Data Governance

The Power of Data Catalogs: 5 Use Cases for Modern Enterprises

Dee Radh

April 7, 2025

the power of data catalogs blog image with blue squares

Successful businesses have embraced a fundamental truth—data is one of their most valuable assets. Yet how that data should be managed, shared, and optimized remains an ongoing challenge.

That’s because in many organizations, fast-growing datasets are scattered across multiple systems and business units, stored in different formats, and governed by various policies. As a result, companies struggle with effective data discovery, governance, and transparency, leading to inefficiencies, compliance risks, and lost business opportunities.

One proven approach to solve this problem is to implement a data catalog. By providing a centralized repository of metadata, a data catalog enables organizations to quickly and effectively manage, search, and understand their data products. Serving as more than an inventory of data assets, a data catalog supports data governance, enables data democratization, and fosters collaboration across teams.

How is a Data Catalog Like a Library?

To understand how a data catalog works, think of it like a library for books. Both serve as organized repositories that make information easy to find, understand, and use. For example, a library helps readers quickly locate books they want based on topic, author, or genre. Similarly, a data catalog helps users discover datasets based on metadata, business terms, or usage history.

In libraries, readers can use a catalog system to search for books by title, keyword, or other criteria. A data catalog lets users search for data products also by using keywords, descriptions, or other information.

Books typically offer a summary, author information, and publication details. Likewise, datasets have metadata such as source, owner, format, and data lineage. Another similarity is helping to find the right product. In libraries, librarians help visitors find the books or publications they need. With a data catalog, data stewards or data owners ensure data products are properly documented and accessible.

Both librarians and data stewards can help people find the right information—even if the requestor doesn’t know the asset they want. In both cases, the end goal is not just storage, but accessibility and usability, ensuring that users can quickly find what they need and use the information effectively.

5 Essential Use Cases That Benefit From a Data Catalog

A modern data catalog offers a fast, intuitive way to find and access data products. These five essential use cases show how organizations benefit from one.

Simplifying and Accelerating Data Discovery

One challenge that data professionals face is accessing the right data when they need it. Legacy approaches typically require data users to ask IT or data engineers to find, retrieve, and validate the data products. Another hurdle is that these processes are sometimes manual, which further slows productivity, creates bottlenecks, and limits an organization’s ability to be truly data driven.

How a data catalog solves this problem. A data catalog allows business users, analysts, and other data consumers to quickly discover, understand, and manage datasets on their own. This enables faster decision-making without requiring a specialized skill set.

A searchable and intuitive interface allows users to locate the data assets they need in seconds. A data catalog categorizes data products, provides descriptions, and enables business-friendly tagging and annotations to improve accessibility. Users can search for data based on keywords, business terms, or specific attributes. They can also view metadata, including data definitions, ownership, and quality scores.

Strengthening Data Governance and Regulatory Compliance

Many organizations must comply with stringent regulations such as GDPR, HIPAA, and CCPA. These require tracking how data is collected, processed, and shared. Ensuring compliance becomes increasingly difficult as data ecosystems grow more complex. Without a data catalog, ensuring compliance is a complicated and time-consuming process.

How a data catalog solves this problem. A data catalog acts as a compliance hub to maintain comprehensive metadata, lineage tracking, and access controls. Organizations can monitor data lineage to see where it originates and how it’s transformed over its lifecycle. The catalog can also help define and enforce governance policies, ensuring sensitive data is properly classified and managed.

In addition, automated compliance reporting using pre-built documentation and audit trails helps ensure adherence to regulations. With visibility into data usage and governance, organizations can proactively manage risks and meet regulatory mandates.

Improving Data Integration and Collaboration Across Teams

Siloed data is a persistent challenge in organizations, regardless of industry. Business units and even individuals often manage their own datasets, without making them sharable across the enterprise. This leads to outdated, untrustworthy, and incomplete datasets.

How a data catalog solves this problem. A data catalog bridges these silos. It centralizes and connects all enterprise metadata, creating a single source of truth. With a unified view of enterprise data, businesses can break down silos and promote cross-functional collaboration, leading to more accurate reporting, streamlined operations, and stronger data-driven strategies.

Teams and business units across the organization can use the data catalog to share data assets, support best practices, and standardize business definitions. By standardizing definitions and establishing a common business vocabulary, the data catalog ensures that teams working across marketing, finance, and operations, for example, are using a consistent business vocabulary.

Boosting Data Quality and Trust for Better Decision-Making

Poor data quality leads to unreliable insights and misguided business decisions. Issues such as missing values, duplicate records, and outdated information create significant operational inefficiencies.

How a data catalog solves this problem. A data catalog helps organizations enhance data trustworthiness by surfacing quality indicators, tagging outdated or incomplete records, and flagging inconsistencies. Automated workflows can enforce data validation rules, ensuring that only accurate, trustworthy, and up-to-date data products are available.

Plus, data catalogs automate data profiling to detect errors and inconsistencies, while version control and historical tracking ensure business units are using the most current data products. By giving organizations greater transparency into data accuracy, lineage, and freshness, a data catalog increases confidence in data products.

Accelerating Data Onboarding and Training for New Employees

New employees who need to use data often face a learning curve to understand an organization’s data ecosystem. Without clear documentation, finding out where data resides, how it’s structured, and who owns it can be challenging.

How a data catalog solves this problem. A data catalog built on a knowledge graph architecture takes ease of use to a new level. It streamlines onboarding by providing a well-documented, searchable repository of data assets, including definitions, business rules, and access policies. This introduces new employees to an organization’s data products and reduces dependency on IT teams to support data discovery.

Discover Data Assets in Seconds—or Faster

A data catalog is more than just a management tool—it’s a strategic enabler that transforms the way businesses find, access, and optimize data assets. From enhancing data discovery to automatically updating metadata from all sources, a modern data catalog delivers ongoing value.

Take an immersive tour of the Actian Data Intelligence Platform to see firsthand how a data catalog, enterprise data marketplace, knowledge graph, and other capabilities come together in a unified solution. See how easy it can be to quickly discover data and AI assets, establish trust in your data, and democratize data access with confidence.

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.

AI & ML

Compliance Automation, Explained

Actian Corporation

April 6, 2025

fundamental truths of generative ai blog

Businesses today are required to comply with a variety of regulatory standards aimed at ensuring data security, privacy, and ethical business practices. Laws like the General Data Protection Regulation (GDPR), California Consumer Privacy Act (CCPA), and Health Insurance Portability and Accountability Act (HIPAA) demand that organizations not only protect sensitive data but also demonstrate compliance through regular audits, reports, and assessments.

However, keeping up with these regulations can be complex, time-consuming, and error-prone. Enter compliance automation, a game-changing solution that leverages technology to streamline and simplify the compliance process. In this article, we’ll explore what compliance automation is, how it works, and why it’s becoming an essential tool for organizations seeking to meet regulatory requirements efficiently.

What is Compliance Automation?

Compliance automation refers to using technology, typically software and artificial intelligence (AI), to streamline and automate processes related to regulatory compliance. Automating routine tasks, monitoring, reporting, and auditing activities helps organizations adhere to industry regulations, standards, and legal requirements more efficiently and accurately.

The goal of compliance automation is to reduce the time, effort, and potential for human error involved in maintaining compliance while also ensuring that the organization can quickly adapt to changing regulations and meet deadlines without missing critical requirements.

How Does Automated Compliance Work?

Automated compliance uses various tools and processes to ensure organizations consistently meet legal, industry, and security standards without manual intervention. Here’s a breakdown of how automated compliance works:

Data Monitoring and Management: Automated tools can track data across systems and ensure it aligns with privacy and security regulations. This includes data collection, storage, processing, and sharing practices.
Real-Time Monitoring: Automated systems continuously monitor activities and identify compliance risks as they occur. This helps detect anomalies or violations in real-time, preventing potential issues from escalating.
Regulatory Reporting: Automated systems can generate and submit reports to regulators or internal stakeholders, ensuring compliance documentation is accurate and up to date without the need for manual input.
Risk Management: Compliance automation tools assess and manage risks associated with data breaches, privacy violations, or non-compliance. They can identify vulnerabilities and suggest corrective actions to address those risks.
Policy Enforcement: Automated compliance solutions ensure an organization’s internal policies align with regulatory standards. They can enforce access controls, encryption, and other privacy practices.
Audit Trails and Logs: Compliance automation systems maintain detailed records of all activities related to data use and compliance, including data access, modifications, and processes. These audit trails are essential for internal and external audits.
Handling Data Subject Requests (DSRs): In the case of regulations like GDPR and CCPA, automation tools can process data subject requests such as data access, deletion, or modification automatically, improving response times and accuracy.

Advantages of Compliance Automation

Compliance automation offers a wide range of benefits for organizations, making the complex and time-consuming task of adhering to regulatory requirements more efficient, accurate, and manageable. Here are the key benefits of adopting compliance automation:

Increased Efficiency: Automating repetitive tasks such as reporting, monitoring, and auditing significantly reduces the manual effort required to ensure compliance.
Reduced Human Error: Automation reduces the chances of mistakes, especially in critical areas like data entry, risk assessment, and reporting.
Cost Savings: With fewer manual processes and reduced reliance on human labor for compliance-related tasks, organizations can save time and money.
Better Risk Management: Automation allows for proactive risk detection and management, helping organizations identify and mitigate potential compliance violations before they escalate into serious issues.
Scalability: Automated compliance systems can easily scale to handle increased data volumes and evolving regulatory requirements as a business grows.
Improved Transparency and Accountability: Automated systems provide a transparent record of all compliance activities, making it easier to demonstrate adherence to regulations and respond to audits or investigations.

How AI Can Help Simplify Compliance

AI can significantly simplify and enhance the compliance process for organizations by automating tasks, improving accuracy, and providing proactive monitoring. AI-powered tools are transforming how businesses approach regulatory requirements like GDPR, CCPA, and HIPAA, enabling them to meet complex compliance standards efficiently. Here’s how AI can simplify compliance:

Automated Data Classification and Mapping

One of the first steps toward regulatory compliance is to ensure that all personal data is correctly identified, classified, and mapped within an organization. AI-powered tools can automate the process of scanning data sources across an organization’s infrastructure, identifying sensitive personal information, and classifying it according to specific regulations (e.g., health data under HIPAA or consumer data under CCPA).

For example, AI can categorize data into sensitive and non-sensitive categories, determine its location, and map it across various systems to track who has access to it. This automated classification can reduce the time spent manually sorting through vast amounts of data and improve the accuracy of data inventory management.

Real-Time Monitoring and Risk Assessment

Continuous monitoring is essential to ensure that organizations comply with regulations at all times. AI can help by constantly monitoring data access, usage, and storage to detect potential violations or suspicious activities. For example, AI systems can be set up to track data breaches, unauthorized access, and anomalous behaviors that could indicate non-compliance with GDPR’s data protection rules or HIPAA’s confidentiality standards.

Moreover, AI-driven risk assessment tools can analyze patterns and predict potential compliance risks. By proactively identifying areas of concern, AI helps organizations take corrective actions before issues escalate into violations that could lead to penalties.

Automating Compliance Reporting

Compliance reporting can be an arduous task, especially when dealing with large volumes of data. AI can simplify this process by automatically generating detailed reports that align with the requirements of GDPR, CCPA, or HIPAA. For example:

GDPR requires organizations to provide a record of processing activities (RoPA). AI tools can automatically track all data processing activities, providing a reliable and up-to-date record.
CCPA requires businesses to offer transparency into how consumer data is collected and used. AI can automatically generate these reports on request, detailing consumer data, processing purposes, and data-sharing practices.
HIPAA mandates frequent risk assessments and audit logs. AI can track access to protected health information (PHI) and generate reports for internal and external audits.

These AI-generated reports are not only accurate but also customizable and generated in real-time, helping businesses meet tight deadlines for regulatory filings or requests from data subjects.

Facilitating Data Subject Rights Requests (DSRs)

Regulations like GDPR and CCPA grant individuals specific rights over their personal data, including the right to access, delete, or correct data. AI-powered systems can automate the handling of Data Subject Rights Requests (DSRs). For example, AI can:

Identify which data needs to be deleted or corrected upon request.
Automatically process and fulfill data access requests by locating the data across various systems.
Enable organizations to manage consent management and update records whenever consent is withdrawn.

This automation reduces the risk of non-compliance when responding to DSRs and ensures timely fulfillment of consumer requests, which is a critical aspect of maintaining GDPR and CCPA compliance.

Ensuring Data Security and Privacy by Design

One of the key principles of GDPR and HIPAA is “Privacy by Design,” which requires organizations to integrate privacy and security measures into their operations from the outset. AI can play a central role by automatically detecting vulnerabilities and ensuring that security controls are in place, especially when handling sensitive data.

AI-driven systems can identify potential weaknesses in data storage, encryption, access controls, and data sharing mechanisms. Additionally, AI can monitor encryption protocols, access logs, and data sharing activities, ensuring that sensitive data is always protected in accordance with HIPAA and GDPR requirements.

Employee Training and Awareness

Compliance is not just about tools and technology; it’s also about educating employees on regulatory requirements and best practices. AI-powered learning management systems (LMS) can deliver personalized compliance training programs that adapt to individual employees’ roles and the latest regulations.

AI can track employee progress, assess knowledge gaps, and provide targeted learning resources. This ensures that employees are always up to date with compliance protocols and helps minimize the risk of human error that could lead to violations.

Audit and Internal Investigations

AI can also assist in the audit and internal investigation processes by automatically flagging suspicious activities or policy violations. It can analyze vast logs, transactions, and communication datasets to identify non-compliance or risky behavior patterns. With AI-driven analytics, organizations can generate detailed audit trails and conduct investigations much faster, ensuring regulatory compliance is maintained.

Streamline Data Governance With Actian’s Platform

The complexity of modern data privacy regulations like GDPR, CCPA, and HIPAA can overwhelm many organizations. However, AI offers powerful tools to simplify and streamline compliance efforts. By automating data classification, monitoring, reporting, and risk assessments, AI helps organizations manage their compliance obligations more efficiently and accurately. In an ever-evolving regulatory landscape, adopting AI not only reduces the risk of non-compliance but also enhances data protection, operational efficiency, and trust with customers and regulators alike.

Streamline data governance and ensure regulatory adherence by partnering with Actian and taking advantage of the platform. This data intelligence platform helps businesses maintain accurate data lineage, ensure compliance, and optimize data management. Companies and organizations can leverage Actian’s expertise to enhance their data lineage strategy and achieve greater transparency, compliance, and efficiency.

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 Management

How to Automatically Map Metadata Across Complex Ecosystems

Actian Corporation

April 5, 2025

Organizations manage vast amounts of information daily across multiple systems, databases, and platforms. As data ecosystems grow in complexity, ensuring consistency, accuracy, and accessibility becomes increasingly challenging. This is where metadata management plays a crucial role. Effective metadata management enables organizations to track, organize, and govern data efficiently.

One of the most critical aspects of metadata management is metadata mapping, which ensures that data definitions, structures, and relationships remain aligned across various sources. Traditionally, mapping metadata required manual effort, making it labor-intensive and prone to errors. However, automation has transformed this process, allowing organizations to seamlessly map metadata across complex ecosystems.

In this article, we’ll explore how to automatically map metadata across large-scale enterprise environments, the benefits of automation, key challenges, and best practices to ensure accuracy and efficiency.

Understanding Metadata Management and Mapping

Metadata mapping is a part of an overarching metadata management program. As such, it’s important to understand the difference between the two concepts.

What is Metadata Management?

Metadata management refers to the administration of metadata, which includes defining, cataloging, and governing metadata across an organization. Metadata provides contextual information about data, such as its source, format, ownership, and relationships with other data assets.

Effective metadata management supports data governance, enhances discoverability, and ensures compliance with industry regulations.

What is Metadata Mapping?

Metadata mapping is the process of aligning metadata fields across different systems to ensure consistency and interoperability. It enables seamless data integration, transformation, and migration by defining relationships between different metadata elements.

For example, an organization may need to map customer data from a CRM system to a data warehouse while ensuring that attributes such as “Customer ID” and “Client Identifier” are correctly linked.

Challenges of Metadata Mapping in Complex Ecosystems

Managing metadata across a single database or application is relatively straightforward, but modern organizations operate within complex ecosystems that include:

Multiple databases (SQL, NoSQL, data warehouses, cloud storage).
Diverse applications (ERP, CRM, HR systems, BI tools).
Hybrid and multi-cloud environments.
Unstructured and structured data sources.

Some key challenges of metadata mapping in such environments are listed below.

1. Data Silos

Different business units often use disparate systems, leading to isolated metadata that lacks standardization. Known as data silos, these isolated environments make it difficult to transfer or use data across multiple sections of an organization.

2. Heterogeneous Data Formats

Metadata can exist in various formats (XML, JSON, CSV, relational databases, etc.), making it difficult to map fields accurately. Ideally, a company’s metadata management systems would account for these different formats.

3. Evolving Data Structures

Metadata definitions change over time due to system upgrades, business needs, or regulatory updates. Keeping mappings up to date is a consistent need that requires continuous monitoring.

4. Scalability Issues

Manually mapping metadata across thousands of data assets is time-consuming and more prone to error. Organizations can scale more easily and save time and money by automating the metadata mapping process.

5. Lack of Metadata Lineage

Understanding the origin and transformations of metadata is essential for compliance and data integrity. Without clear lineage tracking, errors can propagate across systems, and the source of these errors can be difficult to trace.

Automating Metadata Mapping: How it Works

Automated metadata mapping addresses these challenges by streamlining the process using machine learning (ML), artificial intelligence (AI), and metadata management platforms. Here’s how automation enhances metadata mapping.

Metadata Discovery and Classification

Automated tools scan various data sources to extract and classify metadata based on predefined rules. AI-powered engines can identify data types, formats, and structures automatically.

Schema Matching and Alignment

AI and machine learning algorithms compare metadata structures from different systems and suggest mappings based on similarities in data fields, patterns, and relationships.

Standardization

Automated tools enforce metadata governance policies to ensure that data elements conform to organizational standards, reducing inconsistencies.

Lineage and Traceability

Automation enables real-time tracking of metadata changes, providing a complete history of transformations, updates, and dependencies. This ensures transparency and compliance. It also makes error correction easier, as organizations can trace the source of any changes to the metadata.

Self-Learning and Continuous Improvement

AI-driven metadata management systems can learn from historical mappings, improving accuracy over time by recognizing common patterns and user-defined corrections.

Integration With Data Catalogs

Modern metadata mapping tools integrate with data catalogs, allowing users to search and retrieve metadata information efficiently. This enhances data discoverability and usability across the organization.

Benefits of Automating Metadata Mapping

Implementing automated metadata mapping provides organizations with several advantages.

1. Enhanced Accuracy and Consistency

Automation reduces human errors, ensuring that metadata mappings remain consistent across multiple systems.

2. Increased Efficiency and Scalability

Organizations can process and align metadata at scale without relying on manual intervention. This is crucial for large enterprises managing extensive data ecosystems.

3. Improved Compliance and Governance

With real-time metadata lineage tracking, organizations can meet regulatory requirements, such as GDPR, HIPAA, and CCPA, by maintaining accurate metadata records.

4. Faster Data Integration and Migration

Automated mapping simplifies data integration efforts, accelerating ETL (Extract, Transform, Load) processes and system migrations. ETL metadata mapping is important because it adjusts the metadata of incoming datasets to be consistent when performing the ETL process.

5. Cost Savings

Reducing manual efforts and preventing metadata inconsistencies helps organizations save time and resources, leading to lower operational costs.

6. ODS Mapping

An Operational Data Store (ODS) is a centralized repository that integrates data from multiple transactional systems to provide a consolidated, real-time view for operational reporting and analysis. ODS metadata mapping helps ensure data consistency, accuracy, and usability across the ecosystem.

Best Practices for Implementing Automated Metadata Mapping

To successfully implement automated metadata mapping, organizations should follow these best practices:

1. Define Metadata Governance Policies

Establish clear metadata standards, naming conventions, and governance policies to maintain consistency. This makes it easier to move data from one location to another or quickly find a data asset when needed.

2. Leverage AI and Machine Learning

Choose metadata management tools that utilize AI and ML to automate discovery, classification, and mapping processes. Actian’s platform offers robust metadata management tools, allowing organizations’ data teams to spend more time extracting value from assets and less time mapping.

3. Utilize a Centralized Metadata Repository

Store metadata in a centralized repository to ensure accessibility, visibility, and control across the organization. For example, organizations might choose to store data in a data warehouse or data lake.

4. Implement Metadata Lineage Tracking

Ensure that automated tools provide detailed lineage tracking to maintain audit trails and compliance records. This is especially important if the organization deals with sensitive or highly regulated information like credit card transactions.

5. Integrate with Existing Data Management Platforms

Choose solutions that seamlessly integrate with data catalogs, data lakes, and business intelligence tools to maximize efficiency.

6. Conduct Regular Metadata Audits

Automate metadata quality checks to identify and resolve inconsistencies before they impact business operations. Companies may also want to schedule manual audits of metadata and data assets at least once per year.

7. Enable User Collaboration and Feedback

Encourage data stewards and business users to review and refine automated mappings to improve accuracy.

Automate Metadata Mapping to Extract the Most Value Out of Data

Automating metadata mapping is essential for organizations operating within complex, data-intensive ecosystems. By leveraging AI-powered metadata management tools, businesses can ensure accuracy, efficiency, and compliance while reducing manual workload.

As data environments continue to evolve, investing in automated metadata mapping solutions will provide long-term benefits. These solutions enable organizations to maximize data value, streamline operations, and maintain a strong data governance framework.

Take a tour of the Actian Data Intelligence Platform to learn how Actian’s tool can help organize, map, and enhance crucial data assets.

About Actian Corporation

Awards

Actian Awarded Metadata Management Solution of the Year

Actian Corporation

April 3, 2025

Summary

Actian’s Data Intelligence Platform has been recognized as the 2025 Metadata Management Solution of the Year by Data Breakthrough, highlighting its advanced automation, lineage tracking, and enterprise-wide metadata visibility.

Award-winning innovation in metadata management — The platform earned the 2025 Data Breakthrough “Metadata Management Solution of the Year” for its automated metadata extraction, smart documentation, and lineage capabilities.
Automated metadata discovery & unified metadata repository — The platform continuously gathers metadata via advanced scanners and connectors, creating a context-rich, always-updated inventory to accelerate data discovery and governance.
Enhanced transparency, compliance & efficiency — Active metadata management delivers visibility into data provenance, governance enforcement, and business glossary alignment—empowering both technical and business users.

Actian is Awarded for Metadata Management

Actian was recently honored with a prestigious Data Breakthrough Award from Data Breakthrough. The Data Breakthrough Award program recognizes the most innovative companies and technologies that are transforming the data landscape.

Award-winners like Actian are honored for making a real-world impact with technology. This year, Actian received the 2025 Data Breakthrough “Metadata Management Solution of the Year” award for our data intelligence platform. The platform offers an active metadata management solution to power your data and analytics use cases. It collects, inventories, and shares metadata throughout the organization and across all your data sources.

What is Metadata Management and Why Does it Matter?

Metadata management is all about organizing and taking control of your data to make sure it’s governed, searchable, and used effectively across your organization. Essentially, it’s “data about data,” and the goal is to make everything you need to know about your data, such as lineage and dates, easy to find, trust, and use. It solves the common challenge of data teams not being able to quickly and efficiently find the data assets they need for their use cases.

By capturing, classifying, and securing metadata, businesses can unlock the full potential of their data assets, leading to better insights, smarter decisions, and improved operational efficiency. Proper metadata management helps ensure that your data is reliable, compliant, and accessible, which is key for any data-driven organization.

When it comes to metadata management, it’s all about the details. The process includes automating the discovery of data, standardizing metadata formats, and applying governance rules to keep everything in check. Using metadata catalogs and management tools helps you maintain consistency, enforce security, and ensure your data aligns with the needs of different departments and systems.

From descriptive and structural metadata to operational and administrative types, each plays a crucial role in organizing, classifying, and democratizing access to data. This careful management is what allows organizations to fully leverage their data and stay ahead in today’s data-driven world.

Choosing the Right Metadata Management Solution

According to DATAVERSITY, 80% of organizations now place a high priority on metadata management—and the Actian Data Intelligence Platform, is uniquely designed to meet this growing need. What sets the platform apart from other solutions is its innovative smart data inventory and discovery system, enabled by a data catalog, knowledge graph, enterprise data marketplace, self-service capabilities, and a vast array of native data source connectors that automatically gather and retrieve enterprise metadata.

Through advanced APIs and powerful scanners, the platform ensures a reliable, always-updated metadata repository that accelerates insights across the organization by making the right data assets fast and easy to find for any use case. This automated approach not only delivers consistent performance in metadata management, but also maps relationships between systems, applications, and reports—creating a context-rich landscape that offers real-time visibility into data assets and their transformations over time.

Both technical and business users can easily find and understand enterprise data assets, even if they don’t know what data they need when they start their search . Ultimately, the platform empowers data teams to build and maintain a robust repository of data assets that uses metadata to provide clarity around the provenance, context, and usage of enterprise data. By leveraging next-generation features, the Actian Data Intelligence Platform allows teams to spend less time searching for data and more time unlocking its value to drive smarter, data-informed business decisions.

Try the Award-Winning Platform With a Guided Tour

Actian Data Intelligence Platform is a cloud-based solution that helps you easily discover, access, and share trusted data. With its powerful metadata management features, it simplifies data search, exploration, governance, and compliance, allowing both data and business teams to quickly access the data assets the need, which facilitates smarter decision-making. The platform seamlessly connects to various data sources, including cloud, ERP, CRM, and relational systems, through built-in scanners and APIs.

By using knowledge graph technologies, the platform enhances data discovery and offers intelligent recommendations. It also ensures top-notch security with SOC 2 Type II and ISO 27001 compliance and is designed to work smoothly across hybrid and multi-cloud environments, making it scalable for any organization.

Take the self-guided tour or join a live demo.

About Actian Corporation

Data Management

Building a Metadata Management Culture: The Role of Actian

Phil Ostroff

April 2, 2025

The recent Gartner® Data and Analytics Conference in Orlando revealed a fascinating shift in enterprise priorities, including how they view metadata. While metadata management tools have evolved significantly, many attendees recognized a growing challenge: Most metadata management solutions appear strikingly similar in features and functionality.

As a result, the real differentiator is no longer just the technology itself but the tools’ ability to promote implementing and integrating metadata management into an organization’s culture, particularly in strengthening the role of data stewards.

What Gartner is Saying About Metadata Management and Data Stewards

At the conference, Gartner analysts emphasized that metadata management is no longer just about cataloging data—it’s about enabling organizations to extract real business value from their data assets. They highlighted the critical role of data stewards, who serve as the linchpin between metadata governance and operational execution.

Gartner stressed that organizations must go beyond tool adoption and focus on embedding metadata management into daily operations. This requires fostering a culture where data stewards are empowered with the right tools, automation, and governance frameworks to make metadata actionable rather than merely documented. The discussions clarified that metadata initiatives succeed when they align with business goals and integrate seamlessly with data-driven decision-making.

Why Metadata Management Culture Matters

Enterprises investing in metadata management tools often assume that purchasing software is the biggest hurdle. The challenge lies in operationalizing the tool, embedding it into daily workflows, aligning it with governance policies, and fostering a culture where data stewards become empowered leaders rather than passive users.

At the conference, discussions repeatedly circled back to this cultural transformation. Organizations that merely implement a metadata management solution without an adoption strategy often find themselves struggling with stagnant or underutilized metadata repositories. This underscores the need for an approach that prioritizes usability, collaboration, and governance.

How Actian Stands Apart

Among the many metadata management solutions available today, the one from the Actian Data Intelligence Platform distinguishes itself by providing a seamless, user-friendly experience tailored to organizations looking to build a sustainable metadata culture. While competitors like Collibra and Informatica offer robust feature sets, their complexity often acts as a barrier to adoption.

Here are five ways the Actian Data Intelligence Platform outshines traditional vendors to help organizations establish a thriving metadata management culture:

Intuitive User Experience: One of the primary complaints about metadata tools like the ones from some legacy vendors is their steep learning curve. Actian Data Intelligence Platform prioritizes ease of use with an intuitive interface, reducing friction for business users and accelerating adoption across data stewardship teams.
Automated Metadata Harvesting: Unlike legacy solutions that require extensive manual effort, the Actian Data Intelligence Platform provides automated metadata harvesting and lineage tracking, allowing data stewards to focus on governance and quality rather than spending excessive time on data discovery.
Collaboration-Centric Design: The success of a metadata initiative relies on engagement from business and IT users alike. Actian Data Intelligence Platform fosters collaboration by integrating directly with modern data ecosystems and providing contextual insights that facilitate communication between data stewards, analysts, and governance teams.
Agility and Scalability: Enterprises need metadata solutions that can scale with their evolving needs. Actian Data Intelligence Platform flexible architecture allows organizations to start small and expand their metadata initiatives as they mature, reducing the risk of stalled implementations due to over-engineered processes.
Lower Total Cost of Ownership (TCO): Compared to some legacy vendor offerings, the Actian Data Intelligence Platform offers a streamlined deployment model that minimizes overhead costs, ensuring organizations can maximize their return on investment without excessive licensing or implementation fees.

Empowering Data Stewards on their Metadata Journey

A well-integrated metadata management culture hinges on the role of data stewards. These professionals act as the bridge between raw data and actionable insights, ensuring metadata is well-documented, governed, and accessible across the organization.

With the Actian Data Intelligence Platform, and, in particular, its Studio functionality, data stewards gain access to intuitive tools that simplify their workflows rather than burdening them with administrative complexity. Features like automated suggestions, AI-driven data classification, and interactive data lineage mapping allow stewards to focus on enabling business users rather than getting lost in technical minutiae.

Furthermore, the Actian Data Intelligence Platform’s open integration framework ensures that organizations can align metadata efforts with broader data governance initiatives. This alignment is critical for cultivating a culture where metadata is treated as a strategic asset rather than an afterthought. Additionally, the platform’s data marketplace empowers data stewards by providing a centralized platform for and governing enterprise data assets efficiently.

The Future of Metadata Management

As enterprises recognize that metadata management is not just about tools but about fostering a metadata-driven culture, solutions like the Actian Data Intelligence Platform are poised to play a crucial role. By prioritizing usability, automation, collaboration, and cost efficiency, the Actian Data Intelligence Platform empowers organizations to break down silos, strengthen the role of data stewards, and ultimately drive more value from their data assets.

The conversation at Gartner’s conference made one thing clear: Metadata management success is no longer about who has the most features. It’s about which solution best enables organizations to establish a sustainable and effective metadata culture. And in that regard, the Actian Data Intelligence Platform stands out as a true enabler of enterprise-wide metadata transformation.

Find out more by taking a self-guided tour.

About Phil Ostroff

Phil Ostroff is Director of Competitive Intelligence at Actian, leveraging 30+ years of experience across automotive, healthcare, IT security, and more. Phil identifies market gaps to ensure Actian's data solutions meet real-world business demands, even in niche scenarios. He has led cross-industry initiatives that streamlined data strategies for diverse enterprises. Phil's Actian blog contributions offer insights into competitive trends, customer pain points, and product roadmaps. Check out his articles to stay informed on market dynamics.

Data Governance

6 Reasons to Include Business Users in Your Data Catalog Strategy

Charlie Wood

April 1, 2025

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 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

Actian Corporation

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.

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 Actian Corporation

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.

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