Enterprise Data Catalog: Definition, Importance, Architecture, Use Cases, Framework & Benefits
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An enterprise data catalog offers a glimpse into your entire data asset universe. It helps data consumers within organizations to find, understand, and discuss data for decision-making.
In this article, we’ll look at the business value of an enterprise data catalog, benefits, architecture, use cases, and evaluation framework.
Table of contents
- What is an enterprise data catalog?
- When do you need one?
- Use cases
- Business value
- How to evaluate an enterprise data catalog solution
- Enterprise data catalog: Related reads
What is an enterprise data catalog?
An enterprise data catalog is a central point of reference for disparate data assets within large organizations. It makes these assets easy to discover, understand, and use at scale.
Usually, an enterprise data catalog supports:
- Intuitive search and recommendations, trust signals, filtering capabilities, etc. for data search and discovery
- A business glossary framework
- Automated visual lineage to trace the flow of data
- 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
When do you need one?
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. This 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 these requests also finds it challenging to keep up.
At WeWork, a 15-person data team supported the requests of 1500 data users. Most requests were about context:
- Finance team: “What does a number mean?”
- Engineering team: “What is the basic character of a table in Snowflake — a changelog, a fact table, or something else?”
- Product team: “Where is a certain data asset within Snowflake? How do I retrieve it?”
WeWork’s journey towards trust, transparency, and governance with an enterprise data catalog
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 sift manually 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. This leaves your data engineers 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.
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
Some of the top use cases for enterprise data catalogs include:
- Using auto-generated advanced business glossaries: Going beyond mere data definitions and also looking up synonyms, antonyms, categories, classification types, linked assets, and more for better context
- Automating data classifications: 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: Helping 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: Archiving and deprecating unused workflows and data sources - and hence saving cloud computing and storage costs
- Designing custom access policies: Using role-based and purpose-based access to ensure data enablement without compromising security
- Enabling automatic quality edits and custom data checks: Ensuring data accuracy so that your teams spend less time inspecting and verifying data and more time using it to solve problems
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. This makes it easier to keep track of all data assets within the organization.
With an enterprise data catalog, your data team can eliminate duplicate assets and stale or unused data, and cut down on unnecessary data processing. That leads 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. That reduces 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. 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
An enterprise data catalog automates several aspects of data documentation, classification, quality checks, and more. That means your data team can focus on shipping 2-3 times more projects in less time.
Moreover, enterprise data catalogs are self-service and integrate seamlessly with other data products. There’s no need to engineer complex workarounds 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.
An enterprise data catalog includes:
- A metadata lake for storing all kinds of metadata
- A set of integrations that create a plug-and-play environment for the catalog, with:
- 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. Each of the above components are indispensable to fulfilling that vision.
Read more → Components of modern data catalogs
How to evaluate an enterprise data catalog solution
Borrowing from the Forrester Wave™: Enterprise Data Catalog for DataOps, Q2 2022, your organization should choose an enterprise data catalog that offers the latest features for discovering and managing your data. These include:
- Out-of-the-box connectors for data products in your data stack — data sources, data movement tools, BI tools, and more
- Frictionless user experience, along with personalization and collaboration, for technical and business users
- Natural language search support and 360-degree asset profiling
- Data lineage mapping — impact and root cause analysis — so that your data consumers know how data was created and transformed
- Customizability and extensibility via open APIs
- Monitoring, alerts, and compliance
- Ease of development and deployment
- Advanced support for testing, anomaly detection, and machine learning support
- Risk management to enable data protection, privacy, and regulatory policies
- Data orchestration
- Data quality and data lifecycle management
- Data and metadata management
Besides capabilities, the enterprise data catalog should also have a product strategy with:
- A vision in alignment with the current and future needs of customers
- Planned enhancements to support changing customer needs
- An innovation roadmap to demonstrate a proven commitment to future innovation
If you are evaluating an enterprise data catalog solution for your business, take Atlan for a spin.
The latest Forrester report named Atlan a leader in Enterprise Data Catalog for DataOps, giving the highest possible score in 17 evaluation criteria including Product Vision, Market Approach, Innovation Roadmap, Performance, Connectivity, Interoperability, and Portability.
Atlan enjoys deep integrations and partnerships with best-of-breed solutions across the modern data stack. Check out our connectors here.
Atlan already enjoys the love and confidence of some of the best data teams in the world including WeWork, Postman, Monster, Plaid, and Ralph Lauren — to name but a few. Check out what our customers have to say about us here.
Enterprise data catalog: Related reads
- What Is a Data Catalog? & Do You Need One?
- Data catalog benefits: 5 key reasons why you need one
- Open Source Data Catalog Software: 5 Popular Tools to Consider in 2023
- Data Catalog Platform: The Key To Future-Proofing Your Data Stack
- Top Data Catalog Use Cases Intrinsic to Data-Led Enterprises
- Best Alation Alternative: 5 Reasons Why Customers Choose Atlan
- AWS Glue Data Catalog: Architecture, Components, and Crawlers
- Airbnb Data Catalog — Democratizing Data With Dataportal
- Lexikon: Spotify’s Efficient Solution For Data Discovery And What You Can Learn From It
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