Data Governance

Data Quality and Data Observability: Why You Need Both

Actian Corporation

May 26, 2025

As data becomes more central to decision-making, two priorities are taking precedence for data leaders: data quality and data observability. Each plays a distinct role in maintaining the reliability, accuracy, and compliance of enterprise data.

When used together, data quality and data observability tools provide a powerful foundation for delivering trustworthy data for AI and other use cases. With data systems experiencing rapidly growing data volumes, organizations are finding that this growth is leading to increased data complexity.

Data pipelines often span a wide range of sources, formats, systems, and applications. Without the right tools and frameworks in place, even small data issues can quickly escalate—leading to inaccurate reports, flawed models, and costly compliance violations.

Gartner notes that by 2026, 50% of enterprises implementing distributed data architectures will have adopted data observability tools to improve visibility over the state of the data landscape, up from less than 20% in 2024. Here’s how data quality and observability help organizations:

Build Trust and Have Confidence in Data Quality

Every business decision that stakeholders make hinges on the trustworthiness of their data. When data is inaccurate, incomplete, inconsistent, or outdated, that trust is broken. For example, incomplete data can negatively impact the patient experience in healthcare, or false positives in credit card transactions that incorrectly flag a purchase as fraudulent erode customer confidence and trust.

That’s why a well-designed data quality framework is foundational. It ensures data is usable, accurate, and aligned with business needs.

With strong data quality processes in place, teams can:

Identify and correct errors early in the pipeline.
Ensure data consistency across various systems.
Monitor critical dimensions such as completeness, accuracy, and freshness.
Align data with governance and compliance requirements.

Embedding quality checks throughout the data lifecycle allows teams and stakeholders to make decisions with confidence. That’s because they can trust the data behind every report, dashboard, and model. When organizations layer data observability into their quality framework, they gain real-time visibility into their data’s health, helping to detect and resolve issues before they impact decision-making.

Meet Current and Evolving Data Demands

Traditional data quality tools and manual processes often fall short when applied to large-scale data environments. Sampling methods or surface-level checks may catch obvious issues, but they frequently miss deeper anomalies—and rarely reveal the root cause.

As data environments grow in volume and complexity, the data quality architecture must scale with it. That means:

Monitoring all data, not just samples.
Validating across diverse data types and formats.
Integrating checks into data processes and workflows.
Supporting open data formats.

Organizations need solutions that can handle quality checks across massive, distributed datasets. And these solutions cannot slow down production systems or cause cost inefficiencies. This is where a modern data observability solution delivers unparalleled value.

Comprehensive Data Observability as a Quality Monitor

To understand the powerful role of data observability, think of it as a real-time sensor layer across an organization’s data pipelines. It continuously monitors pipeline health, detects anomalies, and identifies root causes before issues move downstream. Unlike static quality checks, observability offers proactive, always-on insights into the state of the organization’s data.

A modern data observability solution, like Actian Data Observability, adds value to a data quality framework:

Automated anomaly detection. Identify issues in data quality, freshness, and custom business rules without manual intervention.
Root cause analysis. Understand where and why issues occurred, enabling faster resolution.
Continuous monitoring. Ensure pipeline integrity and prevent data errors from impacting users.
No sampling blind spots. Monitor 100% of the organization’s data, not just a subset.

Sampling methods may seem cost-effective, but they can allow critical blind spots in data. For instance, an anomaly that only affects 2% of records might be missed entirely by the data team, until it breaks an AI model or leads to unexpected customer churn.

By providing 100% data coverage for comprehensive and accurate observability, Actian Data Observability eliminates blind spots and the risks associated with sampled data.

Why Organizations Need Data Quality and Observability

Companies don’t have to choose between data quality and data observability—they work together. When combined, they enable:

Proactive prevention, not reactively fixing issues.
Faster issue resolution, with visibility across the data lifecycle.
Increased trust, through continuous validation and transparency.
AI-ready data by delivering clean, consistent data.
Enhanced efficiency by reducing time spent identifying errors.

An inability to effectively monitor data quality, lineage, and access patterns increases the risk of regulatory non-compliance. This can result in financial penalties, reputational damage from data errors, and potential security breaches. Regulatory requirements make data quality not just a business imperative, but a legal one.

Implementing robust data quality practices starts with embedding automated checks throughout the data lifecycle. Key tactics include data validation to ensure data meets expected formats and ranges, duplicate detection to eliminate redundancies, and consistency checks across systems.

Cross-validation techniques can help verify data accuracy by comparing multiple sources, while data profiling uncovers anomalies, missing values, and outliers. These steps not only improve reliability but also serve as the foundation for automated observability tools to monitor, alert, and maintain trust in enterprise data.

Without full visibility and active data monitoring, it’s easy for errors, including those involving sensitive data, to go undetected until major problems or violations occur. Implementing data quality practices that are supported by data observability helps organizations:

Continuously validate data against policy requirements.
Monitor access, freshness, and lineage.
Automate alerts for anomalies, policy violations, or missing data.
Reduce the risk of compliance breaches and audits.

By building quality and visibility into data governance processes, organizations can stay ahead of regulatory demands.

Actian Data Observability Helps Ensure Data Reliability

Actian Data Observability is built to support large, distributed data environments where reliability, scale, and performance are critical. It provides full visibility across complex pipelines spanning cloud data warehouses, data lakes, and streaming systems.

Using AI and machine learning, Actian Data Observability proactively monitors data quality, detects and resolves anomalies, and reconciles data discrepancies. It allows organizations to:

Automatically surface root causes.
Monitor data pipelines using all data—without sampling.
Integrate observability into current data workflows.
Avoid the cloud cost spikes common with other tools.

Organizations that are serious about data quality need to think bigger than static quality checks or ad hoc dashboards. They need real-time observability to keep data accurate, compliant, and ready for the next use case.

Actian Data Observability delivers the capabilities needed to move from reactive problem-solving to proactive, confident data management. Find out how the solution offers observability for complex data architectures.

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.

Data Observability

Quality Data, Reliable AI: Introducing Actian Data Observability

Emma McGrattan

May 12, 2025

Summary

This blog introduces Actian’s Data Observability platform—a proactive, AI-powered solution designed to ensure data reliability, reduce cloud costs, and support trustworthy AI by monitoring 100% of data pipelines in real-time.

Proactive AI-powered monitoring prevents data issues: ML-driven anomaly detection identifies schema drift, outliers, and freshness problems early in the pipeline—before they impact downstream systems.
Predictable costs with full data coverage: Unlike sampling-based tools, Actian processes every data record on an isolated compute layer, delivering no-cost surge assurance and avoiding cloud bill spikes.
Flexible, open architecture for modern data stacks: Supports Apache Iceberg and integrates across data lakes, lakehouses, and warehouses without vendor lock-in or performance degradation on production systems.

The Real Cost of Reactive Data Quality

Gartner® estimates that “By 2026, 50% of enterprises implementing distributed data architectures will have adopted data observability tools to improve visibility over the state of the data landscape, up from less than 20% in 2024”. But data observability goes beyond monitoring—it’s a strategic enabler for building trust in data while controlling the rising data quality costs across the enterprise.

Today’s enterprise data stack is a patchwork of old and new technologies—complex, fragmented, and hard to manage. As data flows from ingestion to storage, transformation, and consumption, the risk of failure multiplies. Traditional methods can’t keep up anymore.

Data teams lose up to 40% of their time fighting fires instead of focusing on strategic value.
Cloud spend continues to surge, driven by inefficient and reactive approaches to data quality.
AI investments fall short when models are built on unreliable or incomplete data.
Compliance risks grow as organizations lack the visibility needed to trace and trust their data.

Today’s data quality approaches are stuck in the past:

1. The Legacy Problem

Traditional data quality methods have led to a perfect storm of inefficiency and blind spots. As data volumes scale, organizations struggle with manual rule creation, forcing engineers to build and maintain thousands of quality checks across fragmented systems. The result? A labor-intensive process that relies on selective sampling, leaving critical data quality issues undetected. At the same time, monitoring remains focused on infrastructure metrics—like CPU and memory—rather than the integrity of the data itself.

The result is fragmented visibility, where issues in one system can’t be connected to problems elsewhere—making root cause analysis nearly impossible. Data teams are stuck in a reactive loop, chasing downstream failures instead of preventing them at the source. This constant firefighting erodes productivity and, more critically, trust in the data that underpins key business decisions.

Manual, rule-based checks don’t scale—leaving most datasets unmonitored.
Sampling to cut costs introduces blind spots that put critical decisions at risk.
Monitoring infrastructure alone ignores what matters most: the data itself.
Disconnected monitoring tools prevent teams from seeing the full picture across pipelines.

2. The Hidden Budget Drain

The move to cloud data infrastructure was meant to optimize costs—but traditional observability approaches have delivered the opposite. As teams expand monitoring across their data stack, compute-intensive queries drive unpredictable cost spikes on production systems. With limited cost transparency, it’s nearly impossible to trace expenses or plan budgets effectively. As data scales, so do the costs—fast. Enterprises face a difficult choice: reduce monitoring and risk undetected issues, or maintain coverage and justify escalating cloud spend to finance leaders. This cost unpredictability is now a key barrier to adopting enterprise-grade data observability.

Inefficient processing drives excessive compute and storage costs.
Limited cost transparency makes optimization and budgeting a challenge.
Rising data volumes magnify costs, making scalability a growing concern.

3. The Architecture Bottleneck

Most data observability solutions create architectural handcuffs that severely limit an organization’s technical flexibility and scalability. These solutions are typically designed as tightly integrated components that become deeply embedded within specific cloud platforms or data technologies, forcing organizations into long-term vendor commitments and limiting future innovation options.

When quality checks are executed directly on production systems, they compete for critical resources with core business operations, often causing significant performance degradation during peak periods—precisely when reliability matters most. The architectural limitations force data teams to develop complex, custom engineering workarounds to maintain performance, creating technical debt and consuming valuable engineering resources.

Tightly coupled solutions that lock you into specific platforms.
Performance degradation when running checks on production systems.
Inefficient resource utilization requiring custom engineering.

Actian Brings a Fresh Approach to Data Reliability

Actian Data Observability represents a fundamental shift from reactive firefighting to proactive data reliability. Here’s how we’re different:

1. Proactive, Not Reactive

TRADITIONAL WAY: Discovering data quality issues after they’ve impacted business decisions.
ACTIAN WAY: AI-powered anomaly detection that catches issues early in the pipeline using ML-driven insights.

2. Predictable Cloud Economics

TRADITIONAL WAY: Unpredictable cloud bills that surge with data volume.
ACTIAN WAY: No-cost-surge guarantee with efficient architecture that optimizes resource consumption.

3. Complete Coverage, No Sampling

TRADITIONAL WAY: Sampling data to save costs, creating critical blind spots.
ACTIAN WAY: 100% data coverage without compromise through intelligent processing.

4. Architectural Freedom

TRADITIONAL WAY: Vendor lock-in with limited integration options.
ACTIAN WAY: Open architecture with native Apache Iceberg support and seamless integration across modern data stacks.

Real-World Impact

Use Case 1: Data Pipeline Efficiency With “Shift-Left”

Transform your data operations by catching issues at the source:

Implement comprehensive DQ checks at ingestion, transformation, and source stages.
Integrate with CI/CD workflows for data pipelines.
Reduce rework costs and accelerate time-to-value.

Use Case 2: GenAI Lifecycle Monitoring

Ensure your AI initiatives deliver business value:

Validate training data quality and RAG knowledge sources.
Monitor for hallucinations, bias, and performance drift.
Track model operational metrics in real-time.

Use Case 3: Safe Self-Service Analytics

Empower your organization with confident data exploration:

Embed real-time data health indicators in catalogs and BI tools.
Monitor dataset usage patterns proactively.
Build trust through transparency and validation.

The Actian Advantage: Five Differentiators That Matter

No Data Sampling: 100% data coverage for comprehensive observability.
No Cloud Cost Surge Guarantee: Predictable economics at scale.
Secured Zero-Copy Architecture: Access metadata without costly data copies.
Scalable AI Workloads: ML capabilities designed for enterprise scale.
Native Apache Iceberg Support: Unparalleled observability for modern table formats.

Get Started

Take a product tour and better understand how to transform your data operations from reactive chaos to proactive control.

About Emma McGrattan

Emma McGrattan is CTO at Actian, leading global R&D in high-performance analytics, data management, and integration. With over two decades at Actian, Emma holds multiple patents in data technologies and has been instrumental in driving innovation for mission-critical applications. She is a recognized authority, frequently speaking at industry conferences like Strata Data, and she's published technical papers on modern analytics. In her Actian blog posts, Emma tackles performance optimization, hybrid cloud architectures, and advanced analytics strategies. Explore her top articles to unlock data-driven success.

Data Governance

The Governance Gap: Why 60% of AI Initiatives Fail

Dee Radh

May 1, 2025

governance gap and why ai initiatives can fail

Summary

This blog presents a critical insight: without modern, proactive governance, a majority of AI initiatives will fail to deliver value. It explains what causes breakdowns and how federated, context-aware practices can close the “governance gap.”

Gartner projects that 60% of AI projects will miss their value targets by 2027 due to fragmented, reactive governance structures that don’t align with business objectives.
Common pitfalls include compliance-driven rollouts, siloed teams, and outdated tools, hindering scalability and strategic impact.
A modern solution involves federated data governance via active metadata, context-rich data catalogs, and “shift-left” stewardship at the source—empowering decentralized teams while ensuring oversight.

AI initiatives are surging, and so are the expectations. According to Gartner, nearly 8 in 10 corporate strategists see AI and analytics as critical to their success. Yet there’s a sharp disconnect: Gartner also predicts that by 2027, most organizations, 60%, will fail to realize the anticipated value of their AI use cases because of incohesive data governance frameworks.

What’s holding enterprises back isn’t intent or even IT investments. It’s ineffective data processes that impact quality and undermine trust. For too many organizations, data governance is reactive, fragmented, and disconnected from business priorities.

The solution isn’t more policies or manual controls. It’s modern technology, with a modern data catalog and data intelligence platform as the cornerstones. Modern catalogs can play a key role in data management and governance strategies.

Why Governance Efforts Fail

While many organizations strive toward and commit to better data governance, they often fall short of their goals. That’s because governance programs typically suffer from one of three common pitfalls:

They’re launched in response to compliance failures, not strategic goals.
They struggle to scale due to legacy tools and siloed teams.
They lack usable frameworks that empower data stewards and data users.

According to Gartner, the top challenges to establishing a data governance strategy include talent management (62%), establishing data management best practices (58%), and understanding third-party compliance (43%). With these issues at play, it’s no wonder that data governance remains more aspirational than operational.

Shifting this narrative requires organizations to embrace a modern approach to data governance. This approach entails decentralizing control to business domains, aligning governance with business use cases, and building trust and understanding of data among all users across the organization. That’s where a modern data catalog comes into play.

Going Beyond Traditional Data Catalogs

Traditional data catalogs can provide an inventory of data assets, but that only meets one business need. A modern data catalog goes much further by embedding data intelligence and adaptability into data governance, making it more beneficial and intuitive for users.

Here’s how:

Shift-Left Capabilities for Data Stewards

Moving data governance responsibility upstream can enable new benefits. This shift-left approach empowers data stewards at the source—where data is created and understood the best, supporting context-aware governance and decentralized data ownership.

With granular access controls, flexible metamodeling, and business glossaries, data stewards can apply governance policies when and where they make the most sense. The result? Policies that are more relevant, data that’s more reliable, and teams that gain data ownership and confidence, not friction or bottlenecks.

Federated Data Governance With Active Metadata

A modern data catalog supports federated governance by allowing teams to work within their own domains while maintaining shared standards. Through active metadata, data contracts, and data lineage visualization, organizations gain visibility and control of their data across distributed environments.

Rather than enforcing a rigid, top-down approach to governance, a modern catalog uses real-time insights, shared definitions, and a contextual understanding of data assets to support governance. This helps mitigate compliance risk and promotes more responsible data usage.

Adaptive Metamodeling for Evolving Business Needs

Governance frameworks must evolve as data ecosystems expand and regulations change. Smart data catalogs don’t force teams into a one-size-fits-all model. Instead, they enable custom approaches and metamodels that grow and adapt over time.

From supporting new data sources to aligning with emerging regulations, adaptability helps ensure governance keeps pace with the business, not the other way around. This also promotes governance across the organization, encouraging data users to see it as a benefit rather than a hurdle.

Support Effective Governance With the Right Tools

Adopting a modern data catalog isn’t just about using modern features. It’s also about providing good user experiences. That’s why for data governance to succeed, tools must integrate seamlessly and work intuitively for users at all skill levels.

This experience includes simplifying metadata collection and policy enforcement for IT and data stewards, and providing intuitive search and exploration capabilities that make data easy to find, understand, and trust for business users. For all groups of users, the learning curve should be short, encouraging data governance without being limited by complex processes.

By supporting all types of data producers and data consumers, a modern data catalog eliminates the silos that often stall governance programs. It becomes the connective tissue that aligns people, processes, and policies around a shared understanding of data.

Go From Data Governance Aspirations to Outcomes

Most organizations know that data governance is essential, yet few have the right tools and processes to fully operationalize it. By implementing a modern data catalog like the one from Actian, organizations can modernize their governance efforts, empower their teams, and deliver sustainable business value from their data assets.

Organizations need to ask themselves a fundamental question: “Can we trust our data?” With a modern data catalog and strong governance practices, the answer becomes a confident yes.

Find out how to ensure accessible and governed data for AI and other use cases by exploring our data intelligence platform with an interactive product tour or demo.

About Dee Radh

As Senior Director of Product Marketing, Dee Radh heads product marketing for Actian. Prior to that, she held senior PMM roles at Talend and Formstack. Dee has spent 100% of her career bringing technology products to market. Her expertise lies in developing strategic narratives and differentiated positioning for GTM effectiveness. In addition to a post-graduate diploma from the University of Toronto, Dee has obtained certifications from Pragmatic Institute, Product Marketing Alliance, and Reforge. Dee is based out of Toronto, Canada.

Data Management

Life in an Enterprise Data Team: Before and After Data Intelligence

Dee Radh

April 30, 2025

In the world of enterprise data management, there’s perhaps no image more viscerally recognizable to data professionals than the infamous “Rube Goldberg Data Architecture” diagram. With its tangled web of arrows connecting disparate systems, duplicate data repositories, and countless ETL jobs, it perfectly captures the reality many organizations face today: data chaos.

Life Before a Data Catalog

Imagine starting your Monday morning with an urgent request: “We need to understand how customer churn relates to support ticket resolution times.” Simple enough, right?

Without a data catalog or metadata management solution, your reality looks something like this:

The Dig

You start by asking colleagues which data sources might contain the information you need. Each person points you in a different direction. “Check the CRM system,” says one. “I think that’s in the marketing data lake,” says another. “No, we have a special warehouse for customer experience metrics,” chimes in a third.

The Chase

Hours are spent exploring various systems. You discover three different customer tables across separate data warehouses, each with slightly different definitions of what constitutes a “customer.” Which one is the source of truth? Nobody seems to know.

The Trust Crisis

After cobbling together data from multiple sources, you present your findings to stakeholders. Immediately, questions arise: “Are you sure this data is current?” “How do we know these calculations are consistent with the quarterly reports?” “Which department owns this metric?” Without clear lineage, business glossary or governance, confidence in your analysis plummets.

The Redundancy Trap

A week later, you discover a colleague in another department conducted almost identical analysis last month. Their results differ slightly from yours because they used a different data source. Both of you wasted time duplicating efforts, and now the organization has conflicting insights.

This scenario reflects what MIT Technology Review described in their article “Evolution of Intelligent Data Pipelines”: complex data environments with “thousands of data sources, feeding tens of thousands of ETL jobs.” The result is what Bill Schmarzo aptly illustrated – a Rube Goldberg machine of data processes that’s inefficient, unreliable, and ultimately undermines the strategic value of your data assets.

Enter the Data Catalog:

Now, let’s reimagine the same scenario with a data intelligence solution like Actian in place.

Knowledge Graph-Powered Discovery in Minutes, Not Days

That Monday morning request now begins with an intelligent search in your data catalog. Leveraging knowledge graph technology, the system understands semantic relationships between data assets and business concepts. Within moments, you’ve identified the authoritative customer data source and the precise metrics for support ticket resolution times. The search not only finds exact matches but understands related concepts, synonyms, and contextual meanings, surfacing relevant data you might not have known to look for.

Federated Catalogs With a Unified Business Glossary

Though data resides in multiple systems across your organization, the federated catalog presents a unified view. Every term has a clear definition in the business glossary, ensuring “customer” means the same thing across departments. This shared vocabulary eliminates confusion and creates a common language between technical and business teams, bridging the perennial gap between IT and business users.

Comprehensive Lineage and Context

Before running any analysis, you can trace the complete lineage of the data – seeing where it originated, what transformations occurred, and which business rules were applied. The catalog visually maps data flow across the entire enterprise architecture, from source systems through ETL processes to consumption endpoints. This end-to-end visibility provides critical context for your analysis and builds confidence in your results.

Integrated Data Quality and Observability

Quality metrics are embedded directly in the catalog, showing real-time scores for completeness, accuracy, consistency, and timeliness. Automated monitoring continuously validates data against quality rules, with historical trends visible alongside each asset. When anomalies are detected, the system alerts data stewards, while the lineage view helps quickly identify root causes of issues before they impact downstream analyses.

Data Products and Marketplace

You discover through the catalog that the marketing team has already created a data product addressing this exact need. In the data marketplace, you find ready-to-use analytics assets combining customer churn and support metrics, complete with documentation and trusted business logic. Each product includes clear data contracts defining the responsibilities of providers and consumers, service level agreements, and quality guarantees. Instead of building from scratch, you simply access these pre-built data products, allowing you to deliver insights immediately rather than starting another redundant analysis project.

Regulatory Compliance and Governance by Design

Questions about data ownership, privacy, and compliance are answered immediately. The catalog automatically flags sensitive data elements, shows which regulations apply (GDPR, CCPA, HIPAA, etc.), and verifies your authorization to access specific fields. Governance is built into the discovery process itself – the system only surfaces data you’re permitted to use and provides clear guidance on appropriate usage, ensuring compliance by design rather than as an afterthought.

Augmented Data Stewardship

The catalog shows that the customer support director is the data owner for support metrics, that the data passed its most recent quality checks, and that usage of these specific customer fields is compliant with privacy regulations. Approval workflows, access requests, and policy management are integrated directly into the platform, streamlining governance processes while maintaining robust controls.

Discovery in Minutes, Not Days

That Monday morning request now begins with a quick search in your data catalog. Within moments, you’ve identified the authoritative customer data source and the precise metrics for support ticket resolution times. The system shows you which tables contain this information, complete with detailed descriptions.

Tangible Benefits

The MIT Technology Review article highlights how modern approaches to data management have evolved to address exactly these challenges, enabling “faster data operations through both abstraction and automation.” With proper metadata management, organizations experience:

Reduced time-to-insight: Analysts spend less time searching for data and more time extracting value from it
Enhanced data governance: Clear ownership, lineage, and quality metrics build trust in data assets
Automated data quality monitoring: The system continually observes and monitors data against defined quality rules, alerting teams when anomalies or degradation occur
SLAs and expectations: Clear data contracts between producers and consumers establish shared expectations about the usage and reliability of data products
Improved collaboration: Teams build on each other’s work rather than duplicating efforts
Greater agility: The business can respond faster to changing conditions with reliable data access

From Rube Goldberg to Renaissance

The “Rube Goldberg Data Architecture” doesn’t have to be your reality. As data environments grow increasingly complex, data intelligence solutions like Actian become essential infrastructure for modern data teams.

By implementing a robust data catalog, organizations can transform the tangled web depicted in Schmarzo’s illustration into an orderly, efficient ecosystem where data stewards and consumers spend their time generating insights, not hunting for elusive datasets or questioning the reliability of their findings.

The competitive advantage for enterprises doesn’t just come from having data – it comes from knowing your data. A comprehensive data intelligence solution isn’t just an operational convenience; it’s the foundation for turning data chaos into clarity and converting information into impact.

This blog post was inspired by Bill Schmarzo’s “Rube Goldberg Data Architecture” diagram and insights from MIT Technology Review’s article “Evolution of Intelligent Data Pipelines.”

About Dee Radh

Data Governance

Implementing Data Governance: A Step-by-Step Guide

Actian Corporation

April 25, 2025

Summary

This blog provides a clear, practical roadmap to building a successful data governance program—from setting objectives and defining roles to integrating policies, tools, monitoring, and culture for scalable execution.

Align strategy with business goals and define clear objectives (e.g., data quality, security, compliance, discoverability) to ensure the governance program delivers measurable outcomes.
Assign stakeholder roles and structure ownership by identifying data owners, stewards, executives, and forming a governance council to promote accountability and consistent policy enforcement.
Deploy policies, tools, training, and monitoring through steps like inventory/classification, policy definition, access controls, audits, observability integration, and employee education to embed governance into daily operations.

Data governance isn’t just about compliance—it’s about taking control over your data. For organizations managing fast-growing data ecosystems, governance determines whether data is trusted, usable, and secure across the business.

But too often, governance efforts stall. Siloed ownership, inconsistent policies, and a lack of visibility make it difficult to enforce organization-wide standards or scale. That’s why successful programs combine a clear strategy with tools that surface issues early, clarify responsibilities, and make governance part of day-to-day data operations, not an afterthought.

modern data and analytics governance Image courtesy of Gartner.

To make data governance sustainable and impactful, it must be aligned with business priorities and flexible enough to evolve with organizational needs. Too often, governance programs are implemented in isolation—rigid in design and disconnected from how data is actually used. That disconnect has real consequences: according to Gartner, by 2027, 60% of AI initiatives will fail to deliver expected outcomes due to fragmented governance frameworks.

A modern governance roadmap should emphasize tangible outcomes, continuous improvement, and adaptability. That means:

Establishing a clear and scalable governance structure.
Defining practical policies and standards that reflect real data usage.
Continuously measuring performance and adjusting where needed.
Fostering a culture of ongoing learning and iteration.

This step-by-step guide walks through a practical approach to data governance—from defining ownership and policies to enabling secure access and monitoring enforcement at scale.

Step 1: Define the Objectives of Data Governance

Before launching any tools or technologies, it’s essential to first define the key objectives of the organization’s data governance initiative. This will serve as the foundation for the overall strategy and ensure that all efforts align with the broader goals of the organization.

Key Considerations

Connect to all your data and overcome the challenge of data silos.
Work with trusted data that is high quality and compliant.
Ensure data security, privacy, and compliance.
Enable governed data sharing across teams.
Empower data consumers to easily discover and use the right data.

Step 2: Identify Data Stakeholders and Data Ownership

Next, identify the key stakeholders involved in the management and use of data within the organization. This typically includes data stewards, business users, IT teams, legal and compliance officers, and executives. Defining clear roles and responsibilities for data ownership ensures that accountability is distributed, and data governance policies are consistently enforced.

Step 3: Conduct a Data Inventory and Classification

Data inventory and classification are crucial steps for identifying and managing an organization’s data assets. This involves cataloging all available data assets and sources, understanding where the data resides, and classifying it based on its sensitivity, value, and usage.

Step 4: Define Data Policies and Standards

After understanding an organization’s data landscape, decision makers need to define and implement policies and standards that govern data usage, security, and quality. These may include data access policies, data retention policies, and data security standards. Clear policies ensure that data is used responsibly and in compliance with applicable regulations throughout the organization.

Step 5: Implement Data Security and Privacy Controls

Data security and privacy are at the heart of any data governance initiative. Depending on the type of data being handled, organizations may need to implement encryption, access control, and monitoring measures to protect sensitive data. This includes ensuring compliance with relevant regulations such as GDPR or HIPAA, which govern personal and medical information.

Step 6: Enable Data Access and Collaboration

Data governance shouldn’t hinder the free flow of information within an organization. Instead, it should enable responsible access to data while maintaining security. It’s important to ensure that data can be easily accessed by authorized users and that collaboration between teams is facilitated.

Step 7: Monitor and Enforce Data Governance Policies

Data governance is an ongoing process that requires continuous monitoring and enforcement. Regular audits, reviews, and updates to governance policies are necessary to adapt to new business needs, technological changes, and evolving compliance requirements.

Step 8: Educate and Train Employees

A successful data governance strategy requires buy-in and participation from all levels of the organization. Employees need to understand the importance of data governance, their role in maintaining data quality, and the consequences of non-compliance.

Data Governance and Observability: Cornerstones to a More Robust Data Foundation

Data governance often breaks down where it matters most—in execution. Policies are defined, but not enforced. Ownership is assigned, but not followed through. And without visibility into how data flows and changes, issues go unnoticed until they create real damage.

That’s where enterprise-grade data observability adds power to your governance strategy. It gives teams real-time visibility into data quality, helps reconcile inconsistencies across systems, and makes it easier to monitor policy enforcement at scale. The result: a more automated, trusted, and scalable foundation for delivering AI-ready data across the business.

About Actian Corporation

Data Governance

HIPAA Data Governance: What You Need to Know

Actian Corporation

April 23, 2025

Safeguarding patient data is more critical than ever as most patient data is now digitized. The Health Insurance Portability and Accountability Act (HIPAA) provides a comprehensive framework for protecting the privacy and security of health information.

However, compliance with HIPAA is not just about following a set of rules; it’s about implementing robust healthcare data governance strategies to ensure that health information is managed, protected, and used responsibly.

In this article, we’ll look at the types of organizations that are expected to comply with HIPAA regulations, the different ways HIPAA can be violated, the consequences for violating HIPAA, and the steps an organization can take to successfully implement HIPAA data governance.

Who Needs to Follow HIPAA Guidelines?

HIPAA guidelines apply to a wide range of individuals, organizations, and businesses that handle Protected Health Information (PHI) in the United States. The following entities and individuals are required to follow HIPAA guidelines:

Covered entities: Organizations or individuals who directly handle PHI are subject to HIPAA regulations, including healthcare providers, health insurance companies, health maintenance organizations, employer health plans, and healthcare clearinghouses.
Business associates: Third-party vendors or contractors that work with covered entities and have access to PHI to perform services on their behalf are also subject to HIPAA regulations. These include data storage providers, IT and security vendors, billing and coding companies, and legal and accounting firms.
Healthcare workers and employees: All employees, contractors, or anyone working for a covered entity or business associate who has access to PHI must adhere to HIPAA regulations. This includes doctors and nurses, administrative staff, medical researchers, and support staff.
Individuals handling health information: Any individual who works with or has access to health data, even if not directly involved in providing healthcare, must follow HIPAA rules to protect patient information. This can include employees in various industries like law firms, insurance companies that handle medical information, and health technology.
State and local governments: Government agencies that manage or use PHI in healthcare-related programs like Medicaid, public health services, etc., also need to comply with HIPAA regulations to protect health data.
Healthcare apps and tech companies: As healthcare data is increasingly digitized, technology companies that develop or provide healthcare apps, patient portals, and telemedicine platforms may also be required to comply with HIPAA if they process or store PHI.

What are HIPAA Violations?

HIPAA violations occur when an individual or organization fails to comply with the provisions set out by the Health Insurance Portability and Accountability Act (HIPAA). These violations can range from accidental breaches to intentional misconduct, and they typically involve the unauthorized access, disclosure, or mishandling of PHI. Violations can occur in various forms, whether due to negligence, poor security practices, or malicious intent.

Types of HIPAA violations include:

Unauthorized access to PHI.
Failure to implement safeguards.
Improper disposal of PHI.
Failure to report data breaches.
Unauthorized disclosure of PHI.
Lack of Business Associate Agreements (BAAs).
Failure to implement proper access controls.

What are the HIPAA Violation Penalties?

Violating HIPAA can result in serious consequences, including civil and criminal penalties, civil lawsuits, and reputation damage.

Civil Penalties

The U.S. Department of Health and Human Services (HHS) may impose fines for violations. These penalties can range from $100 to $50,000 per violation, depending on the severity of the breach and whether the violation was due to willful neglect.

The total penalty can be as high as $1.5 million per year for violations of the same provision.

Criminal Penalties

For more severe violations, such as knowingly acquiring or disclosing PHI without authorization, criminal penalties can be imposed, including fines and imprisonment:

Up to $50,000 and up to 1 year in prison for offenses committed without malicious intent or for personal gain.
Up to $100,000 and up to 5 years in prison for offenses committed under false pretenses.
Up to $250,000 and up to 10 years in prison for offenses committed with the intent to sell or distribute PHI.

Civil Lawsuits

In some cases, patients whose PHI has been improperly disclosed may file civil lawsuits against the violator.

Reputation Damage

A HIPAA violation can cause significant damage to an organization’s reputation. Public disclosure of a breach can lead to a loss of trust among patients and clients, resulting in a decline in business.

How to Implement HIPAA Data Governance

For a business or organization to Implement HIPAA data governance, it needs to create and enforce policies, procedures, and controls to ensure the protection, security, and privacy of Protected Health Information (PHI). Effective data governance helps safeguard sensitive health data, reduce the risk of data breaches, and ensure the organization meets legal and regulatory obligations.

Here’s a step-by-step approach to implementing HIPAA data governance:

1. Establish a Data Governance Framework

A solid framework is essential for defining how PHI will be managed, protected, and shared within the organization. The data governance framework should be aligned with HIPAA’s key principles: confidentiality, integrity, and availability of PHI. Organizations should define data ownership, designate data stewards, and develop data governance policies.

2. Conduct a Data Inventory

Before implementing data governance practices, it’s necessary to understand the types of PHI an organization handles, where it’s stored, how it’s used, and who has access to it. Map out where PHI resides and who has access to it, and perform a risk assessment to identify vulnerabilities in the current system that could compromise PHI security.

3. Implement Access Control Mechanisms

HIPAA requires that only authorized individuals can access PHI. Proper access controls are critical to data governance. Implement a system that grants access to PHI based on job roles and use multi-factor authentication and secure password policies to strengthen access controls. It’s also a good idea to make sure that employees and contractors only have access to the minimum amount of PHI necessary to perform their job duties.

4. Establish Data Protection and Security Measures

Implement data security practices to protect PHI from unauthorized access, alteration, or destruction. It’s possible to do this by using encryption to protect PHI both in transit (such over the internet or through email) and at rest, when stored on servers or devices. Ensure that all critical PHI is regularly backed up and that there is a disaster recovery plan in place in case of system failures, natural disasters, or cyber-attacks.

Implement firewalls, anti-malware software, and intrusion detection systems to detect and prevent unauthorized access attempts.

5. Monitor and Audit Access to PHI

Regular monitoring and auditing are essential to track access to PHI, identify potential breaches, and ensure compliance with HIPAA requirements. Maintain detailed audit trails that track who accessed PHI, what actions they performed, and when it occurred. This can help identify potential security threats or non-compliant behavior.

Organizations should perform regular audits of system activity to detect any unauthorized access or misuse of PHI. These audits should be part of an ongoing compliance program and use tools that provide real-time monitoring of systems and alerts for suspicious activities involving PHI.

6. Ensure Proper Data Retention and Disposal

HIPAA requires that PHI be retained for a certain period, and that it be securely disposed of when no longer needed. Failure to properly manage data retention and disposal can result in violations.

Develop and enforce policies specifying how long different types of PHI should be retained. Retain records according to HIPAA’s minimum necessary retention periods or as required by law. When PHI is no longer needed, ensure it is securely deleted. This can involve securely wiping electronic devices or shredding physical records.

7. Conduct Regular Staff Training and Awareness

Employees must understand the importance of HIPAA compliance and their role in protecting PHI. Provide initial and ongoing training to all employees, contractors, and business associates about HIPAA’s privacy and security requirements. Training should cover access control, data handling, and breach response protocols.

Foster a culture of security and privacy within the organization by regularly reminding staff of their responsibility to safeguard PHI and encouraging them to report potential security incidents.

8. Develop a Breach Response Plan

A breach response plan ensures that if PHI is compromised, the organization can respond quickly and in accordance with HIPAA’s notification requirements.

Implement systems to detect and report breaches immediately. This includes monitoring for signs of unauthorized access or data loss. In the event of a breach, HIPAA requires covered entities to notify affected individuals, the Department of HHS, and in some cases, the media. Make sure the plan includes these requirements and timelines for notification (within 60 days of discovery of a breach).

Designate an incident response team to handle breaches and mitigate potential damage. This team should be trained and ready to respond to any potential violation of PHI security.

9. Create Business Associate Agreements (BAAs)

If an organization works with third-party vendors or contractors (business associates) who have access to PHI, it should ensure that there is a Business Associate Agreement (BAA) in place.

The BAA should outline how the business associate will handle PHI and their responsibilities for maintaining security and compliance with HIPAA standards. Ensure that all existing BAAs are up-to-date and in compliance with HIPAA, especially if business associates change their practices or security measures.

10. Continuous Improvement and Compliance Monitoring

HIPAA compliance is an ongoing process, so it’s important to continuously review and improve data governance practices. Regularly conduct internal audits and assessments to evaluate the effectiveness of the organization’s data governance policies and identify any potential gaps.

HIPAA regulations can evolve, so it’s crucial to stay informed about any changes to HIPAA standards and incorporate them into the data governance strategy. Consider using third-party auditors or penetration testers to assess the data governance program and identify vulnerabilities that may need to be addressed.

Implementing HIPAA data governance is a comprehensive process that requires a clear framework, access controls, data protection measures, training, and continuous monitoring. By following best practices and staying proactive about compliance, businesses and organizations can effectively protect PHI, mitigate risks, and ensure they meet HIPAA’s stringent privacy and security requirements.

Partner With Actian for Data Discovery and Governance Needs

Actian provides advanced solutions for data discovery, governance, and lineage tracking. With powerful automation and integration capabilities, Actian’s platform helps businesses maintain accurate data lineage, ensure compliance, and optimize data management. By partnering with Actian, organizations can gain better control over their data assets and drive informed decision-making.

About Actian Corporation

Data Governance

The Crucial Role of Technology in Ensuring BCBS 239 Compliance

Kasey Nolan

April 22, 2025

If you’re just joining us, start with Part 1: An Introduction to BCBS 239, then continue with Part 2: Overcoming Challenges in BCBS 239 Implementation.

Typically, in the wake of financial crises, regulatory standards significantly tightened, imposing stringent demands for greater transparency and efficiency in bank risk management practices. The Basel Committee on Banking Supervision’s standard 239 (BCBS 239) specifically targets the critical areas of risk data aggregation and risk reporting. This standard underscores the need for robust governance and advanced technological frameworks to manage and report risk accurately. Let’s explore how technology is not merely an aid but a central pillar in achieving compliance with rigorous regulations.

The Problem: Integrating Data and Ensuring Accuracy

BCBS 239 presents a formidable challenge, prompting some banks to thoroughly overhaul their risk data aggregation and reporting processes. Traditionally, financial institutions have grappled with data being siloed across disparate, often incompatible systems. This fragmentation can lead to inconsistent data sets, obscuring a unified view of risk profiles, particularly under stress conditions.

The reliance on manual data handling processes compounds these issues, being not only time-consuming but also fraught with potential for errors. Consequently, achieving the high standards of accuracy and timeliness demanded by BCBS 239 becomes a significant challenge.

The Solution: Data Accessibility, Governance, and Trust

Addressing the demands of BCBS 239 requires banks to embrace technology, particularly through the use of metadata management platforms. These platforms are instrumental in transforming the landscape of risk data aggregation and reporting by providing a comprehensive solution that enhances data accessibility, integrity, and governance. Here’s a closer look at how they meet the core requirements of BCBS 239:

Centralized Data Governance: Metadata management platforms facilitate centralized visualization of data assets, ensuring that all data elements are accurately defined and maintained consistently across the organization. This uniform data governance is vital for compliance because it eliminates discrepancies and significantly enhances data integrity by ensuring that everyone within the institution adheres to the same data standards.
Enhanced Data Quality and Lineage: These metadata management platforms are equipped with tools that bolster the quality and traceability of data. By meticulously tracking the origin, movement, and modifications of data, banks can guarantee that the information utilized for risk reporting is precise and can be traced back to its source. This traceability is crucial for meeting the transparency requirements of BCBS 239.

The Benefits: Having Confidence in Compliance

Implementing metadata management platforms streamlines the compliance process, markedly reducing the complexities and resource demands typically associated with adherence to BCBS 239. These platforms significantly bolster risk management capabilities by enhancing the accuracy and accessibility of data, thereby providing banks with a more detailed and comprehensive view of their risk profiles.

This improved data landscape facilitates more informed and confident decision-making throughout the organization. Moreover, the increased consistency, timeliness, and accuracy in reporting not only ensure regulatory compliance but also substantially mitigate the risk of penalties arising from non-conformance.

What it All Boils Down To

As the financial industry continues to navigate the post-crisis regulatory environment, the role of technology in ensuring compliance with standards like BCBS 239 has become indispensable. Banks that proactively adopt advanced metadata management technologies will find themselves better equipped to meet these challenges, ensuring they not only comply with current regulations but are also poised to adapt to future demands in an ever-evolving regulatory landscape.

Actian for BCBS239

To learn how the Actian Data Intelligence Platform can transform a bank’s approach to BCBS 239 compliance and see firsthand Actian’s advanced metadata management capabilities, try an interactive product tour today!

About Kasey Nolan

Kasey Nolan is Solutions Product Marketing Manager at Actian, aligning sales and marketing in IaaS and edge compute technologies. With a decade of experience bridging cloud services and enterprise needs, Kasey drives messaging around core use cases and solutions. She has authored solution briefs and contributed to events focused on cloud transformation. Her Actian blog posts explore how to map customer challenges to product offerings, highlighting real-world deployments. Read her articles for guidance on matching technology to business goals.

Data Governance

How to Ensure GDPR Compliance Using Data Governance

Actian Corporation

April 21, 2025

Professional analyzing code and data systems on screen

The General Data Protection Regulation (GDPR) is one of the most significant pieces of legislation impacting data protection and privacy in the European Union (EU). It came into effect on May 25, 2018, and established strict guidelines on how personal data should be collected, processed, stored, and shared. For organizations that handle personal data, GDPR compliance is not just a legal obligation, but also an essential part of maintaining trust with customers, partners, and stakeholders.

To ensure compliance with GDPR and promote responsible data management, organizations must embed data governance best practices into their operations. Data governance involves creating policies, standards, and procedures for handling data assets, ensuring that data is accurate, accessible, secure, and used appropriately. Below, we will explore key data governance best practices under the GDPR law.

1. Identify and Classify the Data

Begin by identifying and mapping all personal data within the organization. This includes understanding where the data comes from and how it’s processed, stored, and shared. Next, categorize the data based on sensitivity and its purpose to ensure proper handling and protection.

2. Minimize the Data

Only collect data that is necessary for the specific purpose for which it is being processed. Avoid collecting excessive data that isn’t required. Then, ensure that personal data is only used for the purposes it was collected and not repurposed without the individual’s consent.

3. Establish Access Control and Security

Implement role-based access controls (RBAC) so that only authorized personnel can access personal data. Organizations should also encrypt sensitive personal data at rest (such as on a hard drive) and in transit (such as online or through email) to protect it from unauthorized access. Where possible, companies should anonymize or pseudonymize data to reduce the risk of exposure in case of a data breach.

4. Build Privacy in From the Beginning

Ensure that privacy is built into business processes, systems, and operations from the outset. Implement default privacy settings that maximize data protection, such as default data sharing settings set to the most restrictive level.

5. Manage Data Subject Rights

Establish procedures to handle data subject rights – such as the right to access, correct, erase, or restrict processing of their data – within the timeframes mandated by GDPR. Provide clear instructions to data subjects on how to exercise their rights, ensuring ease of access and transparency.

6. Enforce Data Retention and Deletion Policies

Create and enforce clear data retention policies that specify how long personal data will be retained. Personal data should only be kept as long as necessary to fulfill the purpose for which it was collected. Additionally, implement a process for securely deleting data that is no longer needed, in accordance with retention policies. This includes ensuring that data is securely erased from all systems, backups, and storage devices.

7. Manage Vendors and Third Parties

Ensure that any third parties who process personal data on behalf of the organization (data processors) comply with GDPR by signing Data Processing Agreements (DPAs). These agreements should clearly outline roles, responsibilities, and data protection obligations. Regularly assess and audit third-party vendors to ensure they are maintaining the required level of data protection.

8. Develop a Data Breach Response Plan

Develop and maintain a robust data breach response plan that complies with GDPR’s 72-hour breach notification requirement. The plan should include immediate actions, internal notifications, and notifications to affected individuals and relevant authorities. Investigate any potential breaches thoroughly and document the findings. This includes tracking the impact and the corrective actions taken to mitigate the issue.

9. Document All Data Processing Activities

Maintain comprehensive records of data processing activities. Document what data is processed, why it’s processed, the legal basis for processing, and how long the data will be retained. Ensure that the organization’s data governance practices are well-documented to demonstrate compliance with GDPR during audits. This includes maintaining policies, training records, consent logs, and data processing agreements.

10. Conduct Data Protection Impact Assessments (DPIAs)

For high-risk data processing activities, such as large-scale processing of sensitive data, conduct Data Protection Impact Assessments (DPIAs) to identify and mitigate potential risks to the data subject’s privacy. Implement measures to mitigate identified risks, such as pseudonymization, encryption, or restricting access to the data.

11. Provide GDPR Training

Provide ongoing GDPR training for employees to ensure they understand their roles and responsibilities in protecting personal data. This should include the principles of data privacy, data subject rights, and the handling of sensitive data. Foster a culture of privacy within the organization by continuously raising awareness about GDPR compliance and data protection best practices.

12. Appoint a Data Protection Officer (DPO)

If required, appoint a Data Protection Officer (DPO) to oversee the organization’s data protection activities. The DPO will be responsible for ensuring GDPR compliance and acting as a point of contact for data subjects and regulatory authorities. The DPO should operate independently and have the authority to raise privacy concerns directly to top management.

13. Ensure Compliance With Data Transfers Outside the EU

If personal data is transferred outside the EU, ensure compliance with GDPR’s requirements for international data transfers. This may involve using Standard Contractual Clauses (SCCs), Binding Corporate Rules (BCRs), or ensuring that the destination country has an adequate level of data protection.

14. Audit Processes and Continue to Improve

Regularly audit data governance processes to ensure compliance with GDPR. These audits should assess data processing activities, security measures, access controls, and handling of data subject rights. Continuously monitor and update data governance practices to address new risks, changes in business processes, or updates to data protection laws.

By implementing these best practices, organizations can create a robust data governance framework that ensures compliance with GDPR, mitigates risks, and fosters trust with customers and stakeholders.

GDPR Compliance is Crucial to Data Governance Practices

GDPR compliance is integral to modern data governance practices, and implementing these best practices can help organizations safeguard personal data, foster trust, and avoid costly penalties. By creating a solid data governance framework, embedding data protection principles into every aspect of the organization, and maintaining ongoing compliance efforts, the organization will not only meet legal obligations but also gain a competitive advantage.

Get Data Governance Assistance From Actian

Actian has an all-in-one data intelligence platform that provides advanced governance solutions. It can help organizations ensure compliance with regulations like GDPR, manage data assets, and effectively leverage information for better decision-making. Try a tour of the platform today to get a better idea of how Actian can help businesses thrive amidst regulatory restrictions and increasing amounts of data to manage.

About Actian Corporation

Data Governance

A Guide to Data Quality Assurance

Actian Corporation

April 17, 2025

Summary

This blog defines data quality assurance (DQA), explains why it’s essential for decision-making and compliance, and outlines practical steps to ensure accurate, consistent, and reliable data across the enterprise.

DQA is a continuous process of monitoring, validating, and improving data using key quality dimensions like accuracy, completeness, and consistency.
Best practices include governance frameworks, validation rules, profiling and cleansing tools, employee training, and ongoing quality monitoring.
Actian Data Intelligence Platform supports DQA by enabling teams to detect issues early, manage metadata, and maintain trust in business-critical data.

The importance of high-quality data cannot be overstated. Data is the foundation for decision-making, analytics, machine learning models, and operational processes. However, the usefulness of data is heavily dependent on its quality. This is where data quality assurance (DQA) can help by ensuring that the data used by organizations is accurate, consistent, and reliable.

What is Data Quality Assurance (DQA)?

Data quality assurance is the process of monitoring, evaluating, and improving data quality to ensure that it meets predefined standards and is suitable for its intended purposes. It involves various techniques, methodologies, and tools that ensure the data used within an organization is both accurate and consistent, ultimately leading to better decision-making, improved efficiency, and more reliable insights.

DQA is not just about identifying and correcting errors in data, but also about preventing these errors from occurring in the first place. It’s a continuous process that requires proactive planning, monitoring, and evaluation at every stage of the data lifecycle.

Key Elements of Data Quality

Data quality can be assessed through several key dimensions. These include:

Accuracy: Ensuring that the data is correct and free from errors. Accuracy is crucial for making informed decisions and conducting reliable analyses.
Consistency: Data should be consistent across different systems, databases, and applications. Inconsistencies can arise when data is entered or updated in multiple places, leading to discrepancies.
Completeness: Data should be comprehensive and not missing critical information. Incomplete data can lead to skewed analysis and missed opportunities.
Timeliness: Data should be up-to-date and available when needed. Outdated data may lead to decisions based on old or irrelevant information.
Validity: Data must conform to predefined standards, formats, and rules. Invalid data can result in inaccurate analyses or even system failures.
Uniqueness: Ensuring there are no duplicate records or redundant entries that could impact the quality of the data set.
Relevance: Data should be pertinent to the specific objectives and context in which it is being used. Irrelevant data can clutter systems and make it harder to extract valuable insights.

Importance of Data Quality Assurance

Ensuring data quality is essential for several reasons. Let’s take a look at a few of them.

Improved Decision-Making

High-quality data enables organizations to make well-informed decisions. Whether it’s in business strategy, financial forecasting, or operational efficiency, decisions based on accurate, timely, and relevant data are more likely to succeed.

Operational Efficiency

Poor data quality can lead to inefficiencies, redundancies, and increased operational costs. For example, DQA can identify and rectify errors early, preventing them from cascading into larger operational issues. This reduces costs associated with correcting mistakes, such as billing errors or customer refunds, and avoids inefficiencies caused by poor data quality.

Regulatory Compliance

Many industries, such as healthcare and finance, must adhere to strict regulatory requirements regarding data accuracy and integrity. DQA helps organizations maintain compliance and avoid costly penalties.

Customer Trust

In an era where customer data is a critical asset, maintaining high data quality is key to building trust. If an organization continuously provides correct and timely information, customers are more likely to rely on and engage with the brand.

Enhanced Analytics and Reporting

Data quality directly impacts the effectiveness of data analysis and reporting. Clean, consistent, and accurate data ensures that reports and business intelligence tools provide reliable insights to drive positive outcomes.

Best Practices for Data Quality Assurance

To ensure effective data quality assurance, organizations should implement the following best practices:

Establish Clear Data Governance

Create a comprehensive data governance framework that outlines standards, policies, and procedures for managing data across the organization. This ensures that everyone follows the same guidelines and can maintain data quality throughout its lifecycle.

Implement Data Validation Rules

Use validation techniques at the point of data entry to ensure that only high-quality data enters the system. For example, use constraints, drop-down lists, and format checks to prevent invalid or incomplete data from being recorded. Additionally, data quality solutions can be added to the data pipeline to isolate invalid data for further remediation.

Regular Data Audits

Conduct regular data audits to identify discrepancies, errors, and inconsistencies in your data. Automated tools can help to track changes and monitor data quality in real time.

Use Data Profiling and Cleansing Tools

Data profiling tools help organizations to assess the quality of data in terms of structure, patterns, and completeness. Once data issues are identified, data cleansing tools can remove duplicates, correct errors, and standardize data for consistency.

Implement Training and Awareness

Ensure that all employees who handle data are trained in best practices for data entry and management. This includes understanding the importance of accurate data and how to spot potential issues.

Monitor Data Quality Continuously

Data quality is not a one-time effort. Continuous monitoring of data quality helps to identify emerging issues and resolve them before they can have a significant impact on the organization.

Collaborate Across Departments

DQA is not just an IT or data team responsibility. Data quality should be a cross-departmental effort. Engaging teams from operations, finance, marketing, and other departments ensures that data quality needs are met across the organization.

Ensure Data Accuracy With Actian’s Data Intelligence Platform

Data quality assurance is essential for organizations to ensure their data is accurate, consistent, and reliable. By implementing best practices, utilizing the right tools, and establishing a robust data governance framework.

Organization can ensure that their data is accurate by partnering with Actian. Our data intelligence platform helps organizations make better business decisions by enabling data users to quickly detect and act on a dataset’s quality to uncover its trustworthiness before an issue arises. Benefit from advanced metadata management to simplify search, exploration, governance, and compliance – all in one unified platform.

About Actian Corporation

Data Governance

Overcoming Challenges in BCBS 239 Implementation

Kasey Nolan

April 15, 2025

If you missed the first part of this series, check out An Introduction to BCBS 239.

Implementing and ensuring compliance with BCBS 239 principles can pose significant challenges, particularly regarding the integration of data across complex global operations and legacy systems. These include:

Technological Infrastructure: Modernizing legacy systems to handle advanced data aggregation and reporting
Cultural Shifts: Building a risk-aware culture that integrates data governance into daily operations
Skill Gaps: Developing expertise in managing and interpreting complex data systems
Data Silos: Integrating data from diverse sources within the bank to create a unified and comprehensive view

To overcome these challenges, many banks turn to technological solutions that automate and facilitate the governance and management of data. Tools that enhance data governance, provide clear data lineage, and support risk data aggregation are essential for compliance. These technologies not only help in adhering to BCBS 239 but also improve the overall efficiency and reliability of risk management practices.

Banks can support implementation and adherence by taking these actions:

Establish Robust Data Governance Frameworks

A comprehensive data governance framework is essential for ensuring that data across the organization is accurate, consistent, and accessible. This framework should define clear data ownership and accountability, establish data quality standards, and set procedures for data usage and maintenance. Platforms that facilitate strong data governance help institutions embed these practices into their operations, ensuring ongoing compliance and data integrity.

Automate Data Lineage Tracking

Automated data lineage tools are crucial for tracing data from its origin through its lifecycle, providing transparency and accountability. This capability allows banks to quickly identify the sources of their data, understand how it’s processed and transformed, and ensure its accuracy and completeness. Automation in data lineage not only supports compliance efforts, but also reduces the manual effort required, increasing efficiency and reducing the risk of errors.

Embrace Data Integration and Quality

Integrating data from various sources and ensuring its quality are pivotal for effective risk reporting. Solutions that offer robust integration capabilities and support data quality management can help banks consolidate their data into a unified view, crucial for accurate and comprehensive risk assessment. These tools help in cleansing, de-duplicating, and harmonizing data, which are key steps in preparing data for compliance-related reporting and analysis.

Foster a Culture of Compliance and Continuous Improvement

Lastly, fostering a culture that values compliance and continuous improvement is vital. This involves providing training and developing programs to enhance staff understanding and capabilities regarding risk data management and compliance. Tools that support ongoing monitoring and auditing of data practices help reinforce this culture, ensuring that compliance becomes an integral part of the organizational ethos.

Move From Challenge to Opportunity

Successfully implementing BCBS 239 principles is not just a regulatory necessity—it’s a strategic opportunity for banks to strengthen their approach to risk management. By investing in modern data governance frameworks, automating lineage tracking, integrating data across departments, and fostering a culture of compliance, financial institutions can transform a regulatory challenge into a competitive advantage.

The right combination of technology, talent, and culture not only ensures adherence to BCBS 239 but also builds a more agile, transparent, and resilient organization that’s better equipped to succeed in today’s complex financial landscape. Find out how Actian’s Data Intelligence Platform can help by taking a product tour or demo.

About Kasey Nolan

Data Management

Understanding Enterprise Data Catalogs

Actian Corporation

April 11, 2025

As companies collect vast amounts of data from diverse sources, managing, maintaining, and using that data effectively can quickly become overwhelming. This is where an enterprise data catalog comes into play.

An enterprise data catalog is a comprehensive inventory of an organization’s data assets. It provides a centralized, organized repository where data can be easily located, understood, and accessed by various stakeholders, including data scientists, analysts, engineers, and decision-makers. This article explores the importance of an enterprise data catalog, how it enhances data accessibility and usability, and how the Actian Data Intelligence Platform supports organizations in managing their data more efficiently.

What is an Enterprise Data Catalog?

An enterprise data catalog is a metadata management tool that organizes, indexes, and makes accessible all the data assets within an organization. Essentially, it serves as a comprehensive map of the data landscape of a company. It collects metadata about data sources, such as databases, data lakes, cloud storage, and data warehouses, and organizes this information in a structured, easily navigable manner.

At the heart of an enterprise data catalog is metadata, the information that describes data. Metadata includes not just the technical details (such as data type, structure, and location), but also business metadata (such as data owners, usage, and definitions). By centralizing this metadata, a data catalog allows users to gain deeper insights into the data and how it can be applied to solve business problems.

An enterprise data catalog often includes the following features:

Data Discovery: It scans all data sources and automatically discovers new data assets.
Data Compliance: It manages personal data at scale to reduce risk and help companies adhere to regulations.
Business Glossary: It enables a consistent language among all data consumers in an organization.
Metadata Management: It provides rich metadata that helps users understand the context, meaning, and relationships of the data.
Data Lineage: It tracks the flow and transformations of data from its source to its final destination, ensuring transparency and traceability.
Data Quality: It synchronizes with an organization’s data quality tools, allowing for easy access to data quality metrics from as early as the discovery phase.
Data Governance: It includes features that help enforce policies, data privacy regulations, and access controls.
Data Marketplace: It provides a centralized hub, allowing data teams to annotate, tag, and share insights, fostering collaboration across the organization.
Data Stewardship: It uses automated documentation capabilities to assist data stewards as they manage an organization’s data flow.
Search and Query Functionality: Users can easily search for data based on specific criteria or use advanced filtering options to find the most relevant data.

Why is an Enterprise Data Catalog Important?

The importance of an enterprise data catalog cannot be overstated. Here are some of the critical reasons why organizations should prioritize implementing a data catalog.

1. Improved Data Accessibility and Usability

Data often resides in silos across an organization. Different teams may use different systems and technologies, including varying databases and datasets, making it difficult to access and utilize the data effectively. An enterprise data catalog centralizes the metadata from all data sources, making it easier for users to find and access the data they need. Whether it’s a data scientist trying to discover the most relevant dataset for an analysis or a business user looking for historical sales data, the catalog ensures that the information is available and easily discoverable.

2. Enhanced Collaboration and Knowledge Sharing

In many organizations, teams work in isolation, with limited visibility into the work being done by other departments. A data catalog helps break down these silos by providing a shared platform for collaboration. It allows teams to annotate data with useful context, descriptions, and insights. This fosters knowledge sharing and reduces the duplication of efforts across the organization, saving time and money.

3. Simplified Data Governance and Compliance

As regulations like GDPR, HIPAA, and CCPA continue to shape the data privacy landscape, businesses are under pressure to ensure their data is managed properly. An enterprise data catalog is crucial for data governance. It helps organizations keep track of who owns the data, who has access to it, and how it is being used.

4. Higher Data Quality and Integrity

Maintaining high data quality is essential for making reliable business decisions. A well-organized data catalog provides transparency into the data’s lineage, helping organizations understand where data comes from and how it’s transformed over time. This is crucial for ensuring data integrity. By being able to trace the data’s journey, users can identify potential errors, inconsistencies, or gaps in the data, and address them proactively.

5. Faster Decision-Making

In fast-paced business environments, decision-makers need to have timely access to accurate data so they can pivot when needed. By centralizing and organizing the data, a catalog makes it easier to find the right information quickly. With proper metadata and data lineage, users can have confidence that the data they are using is reliable, helping them make better decisions more quickly.

6. Data Growth in Tandem With Organizational Growth

As organizations grow, so does their data. A well-implemented enterprise data catalog scales with the company’s expanding data ecosystem. It supports various data sources and formats, whether on-premises or in the cloud, ensuring that the catalog remains relevant and effective as the data landscape evolves.

How Actian Helps Organizations Manage Their Data

Actian Data Intelligence Platform helps organizations manage, organize, and access their data more efficiently. Powered by knowledge graphs and offering a user-friendly interface, it addresses many of the challenges organizations face with data discovery, collaboration, governance, and quality. Here’s how Actian helps businesses centralize and streamline their data management processes.

1. Centralized Data Discovery

Their data catalog automatically discovers and catalogs an organization’s data assets, making it easier for users to find relevant datasets across a variety of systems and platforms. Whether data resides in a cloud environment, a data warehouse, or a legacy system, the Actian Data Intelligence Platform creates a single, unified view of all that information, ensuring that users can easily access the data they need without wasting time searching through various systems.

2. Metadata Management and Enrichment

Actian goes beyond basic metadata management by allowing users to enrich metadata with business context. It enables users to add descriptions, tags, and annotations to datasets, improving their understandability. With this rich metadata, users gain deeper insights into the data, making it easier to decide how to apply it to business needs. Their metadata management also ensures that the data catalog stays up to date with changes in the organization’s data assets. By allowing users to organize all data in a knowledge graph, Actian makes it easier to search and find the data needed for any specific task.

3. Data Lineage Tracking

One of Actian’s standout features is its robust data lineage tracking. By providing a visual representation of data movement and transformation across the data pipeline through an easy-to-use lineage graph, Actian helps users trace data from its origin to its destination. This visibility into the data lifecycle not only helps maintain data integrity but also supports compliance and governance efforts by making it clear how sensitive data is handled and who has access to it.

4. Data Governance and Compliance Features

Actian empowers organizations to implement strong data governance practices. With its customizable data access controls and permissions, organizations can ensure that only authorized users have access to specific datasets. Actian’s catalog also syncs with an organization’s data quality solutions and reporting features, helping businesses maintain high data quality and comply with regulatory requirements.

5. Collaborative Platform

Actian promotes collaboration across teams by enabling users to share data insights, feedback, and best practices within the catalog. Data stewards, analysts, and business users can collaborate by tagging datasets, writing descriptions, and creating data documentation. This fosters a culture of knowledge sharing and ensures that data is being used consistently and correctly across the organization.

6. Scalability and Flexibility

Actian’s platform is designed to scale with an organization’s growing data needs. It supports various deployment models, including cloud and on-premises, and integrates seamlessly with existing data systems and workflows. As a result, businesses can easily expand their data ecosystem without worrying about the limitations of their data catalog.

Take a Tour Today

When there’s an overabundance of data, an organization’s success relies on proper data stewardship. Managing and accessing data efficiently is crucial for organizations to stay competitive. An enterprise data catalog helps businesses centralize, organize, and access their data in a way that improves data quality, governance, collaboration, and decision-making. Actian offers an advanced, scalable solution that streamlines data management, ensuring that businesses can extract maximum value from their data assets.

By investing in an enterprise data catalog, organizations are not only gaining a powerful tool to manage their data more effectively but are also positioning themselves to leverage data-driven insights to achieve better outcomes, faster. Get a personalized look at how it can work for your business today.

About Actian Corporation

Data Governance

Data Mesh vs. Data Fabric: Which One Should I Choose?

Nick Johnson

April 8, 2025

Centralized or Decentralized Data Governance?

Should you centralize your data management for better control or decentralize it for more agility? The answer might not be as straightforward as it seems.

Data fabric and data mesh offer two distinct approaches to managing and sharing data within an enterprise. They are often discussed in opposition to one another: data fabric emphasizes centralized data access, governance, and security, while data mesh promotes a more democratized and decentralized model.

Both approaches aim to address common data challenges, including:

Ensuring that the right data reaches the right people at the right time to maximize productivity.
Guaranteeing data accuracy, consistency, and completeness.
Protecting sensitive data from unauthorized access.

Neither model is a perfect solution for most organizations, as each has its strengths and limitations. However, a large opportunity exists to combine elements of both approaches to create a governance model that best suits the needs of your business.

What is Data Fabric?

Data fabric is a unified semantic layer that integrates disparate data sources and applications. It enables reusable data pipelines, data lakehouse storage, and metadata management. This approach improves interoperability, streamlines data access, and centralizes security and compliance.

However, overreliance on a single architecture paradigm may lead to costly dependencies, increased complexity, and scalability challenges that prevent innovation. The “data platform” construct is a practical example of the data fabric concept, but when applied rigidly, it can create bottlenecks that can be difficult to overcome.

What is Data Mesh?

Data mesh is a decentralized data architecture and operating model that enables domain-specific teams to assume ownership of their data and treat it as a product. By fostering a federated governance model, data mesh adheres to enterprise-wide policy standards while empowering teams to make autonomous decisions. This model cultivates a culture of accountability, ensuring data quality at the source.

However, aligning diverse governance standards across multiple domains can lead to inconsistencies in data definitions and security risks if not managed cohesively. The proper execution of a data mesh strategy requires a strong governance framework to maintain interoperability across teams.

Lessons From the Era of “Big Data”

Historically, large enterprises leaned toward data fabric as cloud providers like Microsoft, Amazon, Google, Snowflake, and Databricks promoted centralized big data analytics. The cloud data warehouse became the designated “single source of truth,” intended to standardize and govern vast amounts of structured, semi-structured, and unstructured data.

However, as data volumes exploded, data teams struggled to maintain clarity and consistency. Many teams ended up without access to well-governed data, resorting to unmanaged spreadsheets or relying on IT departments, creating further delays and complexity. The promise of big data analytics transformed into a flood of messy, unmanageable data.

Is a Hybrid Approach the Future?

To address the challenges of big data, organizations are increasingly considering a hybrid approach combining data fabric and data mesh principles. According to Gartner’s 2024 Evolution of Data Management Survey, 22% of organizations have implemented data fabric, 26% have adopted data mesh, and 13% already utilize both.¹ The number of organizations that adopt a hybrid approach is expected to grow over the next few years.

A hybrid approach leverages the complementary strengths of both data fabric and data mesh. Gartner predicts: “By 2028, 80% of autonomous data products supporting ‘AI-Ready data’ use cases will emerge from a fabric and mesh complementary architecture.”² Here, data fabric acts as the foundational data management infrastructure, while data mesh provides the delivery framework for high-quality data products.

Why a Hybrid Approach Matters

A hybrid model ensures strong data stewardship by unifying data design and governance (data fabric) while maintaining agility and domain-specific context (data mesh). This approach transforms central data teams from gatekeepers to mediators who support domain-specific teams in maintaining data quality and consistency. Centralized governance establishes enterprise-wide standards, while federated autonomy ensures domain expertise shapes data usage effectively.

Cross-functional collaboration remains essential in a hybrid data architecture. Organizations must balance centralized governance principles with domain-specific insights to ensure data products remain discoverable, trusted, and easy to access.

Choosing the Right Approach

Your organization’s choice of data governance strategy should reflect factors such as company size, diversity and complexity of data sources, departmental structure, and regulatory requirements.

Many growing companies succeed by implementing centralized governance first, then expanding principles to domain-specific areas. However, some large, complex enterprises may need to collaborate initially with domain teams to establish governance standards effectively, given existing data volumes and complexity.

Ultimately, the hybrid approach is the desired end state, offering the flexibility and control necessary to harness your data and operate at peak performance.

¹ 2024 Gartner Evolution of Data Management Survey, Gartner, 2024
² How Data Leaders can Settle the Data Fabric and Mesh Debate, Gartner, 2025

About Nick Johnson

Nick Johnson is a Senior Product Marketing Manager at Actian, driving the go-to-market success for HCL Informix and Actian Zen. With a career dedicated to shaping compelling messages and strategies for databases, Nick brings a wealth of experience from his impactful work at leading technology companies, including Neo4j, Microsoft, and SAS.

Data Quality and Data Observability: Why You Need Both

Build Trust and Have Confidence in Data Quality

Meet Current and Evolving Data Demands

Comprehensive Data Observability as a Quality Monitor

Why Organizations Need Data Quality and Observability

Actian Data Observability Helps Ensure Data Reliability

About Actian Corporation

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Quality Data, Reliable AI: Introducing Actian Data Observability

The Real Cost of Reactive Data Quality

1. The Legacy Problem

2. The Hidden Budget Drain

3. The Architecture Bottleneck

Actian Brings a Fresh Approach to Data Reliability

1. Proactive, Not Reactive

2. Predictable Cloud Economics

3. Complete Coverage, No Sampling

4. Architectural Freedom

Real-World Impact

Use Case 1: Data Pipeline Efficiency With “Shift-Left”

Use Case 2: GenAI Lifecycle Monitoring

Use Case 3: Safe Self-Service Analytics

The Actian Advantage: Five Differentiators That Matter

Get Started

About Emma McGrattan

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The Governance Gap: Why 60% of AI Initiatives Fail

Why Governance Efforts Fail

Going Beyond Traditional Data Catalogs

Shift-Left Capabilities for Data Stewards

Federated Data Governance With Active Metadata

Adaptive Metamodeling for Evolving Business Needs

Support Effective Governance With the Right Tools

Go From Data Governance Aspirations to Outcomes

About Dee Radh

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Life in an Enterprise Data Team: Before and After Data Intelligence

Life Before a Data Catalog

The Dig

The Chase

The Trust Crisis

The Redundancy Trap

Enter the Data Catalog:

Knowledge Graph-Powered Discovery in Minutes, Not Days

Federated Catalogs With a Unified Business Glossary

Comprehensive Lineage and Context

Integrated Data Quality and Observability

Data Products and Marketplace

Regulatory Compliance and Governance by Design

Augmented Data Stewardship

Discovery in Minutes, Not Days

Tangible Benefits

From Rube Goldberg to Renaissance

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Implementing Data Governance: A Step-by-Step Guide

Step 1: Define the Objectives of Data Governance

Key Considerations

Step 2: Identify Data Stakeholders and Data Ownership

Step 3: Conduct a Data Inventory and Classification

Step 4: Define Data Policies and Standards

Step 5: Implement Data Security and Privacy Controls

Step 6: Enable Data Access and Collaboration

Step 7: Monitor and Enforce Data Governance Policies