Enterprise Data Catalog: Definition, Importance, Architecture, Use Cases, Framework & Benefits

Last updated on: March 07th, 2023, Published on: September 27th, 2022

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An enterprise data catalog offers a glimpse into the entire data asset universe and helps data consumers within organizations to find, understand, and discuss data for decision-making.

Data catalog evaluation guide

We’ll look at the business value of an enterprise data catalog, benefits, architecture, use cases, and evaluation framework.

Table of contents

  1. What is an enterprise data catalog?
  2. What to expect from an enterprise data catalog?
  3. Business value of an enterprise data catalog
  4. When do you need an enterprise data catalog
  5. Benefits of an enterprise data catalog
  6. Enterprise data catalog architecture
  7. Enterprise data catalog use cases
  8. Enterprise data catalog evaluation framework
  9. Enterprise data catalog: Related reads

What is an enterprise data catalog?

An enterprise data catalog can be defined as a central point of reference for disparate data assets within large organizations, making them easy to discover, understand, and use at scale.

Usually, an enterprise data catalog is equipped with:

  • Intuitive search and recommendations, trust signals, filtering capabilities, etc. for data search and discovery
  • A business glossary framework
  • Automated visual lineage to trace the data flow
  • Programmable workflows for granular controls and access
  • Native or API-powered integrations to connect data sources, BI tools, and data movement tools across the enterprise

What should you expect from an enterprise data catalog in 2023?

Traditionally, data catalogs were static, siloed, standalone wikis requiring humans to curate and document the data. As organizations evolved, the volume, variety, and velocity of data grew, along with the number and diversity of data users.

These modern organizations with data-driven teams require a solution like an enterprise data catalog that goes beyond conventional data inventorying.

A modern enterprise data catalog uses an active metadata management approach, where the system continuously collects metadata from logs, query history, and usage statistics, and also feeds it to the rest of the data tools. This ensures there is a single, up-to-date source of information for effectively working with data.

enterprise data catalog

The evolution of enterprise data catalogs. Source: Atlan

Prukalpa Sankar, co-founder at Atlan, further highlights how enterprise data catalogs are different from static inventories or wikis of data:

Rather than just storing a “wiki” of this data, EDCs act as a “system of record” to automatically capture and manage all of a company’s data through the data product lifecycle. This includes syncing context and enabling delivery across data engineers, data scientists, and application developers.

That’s why Gartner calls it “a metadata anywhere orchestration platform.”

Such catalogs play a significant role in DataOps. Here’s how Forrester puts it:

Enterprise data catalogs create data transparency and enable data engineers to implement DataOps activities that develop, coordinate, and orchestrate the provisioning of data policies and controls and manage the data and analytics product portfolio.

What is the business value of an enterprise data catalog?

An enterprise data catalog saves costs and time, improves efficiency, simplifies compliance, and helps you grow your organization’s revenues while minimizing the probability of lost opportunities.

Let’s see how.

1. Optimizing costs

An enterprise data catalog sets up a central data workspace, making it easier for your data teams to keep track of all data assets within the organization. So, they can help get rid of duplicate assets, and stale or unused data, and cut down on unnecessary data processing, leading to better resource utilization, lesser storage space, and a cleaner data landscape.

2. Saving time spent looking for data

In Anaconda’s 2021 State of Data Science survey, respondents said they spend “39% of their time on data prep and data cleansing, which is more than the time spent on model training, model selection, and deploying models combined.”

An enterprise data catalog sets up a self-service ecosystem with Google-like search, advanced business glossaries, visual lineage mapping, and more. It creates a central access layer for data, thus reducing time spent on searching for data and preparing it for use.

3. Avoiding hefty compliance fees

An enterprise data catalog allows you to set access controls at scale. Moreover, you can use the metadata collected on data classifications, processing, locations, and more to compile reports on data security and compliance. This helps you avoid hefty compliance fines while ensuring the security, integrity, and privacy of your enterprise’s data assets.

4. Ensuring greater efficiency

Since an enterprise data catalog automates several aspects of data documentation, classification, quality check, and more, your data team can focus on shipping 2-3 times more projects in no time.

Moreover, enterprise data catalogs are self-service and integrate seamlessly with other data products. As such, you don’t have to engineer complex technological workaround to keep the entire data stack interoperable.

5. Increasing overall revenue

Faster decision-making and time-to-insight lead to faster innovation, implementation, and a lower probability of lost opportunities.

When do you exactly feel the need for an enterprise data catalog?

Let’s look at a common scenario within data teams at large organizations. In enterprises, an analyst working on a problem would look for data by:

  • Asking IT if they can help find the data required
  • Checking with colleagues and constructing a narrative using their tribal knowledge
  • Reviewing spreadsheets from previous projects

For instance, CSE Insurance Group, a US-based $20 billion global insurer, had data scattered across the enterprise, which led to data analysts spending hours sifting through columns in multiple tables to find the right fit.

After compiling this data, processing it, and organizing it, the analyst often has to look for additional data and repeat the whole cycle. Despite pulling together the required data, there might be cases where new questions arise as the project progresses, and the analyst has to further refine their work.

The data team supporting the requests of this analyst also finds it challenging to keep up with the requests.

At WeWork, a 15-person data team supported the requests of 1500 data users. Most requests were about context:

  • “What does a number mean?” from the finance team
  • “What is the basic character of a table in Snowflake — a changelog, a fact table, or something else?” from the engineering team
  • “Where is a certain data asset within Snowflake? How do I retrieve it?” from the product team

Using Enterprise data catalog, how WeWork built a new, resilient data stack

In this scenario, a traditional data catalog won’t suffice. It would act as a static single point of reference for your data. You still have to manually shift through its contents, update the context, and then share it with the right people. Sometimes the catalog might not even integrate with other data tools in your tech stack, and your data engineers have to figure out a workaround.

Meanwhile, with large volumes of data pouring in, manually organizing data and its context (classification, tagging, glossary creation, etc.) at scale isn’t feasible.

That’s where an enterprise data catalog can make a difference.

Based on the principles of active metadata management and embedded collaboration, the enterprise data catalog will support the bidirectional flow of data so that you get all the context you need within your daily workflows.

The data analyst at CSE Insurance Group can use Slack to look up data definitions within minutes (instead of hours). The finance head at WeWork could ask for context via Slack. They could also use the Google-like search interface on the enterprise data catalog to look up data assets and get complete context by studying their 360-degree asset profiles.

Another advantage that an enterprise data catalog brings to the table is automation at scale. It automates metadata ingestion, lineage mapping, data policy propagation, classification, PII data detection, and more while supporting integrations across your enterprise.

So, the enterprise data catalog acts as a real-time, single source of truth for all enterprise data across the entire data landscape. It is a living data catalog that actively manages a wide variety of data assets — tables, BI dashboards, SQL queries — in real time. It also integrates with other data tools in your tech stack and manages data at scale.

As a result, the analyst would:

  • Spend mere minutes looking for the data assets they need while using the tools within their daily workflows, rather than switching across multiple applications
  • Set up a dashboard quickly using their BI tools of choice that also integrate seamlessly with the catalog
  • Collaborate with others to resolve any queries or discussions around data

What are the benefits of an enterprise data catalog?

An enterprise data catalog helps data teams in large organizations with:

  • Finding the right data asset via an accessible, intuitive user interface and self-service data search and discovery
  • Understanding data and its journey from its source to dashboards via data lineage mapping at a column level (both upstream and downstream)
  • Getting proper context through chat, upvotes, certification, notes, READMEs, tags, and shareable SQL queries
  • Boosting enterprise team collaboration by integrating seamlessly with other tools like Slack, Jira, GitHub, etc.
  • Simplifying data governance with role-based access controls, automatic PII classification and tagging, and propagation of classifications downstream through lineage for data security, integrity, privacy, and trustworthiness
  • Enabling DataOps via data observability (lineage, data quality) and data discovery (metadata search and business glossary) for smoother data observability and pipeline orchestration

What does an enterprise data catalog architecture look like?

An enterprise data catalog will include the following components:

  • A metadata lake for storing all kinds of metadata
  • A set of integrations that help set up a plug-and-play environment for the catalog, with:
    • Connectors to numerous data sources, such as data warehouses and lakes, data transformation tools, and BI tools
    • Extensibility through open APIs to connect with any data tool you want, from any source
  • An active data governance layer to manage data classification, tagging, encryption, lineage, audit trails, quality checks, data usage, and security with automation and programmable bots
  • An intuitive user workspace layer to:
    • Search across the entire data landscape
    • Customize the search results with metadata filters
    • Get context using business glossaries, 360-degree profiles, chat or discussion history, query logs, lineage maps, etc.
    • Restrict access based on user roles, purposes, or projects
    • Collaborate with tags, announcements, comments, etc.

According to the Eckerson Group, a modern data architecture for enterprises must be adaptable, flexible, smart, automated, collaborative, elastic, and customer-centric.

So, it’s important to ensure these characteristics get reflected while setting up an enterprise data catalog.

Read moreComponents of modern data catalogs

What are the various enterprise data catalog use cases?

As mentioned earlier, an enterprise data catalog helps with data search and discovery, lineage and impact analysis, data security, governance, regulatory compliance, and more. Let’s check out some of the top use cases for enterprise data catalogs:

  • Using auto-generated advanced business glossaries to go beyond mere data definitions and also look up synonyms, antonyms, categories, classification types, linked assets, and much more for better context
  • Automating data classifications, such as auto-classifying personally identifiable information as PII, or auto-propagating sensitivity classification from an upstream data element to all the derived downstream data elements
  • Performing root cause analysis to help analysts look upstream whenever a production pipeline breaks, and downstream to spot probable data mismatch in a dashboard because of the pipeline issue
  • Leveraging data usage statistics to archive and deprecate unused workflows and data sources and hence saving cloud computing and storage costs
  • Designing custom access policies — role-based and purpose-based access — to ensure data enablement without compromising security
  • Enabling automatic quality edits and custom data checks to ensure data accuracy so that your teams spend less time inspecting and verifying data and more time using it to solve problems

Read more → Top data catalog use cases at data-led enterprises

What should an enterprise data catalog evaluation framework include?

Borrowing from the Forrester Wave™: Enterprise Data Catalog for DataOps, Q2 2022, your organization should choose an enterprise data catalog with current offerings, such as:

  1. Out-of-the-box connectors for data products in your data stack — data sources, data movement tools, BI tools, and more
  2. Frictionless user experience, along with personalization and collaboration, for technical and business users
  3. Natural language search support and 360-degree asset profiling
  4. Data lineage mapping — impact and root cause analysis — so that your data consumers know how data was created and transformed
  5. Customizability and extensibility via open APIs
  6. Monitoring, alerts, and compliance
  7. Ease of development and deployment
  8. Advanced support for testing, anomaly detection, and machine learning support
  9. Risk management to enable data protection, privacy, and regulatory policies
  10. Data orchestration
  11. Data quality and data lifecycle management
  12. Data and metadata management

Besides capabilities, the enterprise data catalog should also have a product strategy with:

  1. A vision in alignment with the current and future needs of customers
  2. Planned enhancements to support changing customer needs
  3. An innovation roadmap to demonstrate a proven commitment to future innovation

Read moreThe Forrester Wave enterprise data catalog for DataOps

If you are evaluating an enterprise data catalog solution for your business, take Atlan for a spin - Atlan is a third-generation enterprise data catalog built on the premise of embedded collaboration that is key in today’s modern workplace, borrowing principles from GitHub, Figma, Slack, Notion, Superhuman, and other modern tools that are commonplace today.

Photo by Christin Hume on Unsplash.

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