Category: Blog

Databricks has redefined what's possible in data engineering and analytics, offering powerful, large-scale data pipelines, complex transformations, and machine learning model training. Yet even the most sophisticated data environments often struggle with a familiar issue: technical excellence in data engineering doesn't automatically translate into business value.

That gap exists because the people who need data the most, such as analysts, financial leaders, and business users, often can't find, access, or understand it with confidence. The reason isn't a lack of data assets. It's a lack of context, discoverability, and self-service accessibility.

This is where the modern data catalog becomes not just useful, but essential. And it's where the Actian Data Intelligence Platform is a critical complement to Databricks Unity Catalog, bridging the gap between data engineering and business value.

Accelerate Enterprise-Wide Business Adoption

Databricks Unity Catalog provides robust governance and fine-grained permissions within the Databricks environment. It's designed for engineers, offering lineage, enforcing security, and standardizing access.

While those capabilities are needed by modern enterprises, technical governance isn't enough. Organizations also require data discovery and self-service data access, giving data users and domains a way to make trusted data understandable and usable.

Consider this typical challenge: A marketing analyst needs customer segmentation data to plan a campaign. Meanwhile, a product manager wants to understand feature adoption trends, while the finance team needs revenue data broken down by business unit.

They all know the data exists somewhere in the organization, but they:

• Don't know where to find it.
• Can't decipher technical table names or metadata.
• Are unsure if data is approved for their use case.
• Need to submit tickets for IT to get the data, then wait for access.
• Lack visibility to see how data relates across various systems.

This "last-mile" barrier keeps valuable data locked away from analysts and ultimately decision-makers. Too often, employees can't find the information they need, even in organizations using advanced data platforms.

4 Reasons Data Catalogs Remain Essential in Modern Data Stacks

A modern, comprehensive data catalog, like the Actian Data Intelligence Platform, fills the accessibility gap that data engineering platforms alone can't close. It complements Databricks Unity Catalog in four key ways:

1. Adds Business Context to Technical Metadata

Databricks Unity Catalog tracks technical metadata such as table schemas, column types, and storage locations. Yet business users don't think in terms of customer_dim_v3 or fact_transactions_daily. They think about "customer demographics" and "daily sales."

Actian Data Intelligence Platform enriches Unity Catalog's technical metadata with business-friendly definitions, context, and relationships. A table called cust_seg_ml_scores becomes "Customer Segmentation ML Scores – Propensity scores for marketing segments, updated daily, approved for marketing use." That translation helps users understand not just what the data is, but how and why to use it.

2. Supports Cross-Platform Discovery

While Databricks excels within its ecosystem, enterprises rarely operate on a single platform. They often have data in legacy databases, SaaS applications, on-premises systems, and cloud platforms. This can be challenging for business users and analysts who need to discover data, regardless of where it physically resides.

Actian Data Intelligence Platform provides a unified discovery layer, connecting Databricks Unity Catalog with the rest of the organization's data landscape. Users can search once and find relevant data across all systems, with consistent governance policies, without switching tools.

Search results unify metadata, data lineage, and business context into one interface. This allows users to find relevant data assets regardless of location.

3. Offers Self-Service With Governance Built-In

The promise of modern data platforms is self-service analytics. True self-service doesn't mean uncontrolled access. It means controlled empowerment by balancing data accessibility with governance, security, and compliance.

This is where Actian's approach to data governance shines. The platform delivers:

• Automated access workflows that grant permissions based on business context and user roles.
• Data contracts that define how data products can be consumed.
• Complete lineage tracking that shows business users where data comes from and how it's transformed.
• Governance guardrails that prevent inappropriate use while enabling exploration.

Business users get the self-service experience they need, while data teams maintain the governance they require.

4. Enables Semantic Understanding for Natural Discovery

Traditional search capabilities rely on keywords matching metadata. That works well for finding exact matches, but is blind to context. For example, someone searching for "revenue" won't see data labeled "income." The platform's knowledge graph foundation enables semantic search that understands relationships and context.

With Actian, when a business user searches for "customer lifetime value," the platform automatically delivers results for related concepts such as "CLV," "lifetime revenue," "customer value score," and "retention metrics," even if those exact terms don't appear in the search query. This context-aware intelligence dramatically improves data usability, especially for non-technical roles.

Real-World Impact: Sanoma Media Finland

Sanoma Media Finland provides a perfect example of why data catalogs remain essential even with best-in-class data engineering. As Mikko Eskola, the company's data director, explains, "As the leading Finnish media company, it is important that we deliver relevant content to our audiences and trusted insights to our advertisers."

To achieve these goals, Sanoma needed more than just data engineering capabilities. It needed organizational data discovery that would help the company remain competitive and innovate. The challenge wasn't building data pipelines—Databricks handles that beautifully. The challenge was to make data accessible to business users.

By implementing the Actian Data Intelligence Platform alongside Databricks, Sanoma created a complete solution:

• For data engineers, Databricks provides the horsepower to ingest, transform, and process massive volumes of media data, user behavior, and advertising metrics.
• For business users, Actian's data catalog makes that data discoverable, understandable, and accessible through intuitive interfaces and self-service workflows.

The result? Sanoma expects to significantly reduce bottlenecks in data access management while maintaining governance and security. Marketing teams can find audience data faster. Sales teams can access advertiser insights without waiting for IT. Product teams can explore user behavior patterns with confidence that they're using approved, high-quality data.

Actian and Databricks Deliver a Unified Data Foundation

Think of Databricks as the engine of the data environment that ingests, transforms, and models data at scale. Actian adds the intelligence layer that connects that power to the people who use data.

Together, they create a complete data intelligence ecosystem where every stakeholder, from data engineer to analyst, can trust, understand, and act on data with confidence.

The AI Advantage: Making Governed Data AI-Ready

The importance of data catalogs becomes even clearer as organizations adopt AI and deploy AI agents. Actian's Model Context Protocol (MCP) Server delivers value here by connecting AI assistants like ChatGPT and Claude directly to governed and catalogued data, ensuring that AI responses are accurate, explainable, and compliant.

This is transformative because AI agents need more than access to raw data. They need context, relationships, and business meaning to provide accurate insights. Without this intelligence layer, AI models risk producing unreliable insights from unverified data. With it, organizations can safely scale AI adoption and accelerate value from their data investments.

According to Capgemini, 93% of leaders believe that successfully scaling AI agents delivers a competitive edge over industry peers. However, that edge only materializes when AI agents work with cataloged, governed, AI-ready data.

The Bottom Line: Data Catalogs Aren't Legacy. They're Essential.

Some organizations mistakenly believe that modern data platforms like Databricks eliminate the need for data catalogs. The opposite is true. The more sophisticated an organization's data engineering becomes, the more essential a comprehensive data catalog becomes.

As data ecosystems expand and AI becomes ubiquitous, the need for transparency, trust, and accessibility only grows. Here's why:

• Without a catalog, even the best data engineering creates a technical fortress that only engineers can navigate. Business users remain dependent on IT for every data question.
• With a catalog, data engineering excellence creates business value. The investment in Databricks pays dividends across the entire organization, not just within the data team, by making data readily accessible.

For enterprise organizations serious about being data-driven, the question isn't whether to invest in a data catalog. The question is "How can you afford not to?"

By integrating the Actian Data Intelligence Platform with Databricks Unity Catalog, enterprises achieve true data democratization with data that's governed, contextual, and AI-ready. Organizations benefit from a comprehensive solution that serves the entire organization, from data engineers building pipelines to business analysts making critical decisions.

That's not just a better data stack. It's a competitive advantage built on trust, visibility, and collaboration. Find out more about how Actian and Databricks extend data visibility and governance.

About Danielle Simon

The Senior Director of Strategic Alliances for Actian, Danielle Simon brings over 15 years of experience in global partnerships, alliances, and channel management across data, cloud, AI governance, analytics, and security. Her experience includes accelerating revenue growth for innovative companies through strategic sales and partnerships.

If you've used AI models, you're aware of how quickly they can identify patterns and produce insights. However, what you might not realize is that without the ability to contextualize data, these models are merely making educated guesses.

For example, the models can't tell you whether a sales figure came from the most recent quarter or if a dataset is certified for regulatory reporting. That's where the Model Context Protocol (MCP) Server comes into play.

With MCP Server, your AI stops guessing and starts knowing. It delivers contextual insights you can trust, automate, and act on with confidence.

The MCP Server acts as a secure bridge between large language models (LLMs) and your organization's data. Instead of relying on general training data, it connects AI agents directly to governed, trusted information within your business. This way, your insights are not only faster but also accurate, explainable, and aligned with your business reality.

The Missing Link in AI: Real-Time Context

Most large organizations already have the necessary computing power for AI, but they lack reliable context. The MCP Server addresses this gap.

By giving AI access to live metadata, data lineage, and quality scores, the MCP Server grounds every interaction in real-world business logic. It turns your data catalog into a dynamic foundation for AI, allowing systems to validate facts, trace data origins, and make decisions based on governed information.

That context transforms AI from a helpful assistant into a reliable business partner.

Context in Action: How Industries are Using MCP Servers

All sectors can benefit from implementing the MCP Server. Here are some examples:

Financial services. When every decision carries regulatory weight, the MCP Server gives AI the transparency and control that financial institutions need to ensure compliance and informed decision-making. Real-time data lineage tracking makes audits painless, while automated compliance reporting ensures that both AI models and stakeholders trust the numbers driving investment and risk analysis.

Healthcare and life sciences. From drug discovery to clinical trials, organizations depend on data integrity and explainability. The MCP Server enables AI agents to discover and connect to certified datasets, ensuring that predictive models, simulations, and regulatory submissions are built on validated, compliant data. Researchers can trace the origin and transformation of every data point, accelerating innovation while safeguarding patient privacy and meeting stringent compliance standards.

Manufacturing. For manufacturers looking to modernize their legacy systems, the MCP Server powers instant dependency mapping and impact analysis, making updates or migrations easier. AI agents can identify redundant data or find the right production metrics across plants and systems to support predictive maintenance, inventory optimization, and supply chain visibility.

Retail and consumer goods. Retailers can use the MCP Server to unify data from e-commerce, point of sale (POS) systems, and customer systems. Semantic search lets AI find relationships across products, seasons, and demographics to help teams personalize promotions, forecast demand, and maximize inventory to reduce overstock waste.

Public sector and education. Governments and universities depend on trustworthy data to shape policies, guide research, and measure outcomes. The MCP Server strengthens those processes by identifying data owners, surfacing certified datasets, and ensuring decision makers can confidently use governed data to improve transparency, build public trust, and boost institutional performance.

Go From a Static Data Catalog to Building a Foundation for Agentic AI

The MCP Server does more than connect systems. It connects meaning. It gives AI access to your organization's data intelligence so it can understand not just what the data is, but what it means.

That's how enterprises move from AI that guesses to AI that knows. Whether it's verifying compliance, optimizing operations, or accelerating insights, the MCP Server transforms your data catalog from a passive repository into the active foundation of your AI strategy.

See 10 real-world AI use cases enabled by MCP Servers.

About Actian Corporation

Actian empowers enterprises to confidently manage and govern data at scale, streamlining complex data environments and accelerating the delivery of AI-ready data. The Actian data intelligence approach combines data discovery, metadata management, and federated governance to enable smarter data usage and enhance compliance. With intuitive self-service capabilities, business and technical users can find, understand, and trust data assets across cloud, hybrid, and on-premises environments. Actian delivers flexible data management solutions to 42 million users at Fortune 100 companies and other enterprises worldwide, while maintaining a 95% customer satisfaction score.