Data Governance

6 Reasons to Include Business Users in Your Data Catalog Strategy

Charlie Wood

April 1, 2025

In today’s data-driven world, organizations are increasingly relying on data catalogs to manage, organize, and govern their data assets. While data catalogs are often seen as tools for data engineers, analysts, and data stewards, their true potential is unlocked when business users are actively engaged in leveraging them.

Business users—those who rely on data to make decisions, drive strategy, and deliver value—play a critical role in ensuring the success of a data catalog initiative. Here are six reasons why including business users in your data catalog strategy is not just beneficial, but essential:

1. Bridging the Gap Between IT and Business

One of the most common challenges in data management is resolving the disconnect between technical teams and business users. Data engineers and stewards often focus on the technical aspects of data—schemas, metadata, and governance—while business users are more concerned with how data can answer their questions or solve their problems.

By involving business users in the data catalog, organizations can:

Ensure that the catalog is designed with business context in mind, making it more intuitive and user-friendly.
Help technical teams understand the real-world use cases for data, ensuring that the catalog prioritizes the most valuable assets.
Foster collaboration between IT and business, creating a shared understanding of data and its role in decision-making.

2. Adding Business Context to Metadata

A data catalog is only as valuable as the metadata it contains. While technical metadata (e.g., column names, data types) is essential, it often lacks the context that business users need to understand and trust the data.

Business users can contribute business metadata, such as:

Definitions of key terms and metrics (e.g., “What does ‘customer churn’ mean in our organization?”).
Annotations or tags that describe how data is used in specific business processes.
Insights into the quality, reliability, and relevance of datasets based on their experience.

This additional layer of context makes the data catalog more accessible and meaningful to a broader audience, increasing its adoption and utility.

3. Driving Data Democratization

Data democratization—the process of making data accessible to everyone in an organization—is a key goal for many modern enterprises. However, democratization is only possible if business users feel empowered to find, understand, and use data independently. A well-designed data catalog can serve as the foundation for data democratization, but only if it meets the needs of business users.

By involving business users in the design and implementation of the catalog, organizations can:

Ensure that the catalog includes features like natural language search, business-friendly interfaces, and self-service tools.
Identify and address barriers to adoption, such as overly technical language or complex navigation.
Build a culture of data literacy, where business users feel confident using data to drive decisions.

A well-designed data catalog that’s adopted across all user categories drives data democratization. It enables all users to access the data assets they need, encouraging trust in data and promoting enterprise-wide data usage.

4. Improving Data Governance and Compliance

Data governance is often seen as a top-down process, driven by policies and rules set by IT and compliance teams. However, business users play a critical role in ensuring that governance policies are effective and practical.

By including business users in the data catalog, organizations can:

Encourage them to take ownership of data quality and stewardship within their domains.
Provide a platform for them to flag issues, suggest improvements, and contribute to governance efforts.
Ensure that governance policies are aligned with real-world business needs, rather than being purely theoretical.

This collaborative approach to governance not only improves compliance but also builds trust in the data catalog as a reliable source of truth.

5. Enhancing Decision-Making and Business Outcomes

At its core, the purpose of a data catalog is to enable better decision-making by making data more accessible, understandable, and actionable. Business users are the ones who ultimately turn data into insights and insights into action.

By involving them in the data catalog, organizations can:

Ensure that the catalog supports their decision-making processes, from finding the right data to understanding its limitations.
Identify gaps in the catalog, such as missing datasets or unclear definitions, that could hinder decision-making.
Foster a data-driven culture, where decisions are based on evidence rather than intuition.

When business users are actively engaged with the data catalog, they are more likely to trust and rely on it, leading to better business outcomes and a data-driven culture.

6. Encouraging Adoption and Long-Term Success

One of the biggest challenges with any data catalog initiative is driving adoption. A catalog that is designed solely for technical users may struggle to gain traction among business users, limiting its impact.

By involving business users from the start, organizations can:

Ensure that the catalog meets their needs and expectations, increasing the likelihood of adoption.
Create a sense of ownership and buy-in, as business users feel that their input has been valued.
Build a community of engaged users who advocate for the catalog and encourage others to use it.

This user-centric approach is key to ensuring the long-term success of the data catalog.

Final Thoughts

Including business users in your data catalog strategy is not just a nice-to-have—it’s a necessity. Their involvement ensures that the catalog is relevant, user-friendly, and aligned with the organization’s goals. By bridging the gap between IT and business, adding valuable context to metadata, and driving adoption, business users play a vital role in turning a data catalog into a powerful tool for decision-making and innovation.

To succeed, organizations must foster collaboration between technical and business teams, provide training and support, and continuously gather feedback to improve the catalog. By doing so, they can create a data catalog that truly empowers everyone in the organization to unlock the value of data.

Remember: A data catalog is not just a technical tool—it’s a business enabler. And business users are the key to unlocking its full potential.

Experience a Modern Data Catalog

The Actian Data Intelligence Platform empowers organizations like yours with a unified data catalog that simplifies data discovery, enhances collaboration, and ensures data is accessible and trusted across teams. By providing rich business context and actionable insights, the platform and catalog can help you accelerate decision-making and drive innovation with confidence. Experience them for yourself with an interactive tour or demo.

About Charlie Wood

Charlie Wood has more than 15 years of experience in data management, working in various capacities, including technology sales. He brings a wealth of knowledge in areas such as integration, data catalogs, and data governance. Currently, he supports the field sales teams at Actian, focusing on all aspects of data governance.

Data Governance

An Introduction to BCBS 239

Kasey Nolan

March 27, 2025

Summary

This blog introduces BCBS 239—the Basel Committee’s comprehensive framework of 14 principles for effective risk data aggregation and reporting in global systemically important banks—explaining its origins, purpose, and implementation scope.

Why BCBS 239 matters: Developed after the 2008 financial crisis, this framework strengthens governance, data architecture, and IT resilience to ensure banks can aggregate and report risk data accurately under stress.
Four core principle categories: Governance & infrastructure; risk data aggregation (accuracy, completeness, timeliness, adaptability); risk reporting (clarity, frequency, distribution); and supervisory oversight.
Mandatory for large banks: G‑SIBs must comply by January 1, 2016, with domestic systemically important banks adopting similar standards, though many still struggle with full implementation.

In response to the vulnerabilities exposed by the 2008 financial crisis, the Basel Committee on Banking Supervision developed BCBS 239, formally titled “Principles for Effective Risk Data Aggregation and Risk Reporting”. This regulatory framework is not merely a set of guidelines but a transformative approach to risk data aggregation and risk reporting, particularly for Global Systemically Important Banks (G-SIBs). BCBS 239 establishes rigorous risk data aggregation and reporting standards to enhance the banking sector’s ability to manage, identify, and mitigate financial risks effectively.

Implemented to ensure banks can respond with agility and accuracy in stressful financial periods, this framework is crucial for maintaining stability in the global financial system.

The 14 Principles of BCBS 239

BCBS 239 is divided into several areas, focusing on overarching governance, risk data aggregation capabilities, and risk reporting practices. BCBS 239 outlines 14 key principles, with 11 applicable to banks and 3 to regulatory supervisors across four core focus areas:

Overarching Governance and Infrastructure

This emphasizes the importance of having a robust governance framework, risk data architecture, and IT infrastructure as foundational elements that enable compliance with the other principles. It mainly affects bank boards and senior management, who are responsible for ensuring that these elements are effectively implemented and maintained.

Governance: Banks must have a strong governance framework that clearly assigns responsibilities and establishes control mechanisms for risk data aggregation and reporting. This places responsibility on bank senior management to review and approve of risk data aggregation and risk reporting frameworks.
Data Architecture and IT Infrastructure: Banks are required to maintain data architecture and IT infrastructure that robustly support risk data aggregation and reporting under normal and stress conditions. It impacts IT and data management departments within banks, which must design and maintain these systems.

Risk Data Aggregation Capabilities

These principles focus on a bank’s ability to define, gather, process, and provide risk data in a way that meets the bank’s risk reporting requirements and supports its risk management framework. Banks must develop systems and processes that allow for the accurate, complete, timely, and adaptable aggregation of risk data to ensure that they can respond effectively to both normal and stress conditions in the market.

Accuracy and Integrity: Banks must generate accurate and reliable risk data that minimizes the probability of errors. This principle primarily impacts risk management and data processing teams tasked with ensuring data integrity.
Completeness: Risk data must be comprehensive and cover all material risks and business areas within the bank. This principle involves risk managers and data analysts who must ensure no critical data is omitted from reports.
Timeliness: Risk data should be produced promptly to meet regular and stress condition reporting needs. It affects all levels of risk management, particularly during periods of rapid change when timely data is critical.
Adaptability: Banks should be able to adjust their risk data aggregation capabilities to meet a broad range of reporting requirements and stress conditions. This impacts strategic operational risk teams who need to respond to emerging risks and regulatory demands.

Risk Reporting Practices

These principles pertain to the processes of creating reports that accurately and comprehensively reflect the aggregated risk data, tailored to meet the specific needs of its recipients, which typically include senior management and the board. The reports must be clear, useful, and produced at a frequency that supports timely decision-making and effective risk management.

Accuracy of Risk Data Aggregation: Risk reports must precisely convey aggregated risk data, ensuring that reports are reconciled and validated. This impacts the risk reporting teams responsible for the accuracy and reliability of risk reports.
Comprehensiveness: Risk reports should encompass all material risk areas and reflect the complexity and scope of the bank’s operations. This impacts senior management and board members who rely on these reports for decision-making.
Clarity and Usefulness: Risk reports should be clear, concise, and useful to their intended recipients, facilitating informed decision-making. This principle mainly affects the design and distribution of reports to ensure they meet the needs of executives and board members.
Frequency: The production and distribution frequency of risk reports should be set based on the nature of the risks reported and the needs of the recipients. This impacts how management and the board monitor and respond to risks.
Distribution: Risk reports should be appropriately distributed while maintaining confidentiality. This impacts compliance and risk management teams who must ensure secure and effective communication of risk findings.

Supervisory Review, Tools, and Cooperation

These principles involve the role of regulatory bodies in monitoring and ensuring that banks comply with the set principles through regular reviews and the use of supervisory tools. It requires cooperation among supervisors across different jurisdictions, particularly for banks that operate internationally, to ensure consistent application and adherence to these risk management standards.

Review: Supervisors should periodically evaluate a bank’s compliance with the risk data aggregation and reporting principles. This affects regulatory bodies and internal audit functions tasked with oversight.
Remedial Actions and Supervisory Measures: Regulators should have tools to require banks to take timely corrective actions to address deficiencies in risk data practices. This impacts bank management who are responsible for aligning practices with regulatory expectations.
Home/Host Cooperation: Supervisors should cooperate across jurisdictions to supervise and review the principles effectively, especially in the context of global banking operations. This impacts international banks and their regulatory supervisors in various countries.

Understanding the 14 principles of BCBS 239 is just the beginning of mastering how banks can elevate their risk management frameworks to not only meet regulatory expectations, but also enhance operational efficiency and competitive advantage. Each principle is a stepping stone towards achieving robust data governance, accurate risk reporting, and ultimately, financial stability. This is vitally important, as governance serves as the foundation upon which all procedural and compliance standards are built.

By reinforcing these practices, BCBS 239 ensures that banks have resilient and responsive governance structures capable of addressing potential risks proactively, safeguarding against systemic vulnerabilities and enhancing the overall health of the financial system.

Stay tuned for future blogs in our series on BCBS 239 to learn more about how the Actian Data Intelligence Platform helps ensure that governance frameworks and IT infrastructures are not only compliant with BCBS 239, but optimized for efficiency and scalability. In the meantime, take a product tour to see how enterprise data teams use the platform to quickly discover data and AI assets, establish trust, and democratize data access.

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.

Actian Life

How Internships Pave the Way for Career Success

Savannah Bruggeman

March 26, 2025

In today’s highly competitive job market, standing out is crucial but difficult, with thousands of candidates often vying for a limited number of positions. One of the most effective ways for job seekers to differentiate themselves is by having an internship on their resume.

Not only does an internship provide valuable experience, but it also contributes significantly to professional development, helping candidates gain skills and insights that boost their employability. Internships also offer the chance to build a professional network, opening doors to new opportunities and connections. Plus, they allow college students to apply classroom knowledge in real-world settings, turning theory into tangible skills.

Choosing the right internship is crucial. A well-designed internship offers the chance to gain relevant experience, develop key skills, and build connections within your desired industry. On the other hand, an ill-fitting internship can waste valuable time and may not provide the opportunities needed for long-term career success.

Why Actian is Different

Actian’s internship program is uniquely designed to offer more than just a traditional summer role. Interns are entrusted with real-world projects that allow them to take ownership and make lasting contributions to the company. Rather than performing routine tasks, interns focus on a substantial Capstone Project that challenges them to apply their skills, drive innovation, and leave a meaningful imprint on the organization. This hands-on approach fosters professional growth, allowing interns to develop both their technical and leadership skills.

Actian’s internship experience is structured for comprehensive learning and engagement. Each intern is paired with a dedicated buddy and guided through a personalized learning plan that includes setting and achieving specific goals. The program also offers opportunities for networking and exposure to company leadership through regular events, such as executive panels and Q&A sessions.

Actian ensures that interns not only gain valuable work experience but also enjoy a collaborative, enjoyable, and enriching internship experience. Through fun activities like virtual tie-dye events, virtual smores, and meeting in person for onboarding, interns can bond with their cohorts, ultimately leaving the intern experience with a network of peers they can lean on.

Moving From Intern to Employee

I came into Actian as a conversion rate optimization intern on the web team. My goal was to come up with several tests, from the colors on our website (within our brand guidelines of course) to headline options, to see which resonated the most. Eventually, based on these tests, we made major changes to our top trafficked pages to improve the amount of time visitors spent on our site and improve conversion across the site.

From the very beginning, my manager took the time to understand my strengths and areas for growth, offering personalized advice and constructive feedback. Both my manager and buddy helped me navigate challenges, improve my technical skills, and gain confidence in my decision-making abilities. Over time, I was able to see my conversion rate optimization (CRO) tests go live on the website and see real-time change in our web metrics. I could see and measure the impact I was having on an international website, which is something I never thought I would be able to do, let alone during an internship.

Beyond just supporting my professional development, my manager also encouraged me to think critically, be proactive, and approach problems creatively. Their experience and insights have not only broadened my perspective but have also inspired me to pursue my goals with more clarity and determination. Overall, the mentorship I received was an essential part of my growth and a key factor in making my internship a memorable and enriching experience.

At the end of my internship, I was given an amazing opportunity—a full-time job! I was invited to continue working at Actian. I transitioned onto the product marketing team to drive improvements across content operations.

As I’m coming up on my two-year anniversary as an Actian employee, I can honestly say that I would not be where I am today without the opportunities and experiences Actian gave me. My internship experience went far beyond what I expected—and even what I thought I could accomplish.

It gave me the skills I needed to take my learnings from the classroom into the real world to create impact and drive desired outcomes. It allowed me to become the marketing operations specialist that I am today—and enabled me to grow, develop as a professional, and gain confidence. All of this started with an internship.

Interested in taking your experience out of the classroom? Apply to be an intern.

About Savannah Bruggeman

Savannah Bruggeman is a Marketing Operations Specialist at Actian, bringing a data-driven mindset to campaign optimization. A recent Loyola University Chicago graduate, Savannah has quickly integrated fresh ideas into Actian's marketing processes. She specializes in marketing tech, analytics, and streamlining lead generation workflows. Her blog contributions cover marketing automation, lead management, and performance tracking. Explore her articles for actionable insights on driving marketing ROI.

Data Governance

ISG Research Insights into Data Intelligence and Self-Service Access

Actian Corporation

March 20, 2025

Matt Aslett, Director of Research, Analytics and Data at ISG Research, recently wrote about the importance of data catalogs, data intelligence, and the need for self-service access in an Analyst Perspective on the Actian Data Intelligence Platform. He notes that the “acquisition of Zeenea provides the company with much of the functionality to provide the data intelligence catalog and data product capabilities required to facilitate data democratization.”

The Essential Role of Data Catalogs in Data Discovery

According to ISG Research, while multiple types of data catalogs address specific use cases and user roles, the data intelligence catalog is an emerging category. This type of catalog combines technical metadata, business metadata, and data governance capabilities with knowledge graph functionality to give users a holistic, business-level view of data production and usage.

Aslett wrote, “Actian’s 2024 acquisition of Zeenea was designed to add these capabilities as a complement to its established data platform, data integration, and data quality functionality.”

The Need for Data Intelligence

Aslett expects that through 2027, 75% of organizations will launch data intelligence strategies to gain a deeper understanding of when, how, and where data an organization’s data is being used.

“As enterprises seek to increase data-driven decision-making, many are investing in strategic data democratization initiatives to provide business users and data analysts with self-service access to data across the enterprise,” Aslett explained.

Self-Service Data Access: A Long-Awaited Reality

Having self-service data access has been a long-standing goal for many companies. Yet only 15% of participants in ISG’s Analytics and Data Benchmark Research say their organization is very comfortable allowing business users to work with data that’s not been integrated or prepared for them by IT.

Aslett summarized his perspective by writing “I recommend that any enterprises engaged in data intelligence initiatives and exploring the potential for data products include Actian within their evaluations.”

Want to learn more about the Actian Data Intelligence Platform? Get a copy of the Analyst Perspective for additional insights or connect with one of our experts.

About Actian Corporation

Actian empowers enterprises to confidently manage and govern data at scale. Actian data intelligence solutions help streamline complex data environments and accelerate the delivery of AI-ready data. Designed to be flexible, Actian solutions integrate seamlessly and perform reliably across on-premises, cloud, and hybrid environments. Learn more about Actian, the data division of HCLSoftware, at actian.com.

Actian Life

Actian’s Steffen Kläbe Awarded for Dissertation on Modern Data Analytics

Actian Corporation

March 20, 2025

Actian’s Steffen Kläbe Awarded for Dissertation on Modern Data Analytics

Cloud computing has been one of the most groundbreaking technologies of the last two decades, but where is it heading? As Actian Senior Researcher Steffen Kläbe explains in a dissertation, cloud computing has become a transformative technology, driven by advancements in distributed systems, virtualizations, and fast networks. It delivers key benefits such as elastic and cost-efficient use of resources, ease of use, a low barrier of entry to managed environments, and accessibility for heterogeneous hardware.

As a result, software design must be rethought to natively support the benefits of cloud environments. These are just some of the ideas Kläbe presents in his Ph.D. thesis “Modern Data Analytics in the Cloud Era.”

Kläbe was recently honored with the Dissertation Award for Information Systems by the German Informatics Society for his thesis. He received the award at the Business, Technology, and Web (BTW) 2025 Conference in Bamberg, Germany.

The prestigious award recognizes the top doctoral research in the field over the past two years, making it one of the most significant awards in this sector. Kläbe, who works in Actian’s llmenau office in Thuringia, Germany, was one of only two people to receive the award this year, underscoring the impact and importance of his research for modern enterprises.

Receiving Industry Recognition and Making a Business Impact

At the BTW 2025 conference, Kläbe presented a spotlight paper on Actian Vector 7.0 to approximately 200 industry and research leaders who were in the audience. He shared how Vector, a highly performant analytics database, not only excels in performance, but also offers features for integration with modern ecosystems and for improving ease-of-use for customers.

Kläbe’s co-worker Stefan Hagedorn, Principal Software Engineer at Actian, also presented at the event. Hagedorn’s topic, “Experiences of Implementing In-database TPCx-AI,” explained his team’s insights and learnings when using Vector to perform machine learning workloads of the standardized TPCx-AI benchmark.

The discussions that followed these presentations reinforced the practical implications of Kläbe’s and Hagedorn’s research to address real-world data challenges. These conversations highlighted the growing need for high-performance solutions that can handle modern analytics workloads.

Steffen-Kläbe_photo
Photo by Maximilian Schüle

A Spotlight on Cloud-Based Data Management Systems

Kläbe dissertation addresses the growing challenges of traditional databases to handle modern use cases. In today’s environment where real-time analytics and machine learning workloads are reshaping data management, Kläbe proposes solutions that focus on cloud computing and modern analytics as key areas that change the way systems are designed and used.

His research provides practical solutions to optimize scalability, elasticity, and performance in cloud computing. Key areas of focus include:

Elastic scaling for distributed database engines, ensuring seamless resource allocation based on workload demands.
Approximate database constraints to match fine-grained data ingestion from numerous sources and the need for real-time analytics on live data.
A novel data partitioning method that, with approximate constraints enabled, offers robust query performance.

These advancements align with the evolving needs of enterprises. By optimizing platforms that efficiently process vast amounts of real-time data while remaining cost-effective and scalable, organizations can gain actionable insights faster and improve decision-making.

Enhancing Modern Workloads With Machine Learning Integration

Going beyond infrastructure optimization, Kläbe’s research delves into modern database workloads, particularly with regards to machine learning and user-defined functions (UDFs). His research covers:

Efficient support for UDFs, especially for integrating solutions from Python.
Engine-level machine learning inference integration, streamlining the application of predictive models within database systems.

Kläbe’s thesis ultimately investigates analytical database management systems and their interaction points with the cloud environment. He identifies challenges that must be addressed to deliver and support the benefits of the cloud when compared to traditional, on-premises deployments.

Shaping the Future of Data Management

Kläbe’s research provides a roadmap for cloud computing and database evolution, making modern analytics more accessible and efficient. His award-winning work helps to advance both a business and an academic understanding of the future of data management. For example, elastic scaling, the Python UDF, and in-database machine learning inference features are now part of Actian products.

As the cloud era continues to redefine how data is managed, Kläbe’s work offers insights to pave the way for more efficient, intelligent, and scalable data solutions. For those interested in exploring the full dissertation, it’s available here, with a 10-page summary accessible here.

This is not the first time Kläbe has been recognized for his research. At a 2023 joint conference by EDBT/ICDT in Greece, he received an award for Best Paper for his research on Patched Multi-Key Partitioning for Robust Query Performance.

About Actian Corporation

Data Quality

Data Quality: The Foundation of Informed Decision-Making

Traci Curran

March 18, 2025

data quality informed decision making blog hero

In today’s data-driven business landscape, the quality of your data can make or break your organization’s success. As the volume and complexity of data continue to grow exponentially, ensuring data quality has become more critical than ever. Let’s explore why data quality matters and how Actian’s solutions can help you achieve and maintain high-quality data.

Understanding Data Quality Management

Data Quality Management (DQM) is the set of mature processes, tools, and in-depth understanding of data needed to make informed decisions and solve problems while minimizing risk and impact to your organization or customers1. It’s not just about having high-quality data; it’s about using that data effectively to serve your purposes with flexibility and agility.

Join our upcoming webinar and see how Actian is helping to solve data quality challenges. Register now.

The Attributes of Data Quality

To truly understand data quality, we need to consider several key attributes:

Completeness: Are there any missing fields or information?
Validity: Does the data match its intended use?
Uniqueness: Are you relying on the correct set of data without redundancy?
Consistency: Is the same information available across all concerned parties?
Timeliness: Does the data represent the most accurate and up-to-date information?
Accuracy: Are the data values as expected?
Integrity: Does the data meet your quality governance standards?1

The Impact of Poor Data Quality

The consequences of poor data quality can be severe. Before the COVID-19 pandemic, Gartner estimated that the annual financial impact of poor data quality was around $15 million. This figure has likely increased since then, highlighting the critical need for effective DQM strategies.

Poor data quality can lead to:

Lack of trust in data, causing employees to create their own versions.
Insufficient data underpinning bad decisions.
Increased costs of data management and storage.
Lack of uniformity in data use, complicating application usage.
Unacceptable levels of risk or potential reputational damage.
Communication and collaboration silos.
Inability to react to market changes or crises.
Failure to introduce digital practices that enable cross-departmental data usage.

Implementing a Data Quality Management Framework

Organizations need a robust DQM framework to address these challenges. This framework should guide your data quality governance policies and processes, providing guardrails and metrics to help staff, IT, and vendors keep your data safe, secure, and usable.

Key considerations when building your framework include:

Accountability: Who will lead your data strategy and governance?
Transparency: How will you share data rules and gather feedback?
Compliance: How will you ensure policies and standards are followed?
Protection: What measures will you take to secure, backup, and manage your data?

Actian’s Approach to Data Quality

Actian offers powerful solutions to help organizations improve and maintain their data quality. Actian provides a comprehensive suite of data profiling, cleansing, and monitoring tools.

Data Profiling Made Easy

With Actian, profiling your data is just a click away. The intuitive interface allows you to tailor rules to your unique requirements or leverage expert recommendations to ensure your data is correct and complete.

Intelligent Data Quality Recommendations

Actian’s advanced algorithms and intelligent pattern recognition examine your data, accurately uncovering potential quality issues. You can create tailored rules or rely on Actian’s expert recommendations to process your data and isolate elements that don’t meet specified criteria.

Continuous Data Quality Monitoring

Actian enables you to automate and scale data profiling across datasets of any size or complexity. Intuitive dashboards allow you to dive deeper into individual jobs, datasets, and rules, helping you understand problem areas and track quality over time.

The Benefits of Improved Data Quality

By implementing effective DQM practices and leveraging tools like those provided by Actian, organizations can reap significant benefits:

Informed Decision-Making: High-quality data enables better insights and more accurate predictions.
Increased Efficiency: Streamlined data processes reduce manual data cleaning and verification time.
Enhanced Customer Experience: Accurate customer data leads to more personalized and effective interactions.
Regulatory Compliance: Well-managed data helps organizations meet increasingly stringent data regulations.
Cost Savings: Avoiding the pitfalls of poor data quality can save millions in potential losses and inefficiencies.

In an era where data drives business success, the importance of data quality cannot be overstated. It’s not just about having data; it’s about having data you can trust to inform critical business decisions. By implementing robust Data Quality Management practices and leveraging powerful tools like those offered by Actian, organizations can turn their data into a strategic asset, driving better business outcomes and gaining a competitive edge in their respective industries.

Remember, data quality is an ongoing process, not a one-time effort. Continuously monitoring, improving, and maintaining your data quality will ensure your organization remains agile, informed, and ready to face the challenges of an ever-evolving business landscape.

About Traci Curran

Traci Curran is Director of Product Marketing at Actian, focusing on the Actian Data Platform. With 20+ years in tech marketing, Traci has led launches at startups and established enterprises like CloudBolt Software. She specializes in communicating how digital transformation and cloud technologies drive competitive advantage. Traci's articles on the Actian blog demonstrate how to leverage the Data Platform for agile innovation. Explore her posts to accelerate your data initiatives.

Data Governance

Data Governance Roles and Responsibilities

Actian Corporation

March 17, 2025

Summary

This blog outlines the key roles and responsibilities essential to a robust data governance framework, clearly assigning duties across executive, technical, and operational levels to ensure data integrity, security, and compliance.

Chief Data Officer, Committee & Lead oversee strategy, policies, and cross-team coordination—aligning governance with business objectives and regulatory standards.
Data Owners, Stewards & Custodians manage domains end-to-end: owners define access and quality; stewards monitor integrity and guide usage; custodians secure and maintain technical infrastructure.
Structured implementation best practices—including clear RACI assignment, cross-functional training, and continuous monitoring via KPIs—drive operational efficiency, compliance readiness, and data-driven decision-making.

Today, organizations must ensure that their data is properly managed, secured, and utilized to drive business success. Effective data governance is crucial for regulatory compliance, data quality, and informed decision-making.

A well-defined governance structure improves operational efficiency and protects data assets from potential risks. To implement a strong data governance strategy, organizations must establish clear roles and responsibilities. This article explores the key roles in data governance, effective data governance team structure, and best practices for implementation.

What Does Data Governance Cover?

Data governance encompasses the policies, processes, and roles required to manage data assets effectively. It ensures that data is accurate, accessible, secure, and compliant with regulatory requirements. A comprehensive data governance strategy typically addresses data quality, security, compliance, lifecycle management, access control, and standardization. Organizations must develop clear policies and frameworks that align with their business objectives and regulatory requirements to manage data efficiently. Understanding the role of data quality in data governance is a crucial aspect of this process.

An effective governance model also ensures data consistency across different systems, allowing organizations to make informed, data-driven decisions. By implementing governance protocols, businesses can reduce redundancies, streamline workflows, and maintain data integrity. Additionally, regulatory laws such as GDPR and HIPAA require organizations to establish robust data management protocols, making governance a critical compliance necessity.

Key Roles in Data Governance

When establishing your organization’s data governance framework, you will likely develop your own list of important roles. Below, we’ve listed a few common types of roles, a brief description of who that role represents, and a bulleted list of their responsibilities.

Chief Data Officer (CDO)

The Chief Data Officer is often the leader of a company’s data governance structure. As part of the senior executive suite, the CDO ultimately governs how the organization handles, stores, and uses its data.

Responsible for defining and executing the organization’s data strategy.
Oversee data governance policies and frameworks.
Ensure compliance with data regulations and security standards.
Promote data-driven decision-making across the organization.
Lead data governance initiatives and align them with business objectives.

Data Governance Committee

Alternatively, in some organizations, data governance is not handled by a CDO. Instead, a group of executives and team leaders come together to form a committee that establishes a data governance methodology.

Composed of cross-functional leaders from IT, compliance, and business units.
Set up and enforce data governance policies and priorities.
Approve data standards and resolve data-related disputes.
Ensure alignment between data governance initiatives and business strategy.
Provide oversight for data-related risks and compliance challenges.

Data Governance Lead

Hierarchically below the CDO or Data Governance Committee, the data governance lead is the professional in charge of spearheading the strategic implementation of an organization’s overall governance framework.

Manage the implementation of data governance policies and frameworks.
Coordinate efforts across various data stakeholders.
Monitor compliance with established data policies and best practices.
Provide training and awareness programs on data governance.
Act as a liaison between executive leadership and operational teams.

Data Custodian

Data custodians are the individuals who develop technical methods by which an organization’s data is stored and processed. They help ensure the safety of organizational data.

Handles the technical aspects of data governance, typically within IT departments.
Implements data security measures and access controls.
Ensure proper storage, backup, and archival of data.
Manage data infrastructure and technology solutions.
Support data lifecycle management processes.

Data Owner

Anyone within an organization who manages a specific set of data assets is considered a data owner. They oversee their data assets and typically make strategic decisions regarding the data asset

Hold accountability for specific datasets within an organization.
Define data usage policies and access permissions.
Ensure data quality and integrity within their domain.
Approve modifications to data structures and definitions.
Collaborate with Data Stewards and Custodians to enforce governance policies.

Data Steward

The data steward is sometimes confused with the data custodian. While the custodian is the person who handles the technical aspects of data security and storage, the data steward is the one who uses the tools to ensure that the organization complies with its overall data governance strategy.

Work within business units to ensure data quality and governance compliance.
Monitor data integrity and resolves inconsistencies.
Enforce data governance policies at an operational level.
Provide support for data-related inquiries and issues.
Collaborate with IT teams to implement data standards and controls.

Building an Effective Data Governance Team

Creating a successful data governance team requires careful planning and collaboration. Organizations should define roles and responsibilities clearly, ensuring that all team members understand their specific duties. Cross-departmental collaboration is essential, involving stakeholders from IT, compliance, and business units to ensure alignment. Investing in training and education programs helps equip team members with the necessary skills and knowledge. Establishing clear communication channels within the organization ensures that data governance policies and procedures are effectively implemented and understood by all relevant parties. Following data governance best practices is crucial in this effort.

Additionally, organizations must establish a governance framework that includes continuous monitoring, reporting, and auditing mechanisms. Setting key performance indicators (KPIs) can help organizations measure the success of their governance strategy and make necessary improvements over time.

Implementing a Data Governance Framework

A strong data governance framework provides a structured approach to managing data. Organizations should start by assessing their current data governance maturity and identifying gaps. Defining clear data governance goals aligned with business priorities ensures strategic direction. Developing detailed policies and procedures for data quality, security, and compliance establishes consistency. Assigning roles and responsibilities across teams ensures accountability and effective execution. Implementing data governance technology solutions helps streamline policy enforcement, while continuous monitoring and measurement ensure ongoing improvements in governance effectiveness. Some organizations may have data generated from IoT or edge devices, so it is essential to consider the importance of data governance for the Internet of Things (IoT).

A robust governance framework should incorporate automation tools to enhance efficiency. Artificial intelligence (AI) and machine learning (ML) technologies can help identify data anomalies, detect compliance violations, and improve overall data accuracy. Companies should also consider leveraging cloud-based governance solutions to manage large-scale data operations effectively.

Challenges in Data Governance

Despite its importance, data governance presents several challenges for organizations. Resistance to change can hinder adoption, as employees may be reluctant to modify existing processes. Data silos across different departments can create inconsistencies and inefficiencies. Navigating complex and evolving regulatory requirements requires constant attention and adaptation. Limited resources can make it difficult to allocate dedicated personnel and technology investments for governance initiatives.

Disputes over data ownership and access control can create conflicts between departments. Addressing these challenges requires strong leadership, a culture of data accountability, and the adoption of automation tools to streamline governance processes. A solid foundation in enterprise data governance can help organizations mitigate these challenges effectively.

To overcome these challenges, organizations must foster a data-driven culture where governance is integrated into daily operations. Conducting regular training programs, adopting scalable governance technologies, and establishing executive sponsorship ensures long-term success.

Partner With Actian for Your Data Governance Needs

Actian offers practical data management solutions designed to help organizations implement effective data governance strategies. With a comprehensive suite of tools for data integration, security, and analytics, Actian enables businesses to improve data quality, enhance regulatory compliance, and optimize governance processes.

By leveraging automation and AI-driven insights, Actian helps organizations streamline data governance while reducing data risks. Partnering with Actian ensures a strong foundation for data governance and allows organizations to maximize the value of their data assets. Learn how to build a future-proof strategy to optimize your data governance approach.

To further enhance your data governance framework, explore the Actian Data Intelligence Platform to enable effective discovery, governance, and utilization of enterprise data assets.

About Actian Corporation

Data Governance

Why Organizations Need a Data Governance Roadmap

Actian Corporation

March 13, 2025

Summary

This blog outlines the importance of a structured data governance roadmap, emphasizing its role in enhancing data quality, ensuring compliance, and aligning data management with business objectives.

Structured Planning: A data governance roadmap provides a comprehensive approach to managing data assets, ensuring policies, roles, and technologies are effectively implemented to maintain data integrity and security.
Alignment With Business Goals: The roadmap ensures that data governance efforts are aligned with organizational objectives, facilitating seamless data integration across various functions and supporting long-term growth.
Continuous Improvement: Regular evaluation and refinement of the governance strategies allow businesses to adapt to new challenges and regulatory changes, maintaining the effectiveness of their data governance practices.

A data governance roadmap is a structured plan outlining how an organization manages, protects, and utilizes its data assets. The roadmap provides a comprehensive approach to implementing policies, roles, and technologies that ensure data integrity, security, and compliance. Organizations that experience a lack of data governance often struggle with data inconsistencies, compliance risks, and operational inefficiencies, making a well-defined roadmap essential.

Effective data governance is critical in today’s digital economy where organizations generate massive amounts of data daily. A well-structured roadmap allows businesses to harness their data effectively, driving better decision-making and operational efficiency. Moreover, strong governance minimizes security vulnerabilities and ensures compliance with industry regulations, reducing legal risks. By implementing a data governance roadmap, businesses can build a data-driven culture that promotes transparency and accountability across departments.

Understanding the Importance of a Data Governance Roadmap

A data governance roadmap is critical for organizations striving to leverage their data effectively. It provides a strategic approach to data management, ensuring that businesses can optimize their operations, maintain compliance, and support long-term growth. Establishing a clear data governance implementation roadmap is essential to achieving these objectives.

A well-designed roadmap serves as a blueprint for organizations to systematically manage data, ensuring that all stakeholders understand their roles and responsibilities. It aligns data governance efforts with overall business strategies, facilitating seamless data integration across various functions. Additionally, a roadmap provides a clear structure for risk management, addressing potential challenges before they escalate into critical issues.

The Benefits of Implementing a Data Governance Roadmap

Implementing a data governance roadmap helps organizations improve data quality, maintain regulatory compliance, and enhance decision-making. Reliable and consistent data leads to better business insights and operational efficiency. Additionally, a structured approach to data management mitigates security risks and strengthens data protection protocols, especially in regulated industries where compliance is crucial.

Key benefits of a data governance roadmap include:

Enhanced data accuracy and consistency.
Stronger data security and compliance with industry regulations.
Improved operational efficiency and decision-making capabilities.
Reduced costs that are associated with poor data quality.
Increased trust in data-driven strategies across the organization.

How Data Governance Roadmaps Align With Business Objectives

A data governance roadmap supports business objectives by enabling effective digital transformation, fostering customer trust, and reducing operational costs. It ensures that data-driven strategies align with organizational goals, enhancing overall business performance. Without a roadmap, companies face challenges such as a lack of data standardization, which can hinder productivity and decision-making processes.

A roadmap facilitates cross-departmental collaboration, ensuring that all teams work with consistent, high-quality data. By establishing governance best practices, businesses can drive innovation, improve customer experiences, and gain a competitive edge in their industry. Organizations can use governance frameworks to scale their data strategies as they grow, making future expansions more seamless.

Key Components of a Data Governance Roadmap

Developing a strong data governance roadmap involves multiple components that work together to create a structured and efficient data management framework. These components ensure that data governance strategies align with business objectives and industry regulations. Organizations should develop a data governance implementation plan to outline these components in a structured way.

Establishing Clear Data Governance Goals

Setting clear objectives is the foundation of a successful data governance strategy. Goals should focus on improving data accuracy, regulatory compliance, and accessibility. A roadmap helps define priorities and align data initiatives with overarching business strategies.

Organizations should consider the following when setting governance goals:

Ensuring data is high-quality, secure, and well-managed.
Defining clear guidelines for data classification and ownership.
Aligning governance policies with business and regulatory requirements.
Establishing KPIs to track governance effectiveness.

Defining Roles and Responsibilities

A successful framework requires assigning clear roles within the organization. For example, data stewardship plays a crucial role in maintaining data quality and enforcing governance policies. Key stakeholders, including data stewards, data owners, and governance committees, must collaborate to ensure proper data management and accountability.

Clearly defining roles helps prevent confusion and ensures that governance initiatives are effectively implemented. It also fosters a culture of accountability, encouraging employees to take ownership of data management practices and policies.

Crafting Data Policies and Standards

Effective data governance policies determine how data is collected, stored, accessed, and shared. These policies should address data classification, privacy, security, and retention standards. Companies that do not establish clear guidelines risk data inconsistencies, security vulnerabilities, and compliance violations.

Elements of strong data governance policies include:

Clear definitions of data access and usage.
Compliance standards tailored to industry regulations.
Processes for continuous data validation and integrity checks.
Procedures for handling and responding to data security incidents.
Training programs to educate employees on governance best practices.

Using a Data Governance Roadmap Template

The following data governance roadmap template serves as a foundation for organizations to establish structured data management practices. By using a well-designed template, businesses can ensure consistency, accountability, and regulatory compliance across departments. A roadmap template provides a structured approach to defining governance objectives, assigning responsibilities, and setting policies that support long-term data strategies.

Actionable Advice for Streamlining a Data Governance Strategy

Implementing an effective data governance strategy requires more than just a structured roadmap—it demands continuous refinement and alignment with organizational goals. Companies should focus on actionable steps that improve governance efficiency, foster a culture of accountability, and ensure adaptability to evolving data challenges. By streamlining governance processes, organizations can maximize the value of their data while maintaining compliance and security standards. Steps include:

Setting Realistic and Achievable Goals

Organizations should establish measurable, attainable objectives to guide governance strategies effectively. These goals should align with business priorities and regulatory requirements.

Training and Educating Teams

Educating employees on governance policies, security protocols, and compliance measures ensures that all stakeholders adhere to governance standards.

Ensuring Continuous Improvement

Regularly evaluating governance practices allows businesses to adapt to new challenges and regulatory changes, maintaining the effectiveness of their data governance strategies.

FAQs

What are the first steps in creating a data governance roadmap?

The first steps include assessing current data management practices, defining governance goals, and establishing clear roles and responsibilities.

How do data governance roadmaps help achieve compliance?

A roadmap enforces structured data policies that align with industry regulations, reducing the risk of non-compliance and security breaches.

What tools can assist in implementing a data governance roadmap?

Common tools include data cataloging platforms, data quality management software, and compliance tracking systems to automate governance processes.

Final Thoughts on Implementing an Effective Strategy

A well-defined data governance roadmap is essential for organizations seeking to improve data quality, security, and compliance. By setting clear objectives, assigning roles, and continuously refining governance strategies, businesses can unlock the full potential of their data assets. For a deeper understanding of governance best practices, explore Why is Data Governance Important?

Explore more solutions to drive data agility with Actian.

About Actian Corporation

Product Launches

Winter ’25 Launch: Major Governance Update to Actian Data Marketplace

Dee Radh

March 3, 2025

Actian’s Winter 2025 launch brings a critical upgrade to the Enterprise Data Marketplace.

Self-service data access request
Data teams waste valuable time navigating complex data access processes, with 82% spent on finding and preparing rather than analyzing data. Our new self-service capability streamlines access requests through automated workflows integrated with ServiceNow and Jira, enabling quick, controlled data access while maintaining governance standards.

Why it Matters

The modern enterprise’s relationship with data has reached an inflection point. While organizations have invested heavily in data infrastructure, many struggle to extract value from their data assets. The challenge isn’t just about having data—it’s about making it discoverable, accessible, and trustworthy across the organization, especially for data consumers.

Last fall, we announced a host of new features to help you better understand your data, including the Enterprise Data Marketplace that reimagines how organizations discover, access, and leverage their data assets through a consumer-grade experience that feels more like your favorite e-commerce platform than a traditional enterprise tool. Let’s take a closer look.

Understanding the Enterprise Data Marketplace

The Enterprise Data Marketplace is more than just a catalog of available data assets. The Data Marketplace enables intuitive discovery, access, and sharing of data products with federated data governance–a combination of decentralized data ownership with centralized governance.

Decentralized ownership empowers individual business units and domains to manage their data assets directly, as they best understand their specific data needs and contexts. This local control enables faster decision-making, promotes innovation, and allows teams to be more responsive to their unique requirements. For example, a marketing team can manage their campaign data differently than how a finance team handles financial records, each optimizing for their specific use cases.

However, this decentralization needs to be balanced with centralized governance to maintain consistency and control across the enterprise. Centralized governance ensures common standards for data quality, security, and compliance are maintained throughout the organization. It provides the framework within which decentralized teams can operate, much like how a federal government sets broad policies while states maintain local autonomy.

The Rise of Data-as-a-Product

The data-as-a-product approach represents a fundamental shift in how organizations manage and deliver data. Rather than treating data as raw material that requires significant processing before use, data products package data assets with clear documentation, quality metrics, and usage guidelines. This product-centric approach establishes clear contracts between data stewards/producers and consumers, tearing down silos between domains while ensuring strong governance standards.

Why Enterprises Need Data Products and Marketplace

First, it removes technical barriers. Instead of navigating complex data environments or writing queries, data consumers can browse a catalog of ready-to-use data products, much like shopping on an e-commerce platform. The data contract serves as the product description, clearly stating what’s included and what to expect.

Second, it establishes accountability. Large enterprises often struggle with data quality and trust issues. Data contracts create clear lines of responsibility – data producers commit to maintaining certain quality standards, while consumers understand their usage limitations. This accountability is crucial for non-technical users who need to make business decisions based on the data.

Third, it enables self-service. The combination of well-packaged data products and clear contracts means business users can independently find and use data without constantly relying on IT or data teams. This autonomy is particularly valuable in large enterprises where technical resources are often stretched thin.

Finally, it promotes data literacy. By making data more accessible and understandable, this approach helps non-technical users become more confident in working with data. The clear documentation and quality metrics included in data products help users understand what they’re working with, while contracts provide guidelines for proper usage.

The Impact on Large Enterprises

“Managing data access in a large-scale media organization has traditionally been a complex, time-consuming process. With the Actian Data Intelligence Platform’s Self-Service Data Access Request, we expect to reduce related bottlenecks while maintaining strong governance and security protocols”.
– Mikko Eskola, Chief Data Officer, Sanoma Media Finland

For large enterprises, this approach addresses several critical challenges. It reduces the bottleneck of data requests to technical teams, accelerates decision-making by making data more readily available, and ensures consistent data quality and governance across the organization. Perhaps most importantly, it empowers business users to become more data-driven in their decision-making without requiring them to become technical experts.

As organizations continue to democratize data access, the combination of data products and data contracts provides a structured, scalable way to make data accessible while maintaining quality and governance. It’s a practical solution that bridges the gap between technical capabilities and business needs, making it particularly valuable for large enterprises looking to become more data-driven across all levels of the organization.

Winter 2025 Delivers Even More Value to the Enterprise Data Marketplace

Today, we’re excited to announce the launch of Actian’s Winter 2025 launch that will reshape how enterprises find and access ready-to-use data assets.

Self-Service Data Access

access requests screenshot

Remember the last time you had to request access to a dataset? The emails, the tickets, approval processes, and follow-ups. Data teams spend 82% of their time finding and preparing data than analyzing it. Our new self-service data access capability simplifies how data consumers request and receive access to data assets. Through intuitive workflows integrated with existing tools like ServiceNow and Jira, we’re automating the entire process while maintaining strict audit trails for governance and compliance. This feature particularly benefits data consumers who need quick access to trusted data while ensuring data producers and stewards maintain control and visibility by collecting essential information such as intended usage, business use case, and duration.

Try Self-Service Data Access: Administrators must enable and configure the feature in the “Access Requests” section of the Administration interface. Once enabled, data consumers can initiate a request directly in Actian Explorer by clicking the “Request Access” button. Data producers can review requests in Actian Studio, where they can approve, decline, or request more information. In the Federated Catalog, data access requests are managed at both the tenant and catalog levels.

More Than Just a Data Catalog

At Actian, we’re bringing the dynamic, self-service experience we’ve come to expect in our consumer lives into the enterprise data world. While traditional data catalogs focus on documenting what exists, the Actian Data Intelligence Platform’s Enterprise Data Marketplace focusses on what’s possible. We’re not just extracting metadata—we’re creating a dynamic ecosystem where data products can be discovered, understood, and accessed with confidence.

The Future of Data Intelligence

Winter 2025 marks another milestone in our vision to transform how enterprises manage and leverage their data assets. By combining simplified access with robust governance, we’re helping organizations balance the need for data democratization with the requirements for control and compliance. We’re excited to partner with organizations on this journey toward data democratization and look forward to sharing more innovations at our upcoming Spring 2025 launch this May.

Take Action

Contact us to schedule a demo and see these new features in action
Download the Enterprise Data Marketplace solution guide
Try the product tour

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 Governance

Data Intelligence to Discover, Govern, and Share Data With Ease

Dee Radh

February 28, 2025

For modern enterprises, data is more than a resource—it’s the DNA of informed decision-making and smart innovation. Yet as organizations manage ever-growing data ecosystems, they face a common challenge: How to make trusted, governed data easy to find, access, and share across the enterprise.

The Actian Data Intelligence Platform solves this issue by providing a single solution that connects, locates, and democratizes enterprise data. Built on the knowledge graph architecture for easy discovery and navigation, the platform transforms how organizations understand and utilize their data assets.

Easy-to-Use Capabilities for Intuitive Data Discovery

The Actian Data Intelligence Platform, powered by a knowledge graph, centralizes and unifies enterprise metadata, serving as a single source of truth for data discovery and governance. By consolidating metadata from all data assets and sources, the platform enables organizations to transform their data landscape into a cohesive, accessible, and trusted ecosystem.

Key features and capabilities of the platform include:

Making data discoverable. The platform makes relevant data easy to find. Smart searching capabilities—similar to e-commerce website search engines—simplify the process of finding data assets. In addition, a knowledge graph combines flexibility and intuitive user experiences to enhance data discovery and governance, and to deliver greater value to the organization. Teams can find the data they need for their use cases, even if they don’t know the assets they are looking for.
Elevating data stewardship. The platform’s knowledge graph technology allows users to optimize data discovery with intelligent recommendations and rich search results. These capabilities enhance the context and usability of data, providing structure for data and showing relationships between data assets, which boosts teams’ data knowledge and improves data stewardship.
Building a business glossary. Data users need a consistent business vocabulary that aligns cross-functional teams. Data literacy is key to departmental collaboration and data sharing. The Actian Data Intelligence Platform offers an automated business glossary, enabling organizations to define rules, policies, and criteria that ensure clarity and consistency for data assets.
Seeing changes with data lineage. The platform’s data lineage capabilities capture data transformations, providing a comprehensive view of data’s lifecycle. This includes tracing data’s usage and storage over time while mapping relationships between systems, applications, and reports. This context-rich perspective helps users understand their data’s journey, enhancing transparency and trust.
Ensuring data quality and governance. Data teams must be able to view data quality metrics and address issues proactively. Detecting and resolving data quality concerns lead to confidence in the data and support data usage. The platform automatically synchronizes with an organization’s data quality solutions, allowing teams to view the quality of their data. Likewise, the platform supports data governance for trusted, compliant, and secure information across the enterprise. Organizations benefit from an active and agile data governance approach.
Enabling data compliance. Staying compliant with evolving data regulations is critical, especially in highly regulated industries. The platform helps here, too. It automates compliance by identifying, classifying, and managing personal data assets at scale. Using smart recommendations, it detects sensitive information and suggests proper tagging—ensuring data policies are consistently applied and regulatory requirements are met across the organization.
Utilizing a data marketplace. An enterprise data marketplace serves as a centralized hub where teams can easily access, understand, and utilize ready-to-use data products. By integrating data silos, it transforms data into a shared resource—empowering teams to use a search engine approach to publish, discover, and share governed, high-value data.

Key Benefits of a Data Intelligence Platform

As data environments grow increasingly complex and distributed, organizations need complete visibility and control over their data assets. They also need governance, compliance, and self-service capabilities—and these capabilities must be scalable and available on a single platform.

The Actian Data Intelligence Platform meets all of these requirements. Organizations can use the solution to:

Accelerate data discovery. Quickly find and access relevant data to drive timely insights.
Meet regulatory compliance. Manage risk, ensure privacy, and comply with data regulations effectively.
Sustain a data-driven culture. Promote a culture of data literacy and collaboration.
Optimize data value. Leverage metadata and data assets to extract actionable insights.
Trust enterprise data. Ensure data reliability through robust governance and quality measures.
Discover data at any scale. Utilize a federated catalog that’s structured as a single knowledge graph to organize data and optimize search experiences.
Gain instant value. Realize an immediate return on investment (ROI) by enabling all data teams to find and access the data assets they need.

Support for a Data Mesh Framework

Organizations can support data democratization while also aligning with a data mesh framework that decentralizes data ownership. The Actian Data Intelligence Platform embodies the principles of a data mesh, empowering organizations to manage and govern data products at the domain level.

This approach enables organizations to:

Ensure autonomy for data teams, allowing them to document, curate, and share their own federated catalogs.
Facilitate seamless sharing of high-value data products with teams, departments, and individuals.
Support scalability and adaptability for the data ecosystem by integrating data production and data consumption in one platform.

This decentralized model ensures that organizations remain agile, scalable, and future-ready in their ever-evolving data landscape.

Bringing Data Discovery to All Data Users

The Actian Data Intelligence Platform is more than a tool. It’s a catalyst for transforming how organizations gain maximum value from their data and reach data’s true potential.

Organizations that are ready to uplevel their data intelligence strategy can take a self-guided tour of the platform to discover how easy it can be to find, trust, and democratize data.

About Dee Radh

Data Governance

Knowledge Graphs: The Key to Modern Data Governance

Kunal Shah

February 27, 2025

Summary

This blog shows how knowledge graphs evolve traditional data catalogs into dynamic, context-rich platforms, offering interconnected, semantic views of data to streamline discovery, lineage, compliance, and governance.

Knowledge graphs convert static inventories into living networks of entities and relationships, revealing how data assets connect across business processes, users, and systems.
By capturing semantic context and automating graph updates, they enable smarter search, faster lineage tracing, and holistic governance across AI, finance, healthcare, manufacturing, logistics, and utilities.
Organizations benefit from improved compliance, streamlined discovery, enhanced data quality, and reduced manual effort, making data governance more agile, AI-ready, and cost-effective.

For over a decade, I’ve watched enterprise data management evolve. We’ve seen the rise of data warehouses, data lakes, and countless tools promising to tame the ever-growing beast of organizational data. Data catalogs emerged as a key component, offering a centralized view of data assets. With the growing popularity of AI, and the use of enterprise data to build organizational-specific LLM’s, traditional catalogs are starting to show their age. They tell you what data you have, but often fall short on explaining how it relates, who uses it, and why it matters. This is where Knowledge Graphs step in, offering a transformative leap in data governance.

Forget static lists of tables and columns. Knowledge Graphs represent data as a network of interconnected entities and relationships. Think of it as a dynamic map of your data ecosystem, where every data point is a node, and the connections between them are the crucial links that reveal context and meaning. This interconnectedness is the key differentiator, turning a simple inventory into a powerful engine for data governance.

What is a Knowledge Graph in a Data Catalog Context?

A Knowledge Graph within a data catalog isn’t just a visual representation of data. It’s a structured representation of knowledge about your data. It goes beyond simple metadata by explicitly defining the relationships between different data assets, business terms, processes, and even people. Think of it as adding layers of semantic understanding to your data catalog. Instead of just knowing you have a “customer” table, the Knowledge Graph shows you how that table relates to other data like “orders,” “products,” “customer demographics,” and even the business processes that use this information. This rich network of connections allows for more intelligent querying, discovery, and analysis.

Traditional Data Catalogs: The Limitations

Traditional data catalogs primarily focus on metadata – descriptions of data assets. They help you discover data, understand its structure, and track its lineage. While valuable, they often struggle with:

Lack of Context: They may tell you the name of a dataset, but not how it relates to other data, business processes, or organizational goals.
Limited Semantic Understanding: They treat data elements as isolated entities, missing the rich semantic relationships that drive business insights.
Manual Updates: They often require manual updates and struggle to keep pace with the dynamic nature of enterprise data.
Siloed Information: They may not integrate well with other governance tools, leading to fragmented views of data.

Knowledge Graphs: The Solution

Knowledge Graphs address the traditional data catalog limitations by:

Connecting the Dots: They explicitly represent relationships between data assets, revealing how data flows through the organization, which systems it impacts, and who is responsible for it.
Enriching Semantics: They capture the meaning of data, enabling a deeper understanding of its context and relevance to business objectives. This allows for more intelligent data discovery and analysis.
Dynamic Updates: They can automatically discover and incorporate new data and relationships, ensuring the catalog remains current and accurate.
Unified Governance: They can integrate with other governance tools, providing a holistic view of data and its impact on compliance, security, and quality.

Enhance Data Discovery, Lineage, and a 360-Degree View Across Industries:

Knowledge Graphs significantly enhance core data governance functions across various industries:

Data Discovery: Imagine searching for “customer profitability.” A traditional catalog might return hundreds of tables. A Knowledge Graph, understanding the relationships between data, can pinpoint the specific data elements and calculations relevant to profitability, dramatically accelerating discovery.
Data Lineage: Tracing the origin and transformation of data becomes much easier. In banking, this is crucial for regulatory reporting. A Knowledge Graph can show the complete path of a financial transaction, from its source to its final destination, ensuring accuracy and compliance. In pharma, this could map the journey of a drug from research to manufacturing to patient data.
360-Degree View: Knowledge Graphs provide a holistic view of data assets, enabling better understanding and utilization. For example:
- Banking/Insurance: A 360-degree view of a customer, including their financial holdings, insurance policies, interactions, and risk profile, allows for personalized services and better risk management.
- Pharma/Healthcare: Integrating patient data with research data, clinical trial data, and drug information provides valuable insights for drug development and personalized medicine.
- Manufacturing: Connecting data from the supply chain, production floor, and customer feedback provides a comprehensive view of the product lifecycle, enabling process optimization and improved quality.
- Logistics: Tracking shipments, inventory, and transportation routes in a Knowledge Graph allows for real-time visibility and optimized logistics operations.
- Utilities: Integrating data from smart grids, customer usage, and infrastructure maintenance provides a comprehensive view of the energy network, enabling better grid management and customer service.

Knowledge Graph – Benefits for Data Governance

The impact of Knowledge Graphs on data governance is profound:

Improved Data Discovery: Users can easily find the data they need, along with the context and understanding necessary to use it effectively.
Enhanced Data Quality: By understanding data relationships, organizations can identify inconsistencies, redundancies, and other data quality issues more easily.
Streamlined Compliance: Knowledge Graphs can help organizations track data lineage and usage, simplifying compliance with regulations like GDPR, HIPPA and CCPA.
Increased Business Agility: By providing a clear and comprehensive view of data, Knowledge Graphs empower business users to make data-driven decisions faster and more effectively.
Reduced Costs: By automating data discovery and governance processes, organizations can reduce the costs associated with manual data management.

Beyond the Hype

While the term “Knowledge Graph” might sound like the latest buzzword, the underlying technology has proven its value in various domains. Its application to data governance is a natural evolution, addressing the growing need for more intelligent and dynamic data management.

The Future of Data Governance

In my decade plus experience in this field, I’ve seen many trends come and go. But Knowledge Graphs powered data intelligence feels different. They represent a fundamental shift in how we think about data governance, moving beyond simple catalogs to create a truly connected and intelligent data ecosystem. For organizations looking to truly create AI-ready data, embracing Knowledge Graphs is no longer a luxury, but a necessity. The future of data governance is interconnected, intelligent, and driven by Knowledge Graphs.

Are you ready to connect the dots? Take a tour to simplify discovery, governance, and compliance – all in a unified platform – powered by a knowledge graph

About Kunal Shah

Kunal Shah is a product marketer with 15+ years in data and digital growth, leading marketing for Actian Zen Edge and NoSQL products. He has consulted on data modernization for global enterprises, drawing on past roles at SAS. Kunal holds an MBA from Duke University. Kunal regularly shares market insights at data and tech conferences, focusing on embedded database innovations. On the Actian blog, Kunal covers product growth strategy, go-to-market motions, and real-world commercial execution. Explore his latest posts to discover how edge data solutions can transform your business.

Data Platform

Using Data Intelligence: How Organizations are Achieving Success

Actian Corporation

February 24, 2025

Successful businesses don’t just rely on data—they depend on trusted, integrated, and immediately accessible data to fuel confident decision-making, optimize operations, and accelerate results. From streamlining processes and enhancing customer experiences to achieving strategic goals faster, organizations across industries are realizing the transformative power of a data intelligence platform.

Actian Data Intelligence Platform is designed to solve data challenges and improve business outcomes with self-service data access and simplified data discovery. These capabilities enable users to quickly find and access the data assets they need.

By connecting data silos, improving visibility into data, and enabling users to locate, trust, and leverage their data, a data intelligence platform drives new levels of success across diverse industries. Actian customers are doing just that—achieving outcomes with confidence.

The Need for Data Intelligence: More Than Just Storage

Modern enterprises generate massive amounts of data every day. While they probably have a solution to cost-effectively store it, storing data isn’t the same as making it useful. Many organizations continue to struggle with:

Siloed and fragmented data across multiple sources and departments.
A lack of trust and visibility into their data assets.
Regulatory compliance pressures require stringent data governance.
Inconsistent data definitions leading to misalignment across teams.
Limited self-service data access that forces a reliance on IT teams.

Without a unified platform to organize, govern, and unlock the value of data, organizations risk inefficiencies, lost opportunities, and compliance violations. Actian solves this by delivering a centralized data intelligence platform that empowers business users, IT teams, and data professionals alike to access, discover, and use data assets without requiring advanced technical skills. Modern businesses are putting data intelligence into action and reaping the rewards.

Driving Data-Driven Innovation in Insurance

For global insurance leader Generali, managing sensitive customer data while ensuring regulatory compliance across multiple countries is a critical priority. However, disparate systems and a lack of clarity in data ownership hindered its ability to fully leverage data assets.

The Solution. By deploying the Actian Data Intelligence Platform Data Catalog, Generali created a one-stop shop for its data needs to improve compliance, accountability, and collaboration.
The Impact. Generali’s teams now have streamlined regulatory processes and increased stakeholder engagement in data-driven initiatives.
The Result. As an agile, data-driven insurance provider, Generali better understands its data assets to streamline compliance, manage large datasets at scale, and drive sustainable business value.

Unifying Freight Data for a Global Cargo Company

With a network across more than 100 countries and over 300 destinations, Lufthansa Cargo needed a way to connect fragmented systems and integrate data from multiple sources in real-time, on a single platform.

The Solution. Actian Data Intelligence Platform Data Catalog created a single source of truth. The user-friendly solution enables business and IT teams to collaborate seamlessly.
The Impact. Lufthansa Cargo centralized its analytics and BI data, enabling decisions that improve logistics, reduce inefficiencies, and enhance operational agility.
The Result. A fully data-driven air cargo business that is well-positioned to meet its 2025 digital transformation goals.

Powering Smart Travel Using Integrated Data

With millions of passengers relying on real-time travel information, SNCF Connect & Tech needed a way to organize, access, and trust its vast data assets. Its data was scattered across Excel files, Wiki pages, and other systems, making it difficult for teams to leverage information effectively.

The Solution: Actian Data Intelligence Platform consolidated data assets, creating a centralized, easy-to-navigate solution for data discovery.
The Impact: Employees can quickly find and use trusted data to inform planning, identify strategic KPIs, and improve customer experiences.
The Result: A seamless digital travel experience for millions of users, backed by reliable, real-time data.

Turning Data into Housing Solutions

Managing more than 74,000 properties and 130,000 tenants, German housing leader Gewobag needed a better way to handle its growing data complexity. From sustainability initiatives to predictive maintenance, leveraging data is key to long-term success.

The Solution. Actian Data Intelligence Platform enabled Gewobag to map relationships between datasets, improve transparency, and introduce self-service tools.
The Impact. Teams now have a better understanding of data quality, accessibility, and usability, allowing for better energy efficiency, forecasting, and decision-making.
The Result. Data-driven housing management that supports sustainable urban development.

Taking a Modern Approach to Safer Roads

As one of Europe’s largest toll motorway operators, Autostrade per l’Italia set a goal for a digital transformation that entailed leveraging data, modernizing its technology ecosystem, and moving to the cloud. It needed to address inefficient workflows that created operational risks, which could have been solved by standardizing processes.

The Solution. Actian Data Intelligence Platform and data catalog utilized metadata and data governance tools to solve data challenges and support modernization.
The Impact. The company democratizes data, views data as an asset, and replaces manual processes with automation—enabling a level of automation that’s about 90%.
The Result. An intelligent, data-driven approach to ensure safer roads, with more business users now leveraging the data catalog.

How Our Data Intelligence Platform Delivers Transformative Results

The future of business belongs to those who can locate, access, and share their data effectively. Whether organizations are in financial services, transportation, retail, manufacturing, or another industry, the Actian Data Intelligence Platform is the key to making smarter, faster, and more confident decisions.

The platform enables:

A centralized data hub that breaks down silos and unifies data sources.
Enhanced data discoverability to enable teams to quickly find and trust data.
Operational efficiency by reducing manual work and improving data governance.
Strategic decision-making with self-service data access for business users.
Scalability and compliance to support regulatory needs and business growth.

By modernizing how companies manage, access, and use their data, the data intelligence platform drives efficiency, innovation, and competitive advantage. See how it transforms data strategies. Take the interactive tour.

6 Reasons to Include Business Users in Your Data Catalog Strategy

1. Bridging the Gap Between IT and Business

2. Adding Business Context to Metadata

3. Driving Data Democratization

4. Improving Data Governance and Compliance

5. Enhancing Decision-Making and Business Outcomes

6. Encouraging Adoption and Long-Term Success

Final Thoughts

Experience a Modern Data Catalog

About Charlie Wood

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An Introduction to BCBS 239

The 14 Principles of BCBS 239

Overarching Governance and Infrastructure

Risk Data Aggregation Capabilities

Risk Reporting Practices

Supervisory Review, Tools, and Cooperation

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Self-Service Data Access: A Long-Awaited Reality

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Actian’s Steffen Kläbe Awarded for Dissertation on Modern Data Analytics

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Shaping the Future of Data Management

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Data Quality: The Foundation of Informed Decision-Making

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Continuous Data Quality Monitoring

The Benefits of Improved Data Quality

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Data Governance Roles and Responsibilities

What Does Data Governance Cover?

Key Roles in Data Governance

Chief Data Officer (CDO)

Data Governance Committee

Data Governance Lead

Data Custodian