How Enterprise Data Catalogs Drive Business Value at Scale

As organizations become more data-driven and the scope of data consumers expands through self-service, data literacy and data fluency have become valuable organizational competencies. Current events throughout the world are escalating the value of these competencies from valuable to imperative. The COVID-19 pandemic affects all dimensions of business and society. As the pandemic moves from current events to recent history, data competencies will be essential for businesses to manage the economic, social, and regulatory consequences.

For teams responsible for delivering trusted insights at speed, choosing the right data catalog is now table-stakes. Yet, many still spend 40% of their time trying to find reliable data in disorganized, siloed systems. This guide will detail how modern enterprise data catalogs address such challenges—centralizing governance, accelerating discovery, and embedding continuous curation to fuel self-service. 

What is the role of the enterprise data catalog?

An enterprise data catalog serves as the single source of truth for asset definitions, lineage, and usage context. As a result, teams are no longer forced to work in silos or to rely on inconsistent data definitions. Instead, users are able to find, interpret, and manage data with ease. 

Cataloging alone isn’t enough, though. Teams must also know how to act on their metadata, moving beyond basic data literacy to true data fluency. Like language fluency, data fluency goes further than reading and writing: It enables the expression of ideas, the creation of compelling data stories, and the spark of ideation that drives innovation. A data-fluent organization turns raw insights into narratives that bring data to life. This bridges the gaps between IT, analysts, and business stakeholders, so everyone speaks the same language and can make decisions based on shared understanding. 

3 types of enterprise data catalogs

Within the general data catalog umbrella, there are several more granular categories. Though there can be overlap, different enterprise data catalogs have different areas of focus. Here’s what you need to know: 

1. AI-powered catalogs and agents

Thanks to artificial intelligence and machine learning (ML), these catalogs excel at automating metadata ingestion, identifying data relationships, and recommending assets. They use AI and ML for two main purposes: 

1. Platform automation: Besides automated metadata ingestion, data-quality agents and intelligent SQL editors streamline curation and query authoring without manual intervention.

2. ML workflow foundation: Built-in training-data lineage, feature-store metadata, and model-governance artifacts ensure end-to-end visibility into machine-learning workflows.

Alation, for instance, leverages behavioral intelligence to enhance search relevance, surface trusted data, and suggest appropriate stewards and policies. 

It stands out by combining machine learning with active metadata and built-in data stewardship workflows. Unlike similar tools such as Atlan, Alation’s Behavioral Analysis Engine (BAE) auto-ranks datasets by trust and flags outliers, thereby increasing analyst productivity. Its intelligent SQL editor recommends datasets based on popularity, context, and usage history, empowering users to act with confidence.

Additionally, Alation’s AI agents continuously assess data quality at scale as shown below, flagging anomalies and suggesting remediation steps.

Ultimately, the catalog accelerates machine-learning workflows by simplifying training-data discovery, managing ML metadata, visualizing model lineage, and providing explainability for compliance.

2. Compliance-centric data catalogs

Compliance-centric catalogs are built to meet rigorous regulatory and privacy standards, offering extensive policy frameworks, audit trails, and modular governance controls. Collibra’s Data Intelligence Cloud is a good example of this. It segments core functions—cataloging, governance, lineage, privacy, and data quality—into distinct modules that enterprises can license and integrate as needed. 

While such modularity affords more control over data policies, it often requires dedicated professional services and complex orchestration. This can both delay deployment and drive up total cost of ownership.

Overall, compliance-focused catalogs excel in environments where compliance trumps ease of use. Highly regulated industries such as banking, healthcare, and government frequently adopt them to satisfy audit requirements and automate risk management. However, the separation of capabilities into multiple products also creates a fragmented user experience that’s harder for users to navigate. Organizations must weigh their compliance imperatives against the potential productivity drag and consider whether they have the in-house technical resources to manage ongoing integrations and upgrades.

3. IT-centric data catalogs

Lastly, some data catalog tools are more IT-focused. They cater primarily to data architects, data engineers, and governance teams. Plus, they offer deep integration with enterprise data platforms and policy engines. 

Informatica’s catalog is one such example. It’s built on the broader Intelligent Data Management Cloud (IDMC) suite, which ties together data ingestion, quality, and master data management services. Its strength lies in extensive connector libraries and templated governance workflows. While on the more expensive end of the price spectrum, Informatica does allow IT to enforce organization-wide standards and automate metadata harvesting at scale.

The caveat is that IT-centric catalogs like the above surface metadata through complex schemas and developer-focused interfaces. As a result, business users and data analysts may find the out-of-the-box views too technical (or even too sparse if features meant for non-technical users are not robust). Hence the reason this type of catalog is best suited for organizations with mature data operations teams that can handle additional configuration or create custom dashboards if needed. It may also suffice for organizations that prioritize centralized control and policy enforcement over rapid self-service adoption.

In any case, before selecting a data catalog built for enterprise scale, outline your business priorities. For example, they might involve improving speed, collaboration, or compliance. Then, map those objectives to the catalog that will best support them. This will require evaluating your current maturity stage. 

The enterprise data culture maturity model

Alation’s data culture maturity model focuses on four pillars: Leadership, Search and Discovery, Literacy, and Governance. Each has a spectrum from nascent to mature and is assessed via targeted statements to guide honest evaluation.

• Leadership: Involves vocal sponsorship, business value alignment, and organizational engagement

• Search and Discovery: Measures how easily users can locate and trust data

• Data Literacy: Evaluates whether employees across roles can confidently access and interpret data

• Governance: Assesses formal policies, metadata documentation, and accountability structures

Let’s briefly examine what maturity might look like at each of the five stages in Alation’s data culture maturity model:

• Level 1: Ad Hoc. Data lives in silos. Governance is reactive. Catalog use is minimal or nonexistent.

• Level 2: Developing. Some stewardship exists. Data discovery is possible, but inconsistent. Literacy varies widely.

• Level 3: Emerging. A formal catalog is adopted. Users trust curated assets. Governance becomes proactive.

• Level 4: Operational. Catalog is embedded in workflows. KPIs measure program success. Stewardship and literacy are incentivized.

• Level 5: Transformative. Data is fully democratized. Catalog informs strategy, compliance, and innovation.

As organizations evolve across the maturity model, they also advance their data governance practices, improve asset lifecycle management, and formalize data modeling standards. Effective curation becomes a continuous process embedded in daily operations. Many organizations have seen this to be true. For instance, Sallie Mae adopted Alation’s Data Intelligence Platform as a “front door” for its 500-plus data users—cataloging 250 TB across 350,000 fields. This enabled the company to overcome data governance challenges, boost data literacy, and accelerate trusted, self-service analytics company-wide.

Underpinning this seamless curation is the Open Connector Framework. It provides a broad library of prebuilt connectors—linking Snowflake, Tableau, Power BI, and dozens of other data sources. By automating metadata ingestion and enabling federated queries, the framework ensures that as new systems come online, they’re immediately woven into your governed data ecosystem. This keeps governance intact and insights flowing.

With your current data culture maturity stage and business goals in mind, the next step is to begin researching data catalog solutions built for enterprises specifically. 

What to look for in an enterprise data catalog

There are some basic features you’ll want in a catalog solution. First, robust search functionality is essential: Your analysts need to find data quickly and turn it into actionable insights. Alation’s data catalog, shown below, offers federated search. This allows querying of Snowflake, Tableau, Power BI, and other sources from a single interface. As a result, there’s no need to toggle between tools, and data assets are also ranked by relevance and usage. Trust flags and query recommendations further streamline discovery and boost productivity.

Second, look for collaboration features, such as user tagging, so team members can easily request context-specific feedback or resolve issues. 

Beyond these data catalog essentials, prioritize the following capabilities:

Behavioral intelligence

Behavioral intelligence captures granular signals—search queries, dataset usage frequency, and access patterns—and converts them into actionable guidance.

• Discovery personalization: Analysts should be able to find trusted data without sifting through siloed sources. Choose a catalog that surfaces context-relevant assets automatically. 

• Automated governance: Select an option that not only detects unusual or risky access patterns but also enforces policies before issues escalate.

• Data quality checks: A catalog should highlight high-value or problematic datasets based on actual usage and guide stewards to address quality issues.

Alation’s Behavioral Analysis Engine analyzes user interactions to surface trusted datasets and detect unusual usage patterns. It then recommends targeted stewardship actions—like data lineage reviews and policy adjustments—to maintain data reliability without manual oversight.

Integrated governance

A strong catalog embeds governance: policy centers, automated stewardship workflows, and compliance linkages across assets. 

• Error reduction capabilities: Your catalog should reduce redundant tasks and lower the risk of mistakes by centralizing policies and guidance in one place.

• Remediation workflows: Users need the ability to address issues immediately and access required data without friction. 

• Collaboration features: The catalog you choose should surface clear ownership across systems, so data teams can quickly identify and consult the right data steward. 

Alation’s Policy Center lets governance teams define, monitor, and enforce data access rules through automated workflows.

Business glossaries and lineage

Effective catalogs offer glossary-building tools and lineage visualizations to contextualize data and clarify upstream/downstream dependencies. 

• Data flow tracking: Look for a tool that allows you to visualize data lineage—where data originated, how it’s transformed, and the paths it takes over time.

• Historical insights: Your chosen catalog should get analysts access to past data records to perform efficient, context-rich analyses.

• Language standardization: It’s important to be able to create a clear business glossary to ensure that all data users within your organization use the same language.

Glossaries in Alation can be crowd-sourced and steward-assigned, with lineage visualizations spanning BI tools, warehouses, and pipelines.

Impact analysis and observability

Automatic lineage and change alerts reveal downstream effects instantly, reducing break and fix cycles.

• Outcome visualization: Users should be able to see how changes might affect specific areas of the business to anticipate downstream effects before they occur.

• Decision guidance: Your catalog should highlight which change paths deliver the greatest business value and align with organizational goals.

• Risk mitigation capabilities: Choose a catalog that enables impact analysis (illustrated below) so you can plan ahead, minimize disruptions, and address potential issues proactively.

Alation’s data lineage provides an end-to-end look at your data so you can better analyze potential impacts.

Access controls and security

Row- and column-level masking with dynamic policies protect sensitive data while enabling governed self-service.

• User-specific access controls: For security, ensure you can restrict sensitive data visibility so only qualified roles can see regulated fields.

• Governed self-service capabilities: A good catalog solution will enable analysts to retrieve necessary data swiftly on their own while still enforcing security policies.

• Dynamic policy update capabilities: Select a catalog that can automatically adjust masking and access rules as conditions change to keep governance current.

Alation’s Policy Center audits usage in minutes and streamlines policy management by putting policy information and updates in a single place.

5 best practices for getting started with an enterprise data catalog

Once you’ve chosen the most fitting enterprise data catalog for your business, how can you set your catalog and your organization up for success? Consider a handful of the many best practices for effective data cataloging. 

1. Identify data ownership roles

Clarify who is responsible for curating, maintaining, and approving metadata. Data stewards, data owners, and SMEs need clearly defined duties. Assigning ownership depends on organizational structure. In a retail company, for example, data stewards might be embedded in merchandising or marketing teams, while SMEs may sit in IT or analytics.

Tip: Build a RACI matrix so every critical KPI—revenue, churn, NPS—maps to a named data steward.

Why it matters: Clear ownership accelerates issue resolution and prevents critical data from “falling through the cracks.”

2. Collect user requirements

Interview data consumers to understand their pain points. Tailor metadata, policies, and glossary terms to their needs.

Tip: Run weekly listening sessions to stay on top of current issues and gather glossary terms.

Why it matters: Mapping real-world pain points to metadata fields keeps glossaries relevant and adoption high.

3. Document metadata rigorously

Use automation where possible, but always layer in human-curated insights. Rich metadata drives usability.

Tip: Schedule nightly crawls followed by steward review.

Why it matters: Consistent, up-to-date metadata cuts analyst search time and safeguards regulatory compliance.

4. Promote early collaboration

Onboard your most active users first. Their buy-in helps socialize the tool and democratize tribal knowledge.

Tip: Launch a 30-day “docu-jam” contest that rewards the most new glossary terms and lineage links.

Why it matters: Cross-functional input surfaces tribal knowledge early, avoiding rework and boosting trust in the catalog.

5. Track success metrics

Ensure data stewards and data owners have clear responsibilities in the workflow. Building a business glossary and intuitive dashboards enhances usability. Emphasize functionality that enables quick wins, such as saving searches or linking assets to key KPIs.

Start with foundational data quality metrics—completeness, freshness, duplication—to prove early ROI.

Tip: Set catalog KPIs like search-to-dataset click-through ≥ 60% and query reuse growth ≥ 25%.

Why it matters: Data-driven KPIs prove ROI to executives and guide continuous catalog improvements.

If you implement these recommendations, you can drive adoption within your organization and turn your data catalog into an invaluable asset for business decision-making. Over time, you’ll progress further through the stages of maturity. 

Plotting the path to enterprise data catalog maturity

Maturity is a moving target, but every organization can improve by assessing its current state and setting targeted goals.

1. Begin with honest benchmarking using Alation’s model. Focus initial efforts on areas that provide the greatest business value—often search and governance. Mobilize leadership, onboard stewards, and celebrate early wins.

2. Consider developing maturity playbooks tailored by function. Create a stewardship playbook for data owners, a search-and-discovery guide for analysts, and a governance handbook for compliance teams. These resources reduce ramp-up time and standardize best practices.

3. Implement observability practices and data profiling. Doing so can help monitor catalog health and support a growing data ecosystem. As the data stack grows, the catalog must offer scalability and robust self-service capabilities to keep pace with expanding demand.

A centralized repository ensures all datasets are searchable and auditable. A modern enterprise data catalog accelerates trust, governance, and self-service—but only if it arrives on time and at the right cost.

You can build your own data catalog to customize it to your liking, but it can take quite a bit of time and effort, with lengthy refining. If you’re looking to get organized quickly, buying a data catalog may be your better path.

Download our build vs. buy guide to see which option is best for your organization and compare the development effort, scalability, and maintenance overhead of each option.