software to be easy to use, delightful and friendly.

Want to check it out? Reach out to us and we will show you a demo.

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