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Aug 6 2025

A Step Ahead: From Acts to Aggregates — Record-ness and Data-ness in Practice

Introduction What is the difference between records and data? What differentiates records managers from data managers? Do these distinctions still matter as organizations take the plunge into artificial intelligence? Discussions that attempt to distinguish between records and data frequently articulate a heuristic for differentiation. “These items are records; those items are data.” Many organizations have […]

Understanding Structural Metadata

Today, organizations and individuals face an ever-growing challenge: the sheer volume of data being generated and stored across various systems. This data needs to be properly organized, categorized, and made easily accessible for efficient decision-making. One critical aspect of organizing data is through the use of metadata, which serves as a descriptive layer that helps users understand, find, and utilize data effectively.

Among the various types of metadata, structural metadata plays a crucial role in facilitating improved data management and discovery. This article will define what structural metadata is, why it is useful, and how the Actian Data Intelligence Platform can help organizations better organize and manage their structural metadata to enhance data discovery.

What is Structural Metadata?

Metadata is often classified into various types, such as descriptive metadata, administrative metadata, and structural metadata. While descriptive metadata provides basic information about the data (e.g., title, author, keywords), and administrative metadata focuses on the management and lifecycle of data (e.g., creation date, file size, permissions), structural metadata refers to the organizational elements that describe how data is structured within a dataset or system.

In simpler terms, structural metadata defines the relationships between the different components of a dataset. It provides the blueprint for how data is organized, linked, and formatted, making it easier for users to navigate complex datasets. In a relational database, for example, structural metadata would define how tables, rows, columns, and relationships between entities are arranged. In a document repository, it could describe the format and organization of files, such as chapters, sections, and subsections.

Key Features of Structural Metadata

Here are some key aspects of structural metadata:

Data Relationships: Structural metadata defines the relationships between data elements or files within a dataset. For instance, in a relational database, it describes how tables are linked through keys or indexes, and how columns relate to each other within the same table.
Data Formats and Types: It specifies the data formats used (e.g., text, numeric, date) and helps identify the data types of each element. In data warehousing, structural metadata defines the schema, such as whether data is stored in star or snowflake schema.
Hierarchical Organization: It outlines how data is organized hierarchically or sequentially, such as parent-child relationships between datasets or subfolders within a directory structure.
Data Integrity and Constraints: Structural metadata often includes information on constraints like field lengths, data validation rules, and referential integrity, ensuring that the data is consistent and follows predefined rules.
Access and Navigation: This metadata helps users understand how to access and navigate large datasets by providing information about where and how data is located, allowing for efficient querying and retrieval.

Why is Structural Metadata Important?

Structural metadata plays a fundamental role in ensuring that data is understandable, accessible, and usable. Here are several reasons why it is essential:

Data Discovery and Access: Structural metadata enables users to locate and understand data more efficiently. By understanding how data is organized and the relationships that exist between various components, users can easily navigate large datasets to find relevant information without having to sift through each individual data element.
Data Quality and Consistency: When the structure of a dataset is clearly defined through metadata, it ensures that data is consistently formatted and follows specific rules. This consistency helps maintain data quality and reliability, which is crucial for analysis and decision-making.
Improved Data Integration: Organizations often deal with data spread across different systems, platforms, and applications. Structural metadata facilitates integration by defining how disparate data sources are connected or how they should interact. It helps in joining datasets correctly, enabling better cross-platform analytics.
Data Governance and Compliance: In regulated industries, where data must meet specific legal or industry standards, structural metadata ensures that data complies with necessary rules and regulations. It makes audits, data governance practices, and compliance checks easier and more transparent.
Efficient Querying and Analytics: In analytics and business intelligence tools, having a well-structured dataset enables more efficient querying of data. Structural metadata allows for faster retrieval of data, helping analysts and business users generate insights quickly and accurately.
Enhanced Data Management: By providing clear definitions of data formats, relationships, and constraints, structural metadata streamlines the process of managing, updating, and maintaining datasets. It reduces errors and minimizes the risk of misinterpreting data, which can lead to faulty conclusions.

Challenges of Managing Structural Metadata

Despite its importance, managing structural metadata is not without challenges.

Complexity: As datasets grow and become more complex, it can be difficult to keep track of all the different relationships, hierarchies, and formats. Large organizations may struggle to maintain a consistent structure across numerous datasets.
Data Silos: In many organizations, data is stored in separate systems or applications, each with its own metadata standards. This can create silos where data is not easily discoverable or usable across departments or platforms.
Lack of Standardization: Without a standardized approach to metadata management, organizations may struggle to consistently define structural metadata across their datasets. This inconsistency can lead to confusion and errors, hindering data integration and analysis efforts.
Scalability: As organizations continue to generate more data, the challenge of managing structural metadata at scale becomes more pronounced. This requires robust tools and systems capable of handling increasing volumes of metadata efficiently.

How Actian Can Help Organize and Manage Structural Metadata for Better Data Discovery

The Actian Data Intelligence Platform provides organizations with the tools to handle their metadata efficiently. By enabling centralized metadata management, organizations can easily catalog and manage structural metadata, thereby enhancing data discovery and improving overall data governance. Here’s how the platform can help:

1. Centralized Metadata Repository

The Actian Data Intelligent Platform allows organizations to centralize all metadata, including structural metadata, into a single, unified repository. This centralization makes it easier to manage, search, and access data across different systems and platforms. No matter where the data resides, users can access the metadata and understand how datasets are structured, enabling faster data discovery.

2. Automated Metadata Ingestion

The platform supports the automated ingestion of metadata from a wide range of data sources, including databases, data lakes, and cloud storage platforms. This automation reduces the manual effort required to capture and maintain metadata, ensuring that structural metadata is always up to date and accurately reflects the structure of the underlying datasets.

3. Data Lineage and Relationships

With Actian’s platform, organizations can visualize data lineage and track the relationships between different data elements. This feature allows users to see how data flows through various systems and how different datasets are connected. By understanding these relationships, users can better navigate complex datasets and conduct more meaningful analyses.

4. Data Classification and Tagging

The Actian Data Intelligence Platform provides powerful data classification and tagging capabilities that allow organizations to categorize data based on its structure, type, and other metadata attributes. This helps users quickly identify the types of data they are working with and make more informed decisions about how to query and analyze it.

5. Searchable Metadata Catalog

The platform’s metadata catalog enables users to easily search and find datasets based on specific structural attributes. Whether looking for datasets by schema, data format, or relationships, users can quickly pinpoint relevant data, which speeds up the data discovery process and improves overall efficiency.

6. Collaboration and Transparency

Actian’s platform fosters collaboration by providing a platform where users can share insights, metadata definitions, and best practices. This transparency ensures that everyone in the organization is on the same page when it comes to understanding the structure of data, which is essential for data governance and compliance.

7. Data Governance and Compliance

Using a federated knowledge graph, organizations can automatically identify, classify, and track data assets based on contextual and semantic factors. This makes it easier to map assets to key business concepts, manage regulatory compliance, and mitigate risks.

Get a Tour of the Actian Data Intelligence Platform Today

Managing and organizing metadata is more important than ever in the current technological climate. Structural metadata plays a crucial role in ensuring that datasets are organized, understandable, and accessible. By defining the relationships, formats, and hierarchies of data, structural metadata enables better data discovery, integration, and analysis.

However, managing this metadata can be a complex and challenging task, especially as datasets grow and become more fragmented. That’s where the Actian Data Intelligence Platform comes in. With Actian’s support, organizations can unlock the full potential of their data, streamline their data management processes, and ensure that their data governance practices are aligned with industry standards, all while improving efficiency and collaboration across teams.

Take a tour of the Actian Data Intelligence Platform or sign up for a personalized demonstration today.

The post Understanding Structural Metadata appeared first on Actian.

Data Contracts, AI Search, and More: Actian’s Spring ’25 Product Launch

This blog introduces Actian’s Spring 2025 launch, featuring 15 new capabilities that improve data governance, observability, productivity, and end-to-end integration across the data stack.

Actian’s new federated data contracts give teams full control over distributed data product creation and lifecycle management.
Ask AI and natural language search integrations boost productivity for business users across BI tools and browsers.
Enhanced observability features deliver real-time alerts, SQL-based metrics, and auto-generated incident tickets to reduce resolution time.

Actian’s Spring 2025 launch introduces 15 powerful new capabilities across our cloud and on-premises portfolio that help modern data teams navigate complex data landscapes while delivering ongoing business value.

Whether you’re a data steward working to establish governance at the source, a data engineer seeking to reduce incident response times, or a business leader looking to optimize data infrastructure costs, these updates deliver immediate, measurable impact.

What’s new in the Actian Cloud Portfolio

Leading this launch is an upgrade to our breakthrough data contract first functionality that enables true decentralized data management with enterprise-wide federated governance, allowing data producers to build and publish trusted data assets while maintaining centralized control. Combined with AI-powered natural language search through Ask AI and enhanced observability with custom SQL metrics, our cloud portfolio delivers real value for modern data teams.

Actian Data Intelligence

Decentralized Data Management Without Sacrificing Governance

The Actian Data Intelligence Platform (formerly Zeenea) now supports a complete data products and contracts workflow. Achieve scalable, decentralized data management by enabling individual domains to design, manage, and publish tailored data products into a federated data marketplace for broader consumption.

Combined with governance-by-design through data contracts integrated into CI/CD pipelines, this approach ensures governed data from source to consumption, keeping metadata consistently updated.

Organizations no longer need to choose between development velocity and catalog accuracy; they can achieve both simultaneously. Data producers who previously spent hours on labor-intensive tasks can now focus on quickly building data products, while business users gain access to consistently trustworthy data assets with clear contracts for proper usage.

Ask AI Transforms How Teams Find and Understand Data

Ask AI, an AI-powered natural language query system, changes how users interact with their data catalog. Users can ask questions in plain English and receive contextually relevant results with extractive summaries.

This semantic search capability goes far beyond traditional keyword matching. Ask AI understands the intent, searches across business glossaries and data models, and returns not just matching assets but concise summaries that directly answer the question. The feature automatically identifies whether users are asking questions versus performing keyword searches, adapting the search mechanism accordingly.

Business analysts no longer need to rely on data engineers to interpret data definitions, and new team members can become productive immediately without extensive training on the data catalog.

Chrome Extension Brings Context Directly to Your Workflow

Complementing Ask AI, our new Chrome Extension automatically highlights business terms and KPIs within BI tools. When users hover over highlighted terms, they instantly see standardized definitions pulled directly from the data catalog, without leaving their reports or dashboards.

For organizations with complex BI ecosystems, this feature improves data literacy while ensuring consistent interpretation of business metrics across teams.

Enhanced Tableau and Power BI Integration

Our expanded BI tool integration provides automated metadata extraction and detailed field-to-field lineage for both Tableau and Power BI environments.

For data engineers managing complex BI environments, this eliminates the manual effort required to trace data lineage across reporting tools. When business users question the accuracy of a dashboard metric, data teams can now provide complete lineage information in seconds.

Actian Data Observability

Custom SQL Metrics Eliminate Data Blind Spots

Actian Data Observability now supports fully custom SQL metrics. Unlike traditional observability tools that limit monitoring to predefined metrics, this capability allows teams to create unlimited metric time series using the full expressive power of SQL.

The impact on data reliability is immediate and measurable. Teams can now detect anomalies in business-critical metrics before they affect downstream systems or customer-facing applications.

Actionable Notifications With Embedded Visuals

When data issues occur, context is everything. Our enhanced notification system now embeds visual representations of key metrics directly within email and Slack alerts. Data teams get immediate visual context about the severity and trend of issues without navigating to the observability tool.

This visual approach to alerting transforms incident response workflows. On-call engineers can assess the severity of issues instantly and prioritize their response accordingly.

Automated JIRA Integration and a new Centralized Incident Management Hub

Every detected data incident now automatically creates a JIRA ticket with relevant context, metrics, and suggested remediation steps. This seamless integration ensures no data quality issues slip through the cracks while providing a complete audit trail for compliance and continuous improvement efforts.

Mean time to resolution (MTTR) improves dramatically when incident tickets are automatically populated with relevant technical context, and the new incident management hub facilitates faster diagnosis and resolution.

Redesigned Connection Flow Empowers Distributed Teams

Managing data connections across large organizations has always been a delicate balance between security and agility. Our redesigned connection creation flow addresses this challenge by enabling central IT teams to manage credentials and security configurations while allowing distributed data teams to manage their data assets independently.

This decoupled approach means faster time-to-value for new data initiatives without compromising security or governance standards.

Expanded Google Cloud Storage Support

We’ve added wildcard support for Google Cloud Storage file paths, enabling more flexible monitoring of dynamic and hierarchical data structures. Teams managing large-scale data lakes can now monitor entire directory structures with a single configuration, automatically detecting new files and folders as they’re created.

What’s New in the Actian On-Premises Portfolio

Our DataConnect 12.4 release delivers powerful new capabilities for organizations that require on-premises data management solutions, with enhanced automation, privacy protection, and data preparation features.

DataConnect v12.4

Automated Rule Creation with Inspect and Recommend

The new Inspect and Recommend feature analyzes datasets and automatically suggests context-appropriate quality rules.

This capability addresses one of the most significant barriers to effective data quality management: the time and expertise required to define comprehensive quality rules for diverse datasets. Instead of requiring extensive manual analysis, users can now generate, customize, and implement effective quality rules directly from their datasets in minutes.

Advanced Multi-Field Conditional Rules

We now support multi-field, conditional profiling and remediation rules, enabling comprehensive, context-aware data quality assessments. These advanced rules can analyze relationships across multiple fields, not just individual columns, and automatically trigger remediation actions when quality issues are detected.

For organizations with stringent compliance requirements, this capability is particularly valuable.

Data Quality Index Provides Executive Visibility

The new Data Quality Index feature provides a simple, customizable dashboard that allows non-technical stakeholders to quickly understand the quality level of any dataset. Organizations can configure custom dimensions and weights for each field, ensuring that quality metrics align with specific business priorities and use cases.

Instead of technical quality metrics that require interpretation, the Data Quality Index provides clear, business-relevant indicators that executives can understand and act upon.

Streamlined Schema Evolution

Our new data preparation functionality enables users to augment and standardize schemas directly within the platform, eliminating the need for separate data preparation tools. This integrated approach offers the flexibility to add, reorder, or standardize data as needed while maintaining data integrity and supporting scalable operations.

Flexible Masking and Anonymization

Expanded data privacy capabilities provide sophisticated masking and anonymization options to help organizations protect sensitive information while maintaining data utility for analytics and development purposes. These capabilities are essential for organizations subject to regulations such as GDPR, HIPAA, CCPA, and PCI-DSS.

Beyond compliance requirements, these capabilities enable safer data sharing with third parties, partners, and research teams.

Take Action

Join the Corinium webinar Data Contract First Governance: Building Trust Through Data Products
Download the eBook Putting Data Contracts First: The Engineer’s Guide to Data Trust
Contact us to schedule a demo and see these new features in action

The post Data Contracts, AI Search, and More: Actian’s Spring ’25 Product Launch appeared first on Actian.

Thoughts on the DAMA DMBoK

Many years ago, I contributed material on Database Development and Database Operations Management to the first edition of DAMA International’s “Data Management Body of Knowledge” (the DAMA DMBoK).i Now that work has begun on the Third Edition of the DMBoK, I’d like to share a few thoughts and critiques on the DMBoK for consideration. Some […]

The Role of Data Stewards in Data Governance

Data has evolved from a byproduct of business operations into a strategic asset — one that demands thoughtful oversight and intentional governance. As organizations increasingly rely on data to drive decisions, compliance, and innovation, the role of the data steward has taken on new urgency and importance.

Data stewards are responsible for managing the quality and accessibility of data within an organization. They play a critical role in ensuring that data governance policies are followed and that data is properly utilized across the organization. In this article, we will explore the role of data stewards, their responsibilities, and how platforms like the Actian Data Intelligence Platform can help streamline and optimize their efforts in managing data governance.

What is Data Stewardship?

Data stewardship refers to the practice of defining, managing, overseeing, and ensuring the quality of data and data assets within an organization. It is a fundamental aspect of data governance, which is a broader strategy for managing data across the organization in a way that ensures compliance, quality, security, and value. While data governance focuses on the overall structure, policies, and rules for managing data, data stewardship is the hands-on approach to ensuring that those policies are adhered to and that data is kept accurate, consistent, and reliable.

The Role and Responsibilities of Data Stewards

Data stewards are the custodians of an organization’s data. They are the bridge between technical teams and business users, ensuring that data meets the needs of the organization while adhering to governance and regulatory standards.

Below are some of the key responsibilities of data stewards within a data governance framework.

1. Data Quality Management

Data stewards ensure data quality across the organization. They ensure data is accurate, consistent, complete, and up to date. They are tasked with establishing data quality standards and monitoring data to ensure that it meets these criteria. Data stewards are also responsible for identifying and addressing data quality issues, such as duplicates, missing data, or inconsistencies.

2. Data Classification and Categorization

Data stewards are responsible for organizing and classifying data—applying metadata, managing access controls, and ensuring sensitive information is properly handled—to make data accessible, understandable, and secure for stakeholders.

3. Data Governance Compliance

Data stewards ensure that the organization follows data governance policies and procedures. They monitor and enforce compliance with data governance standards and regulatory requirements such as GDPR, CCPA, and HIPAA.

4. Data Access and Usage Monitoring

Data stewards define and enforce data access policies, ensuring that only authorized personnel can access sensitive or restricted data. They also monitor for violations of governance policy.

5. Data Lifecycle Management

Data stewards oversee the entire data lifecycle, from creation and storage to deletion and archiving.

6. Collaboration With Data Governance Stakeholders

Data stewards work closely with stakeholders in the data governance ecosystem, including data owners, data engineers, business analysts, and IT teams. They ensure that data governance practices are aligned with business goals. Data stewards are responsible for bridging the gap between technical and business teams, ensuring that the data is aligned with both technical requirements and business objectives.

7. Reporting and Documentation

Data stewards are responsible for documenting data governance policies, standards, and procedures. This documentation is essential for audits, regulatory compliance, and internal training.

Actian Data Intelligence Platform Makes Data Stewardship Easier

Data stewards play a crucial role in the success of an organization’s data governance framework. They are responsible for managing data quality, ensuring compliance, monitoring data access, and maintaining data integrity. By leveraging the Actian Data Intelligence Platform, data stewards can streamline their responsibilities and more effectively govern data across the organization.

With the platform’s centralized data catalog, automated data quality monitoring, data lineage tracking, and compliance tools, data stewards are empowered to maintain high-quality data, ensure regulatory compliance, and foster collaboration between stakeholders.

Request a personalized demo of the Actian Data Intelligence Platform today.

The post The Role of Data Stewards in Data Governance appeared first on Actian.

Turning Data into Insights: A Smarter Playbook for Mid-Size Businesses

In today’s hyper-competitive economy, data is a critical asset that drives innovation, strategic decision-making, and competitive advantage. However, for many mid-sized organizations, turning raw data into actionable business intelligence (BI) is challenging. The rapid pace of technological advancements, coupled with increasingly complex data environments, presents significant hurdles, particularly for those with limited resources to build […]

The post Turning Data into Insights: A Smarter Playbook for Mid-Size Businesses appeared first on DATAVERSITY.

Real-Time Financial Data: Transforming Decision-Making in the Banking Sector

Think of a bank’s treasurer responsible for international cash movement across its global accounts. He receives a notification that a significant amount has been credited to one of the accounts in Asia. A few minutes later, the funds have been transferred to clear up a cash requirement on the other side of the world in Europe. […]

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Data Owner vs. Data Steward: What’s the Difference?

Companies rely on data to make strategic decisions, improve operations, and drive innovation. However, with the growing volume and complexity of data, managing and maintaining its integrity, accessibility, and security has become a major challenge.

This is where the roles of data owners and data stewards come into play. Both are essential in the realm of data governance, but their responsibilities, focus areas, and tasks differ. Understanding the distinction between data owner vs. data steward is crucial for developing a strong data governance framework.

This article explores the differences between data owners and data stewards. It explains the importance of both roles in effective data management and shares how Actian can help both data owners and data stewards collaborate and manage data governance more efficiently.

What is a Data Owner?

A data owner is the individual or team within an organization who is ultimately responsible for a specific set of data. The data owner is typically a senior leader, department head, or business unit leader who has the authority over data within their domain.

Data owners are accountable for the data’s security, compliance, and overall business value. They are responsible for ensuring that data is used appropriately, securely, and per organizational policies and regulations.

Key responsibilities of a data owner include:

Accountability for Data Security: Data owners are responsible for ensuring that data is protected and secure. This includes managing access permissions, ensuring compliance with data protection regulations such as GDPR or HIPAA, and working with IT teams to prevent data breaches.
Defining Data Usage: Data owners determine how their data should be used within the organization. They help define the policies and rules that govern how data is accessed and shared, ensuring that data serves business needs without exposing the organization to risk.
Compliance and Regulatory Requirements: Data owners must ensure that their data complies with relevant regulations and industry standards. They oversee audits and ensure that proper documentation and controls are in place to meet compliance requirements.
Data Strategy Alignment: Data owners work closely with organizational leadership to ensure that the data aligns with broader business strategies and goals. They ensure that data is properly utilized to drive business growth, innovation, and decision-making.
Data Access Control: Data owners have the authority to define who can access their data. They set up permissions and manage user roles to ensure that only authorized individuals can access sensitive or critical data.

What is a Data Steward?

While the data owner holds the ultimate responsibility for the data, the data steward is the individual who takes a more operational role in managing, maintaining, and improving data quality. Data stewards typically handle the day-to-day management and governance of data, ensuring that it’s accurate, complete, and properly classified.

They act as the custodian of data within the organization, working closely with data owners and other stakeholders to ensure that data is used effectively across different teams and departments.

Key responsibilities of a data steward include:

Data Quality Management: Data stewards play a critical role in maintaining data quality. They are responsible for ensuring that data is accurate, complete, consistent, and up to date. This involves implementing data validation rules, monitoring data integrity, and addressing data quality issues as they arise.
Metadata Management: Data stewards manage the metadata associated with data. This includes defining data definitions, data types, and relationships between datasets and data assets. By organizing and maintaining metadata, data stewards ensure that data can be easily understood and accessed by anyone in the organization who needs it.
Data Classification and Standardization: Data stewards are involved in classifying data, tagging it with relevant metadata, and establishing data standards. This helps ensure that data is consistent, well-organized, and easily searchable.
Collaboration with Data Users: Data stewards often work closely with data users, such as analysts, data scientists, and business units, to understand their needs and provide them with the appropriate resources. They help ensure that data is accessible, usable, and meets the specific needs of different departments.
Data Lineage and Documentation: Data stewards maintain records of data lineage, which track the flow and transformation of data from its source to its destination. This helps ensure traceability and transparency, allowing users to understand where data comes from and how it has been modified over time.

Data Owner vs. Data Steward: Key Differences

While both data owners and data stewards are essential to effective data governance, their roles differ in terms of focus, responsibilities, and authority. Below is a comparison of data owner vs. data steward roles to highlight their distinctions:

	Data Owner	Data Steward
Primary Responsibility	Overall accountability for data governance and security.	Day-to-day management, quality, and integrity of data.
Focus	Strategic alignment, compliance, data usage, and access control.	Operational focus on data quality, metadata management, and classification.
Authority	Holds decision-making power on how data is used and shared.	Executes policies and guidelines set by data owners, ensures data quality.
Collaboration	Works with senior leadership, IT, legal, and compliance teams.	Works with data users, IT teams, and data owners to maintain data quality.
Scope	Oversees entire datasets or data domains.	Focuses on the practical management and stewardship of data within domains.

Why Both Roles are Essential in Data Governance

Data owners and data stewards play complementary roles in maintaining a strong data governance framework. The success of data governance depends on a clear division of responsibilities between these roles:

Data owners provide strategic direction, ensuring that data aligns with business goals, complies with regulations, and is properly secured.
Data stewards ensure that the data is usable, accurate, and accessible on a daily basis, helping to operationalize the governance policies set by the data owners.

Together, they create a balance between high-level oversight and hands-on data management. This ensures that data is not only protected and compliant but also accessible, accurate, and valuable for the organization.

How Actian Supports Data Owners and Data Stewards

Actian offers a powerful data governance platform designed to support both data owners and data stewards in managing their responsibilities effectively. It provides tools that empower both roles to maintain high-quality, compliant, and accessible data while streamlining collaboration between these key stakeholders.

Here are six ways the Actian Data Intelligence Platform supports data owners and data stewards:

1. Centralized Data Governance

The centralized platform enables data owners and data stewards to manage their responsibilities in one place. Data owners can set governance policies, define data access controls, and ensure compliance with relevant regulations. Meanwhile, data stewards can monitor data quality, manage metadata, and collaborate with data users to maintain the integrity of data.

2. Data Lineage and Traceability

Data stewards can use the platform to track data lineage, providing a visual representation of how data flows through the organization. This transparency helps data stewards understand where data originates, how it’s transformed, and where it’s used, which is essential for maintaining data quality and ensuring compliance. Data owners can also leverage this lineage information to assess risk and ensure that data usage complies with business policies.

3. Metadata Management

Metadata management capabilities embedded in the platform allow data stewards to organize, manage, and update metadata across datasets. This ensures that data is well-defined and easily accessible for users. Data owners can use metadata to establish data standards and governance policies, ensuring consistency across the organization.

4. Automated Data Quality Monitoring

Data stewards can use the Actian Data Intelligence Platform to automate data quality checks, ensuring that data is accurate, consistent, and complete. By automating data quality monitoring, the platform reduces the manual effort required from data stewards and ensures that data remains high-quality at all times. Data owners can rely on these automated checks to assess the overall health of their data governance efforts.

5. Collaboration Tools

The platform fosters collaboration between data owners, data stewards, and other stakeholders through user-friendly tools. Both data owners and stewards can share insights, discuss data-related issues, and work together to address data governance challenges. This collaboration ensures that data governance policies are effectively implemented, and data is managed properly.

6. Compliance and Security

Data owners can leverage the platform to define access controls, monitor data usage, and ensure that data complies with industry regulations. Data stewards can use the platform to enforce these policies and maintain the security and integrity of data.

Data Owners and Stewards Can Tour the Platform to Experience Its Capabilities

Understanding the roles of data owner vs. data steward is crucial for establishing an effective data governance strategy. Data owners are responsible for the strategic oversight of data, ensuring its security, compliance, and alignment with business goals, while data stewards manage the day-to-day operations of data, focusing on its quality, metadata, and accessibility.

Actian supports both roles by providing a centralized platform for data governance, automated data quality monitoring, comprehensive metadata management, and collaborative tools. By enabling both data owners and data stewards to manage their responsibilities effectively, the platform helps organizations maintain high-quality, compliant, and accessible data, which is essential for making informed, data-driven decisions.

Tour the Actian Data Intelligence Platform or schedule a personalized demonstration of its capabilities today.

The post Data Owner vs. Data Steward: What’s the Difference? appeared first on Actian.

Beyond Paper Policies: Building a Living Data Policy Framework

Data policies serve as the guardrails for how organizations manage their most valuable asset: data. Just as communities establish guidelines for shared community spaces, data policies provide the framework for how teams access, utilize, and govern their collective and shared data resources. These policies aren’t merely bureaucratic exercises. They establish the rules of engagement for […]

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Author: Subasini Periyakaruppan

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Mar 5 2025

A Data Value Manifesto

If you haven’t already heard, a number of organizations have laid off their CDOs and CDO groups and data teams because of a perceived lack of significant or measurable business value. In addition, a recently released report from MIT Sloan delivers some very depressing numbers about the efficacy of CDO groupsi: The average tenure of […]

Living the Ungoverned Life

Organizations often assume they have data governance under control, but in reality, many are simply reacting to data chaos rather than actively managing it. This isn’t due to negligence or a lack of concern — rather, it’s because they don’t recognize that governance is already happening, albeit informally and inconsistently. Every day, employees make critical […]

The Serviceberry Mindset: How Nature’s Gift Economy Can Reshape Data Governance

The Death of the Data Silo Is Not the End of the Problem For years, we’ve heard that breaking down data silos is the holy grail of business transformation. We’ve been told that better pipelines, integrated analytics, and AI-driven decision-making will finally unlock the full potential of enterprise data. But here’s the question no one […]

Reimagining Data Preparation for High-Impact Decision-Making

Data often arrives from multiple sources in inconsistent forms, including duplicate entries from CRM systems, incomplete spreadsheet records, and mismatched naming conventions across databases. These issues slow analysis pipelines and demand time-consuming cleanup. Organizations now use machine learning-assisted data preparation to address these challenges, which automatically standardizes formats, detects anomalies, and applies business rules. Data […]

The Art of Lean Governance: Moving Beyond Governance Buzzwords and Bling

This column will expand on a Systems Thinking approach to Data Governance and focus on process control. The vendors of myriad governance tools focus on metadata, dictionaries, and quality metrics. Their marketing is a sea of buzzwords and bling — bells and whistles. Yet, where is the evidence of adding actual business value, defined as […]

Celebrating a Year of Excellence: EDM Council’s Data Excellence Program

The EDM Council’s Data Excellence Program has reached a significant milestone: its first anniversary. The program is proving to be a game-changer in the data management landscape for promoting commitment to best practices and data excellence at the organizational level. Designed to recognize and support organizations dedicated to elevating their data management capabilities, the program […]

Empowering Data Stewards: Building a Forum That Drives Value

Data steward forums are catalysts for organizational data wisdom and cultural transformation. When executed thoughtfully, they become your strongest asset in building a data-driven organization. However, their success hangs delicately on implementation — the difference between fostering lasting engagement and watching enthusiasm fade lies in the fundamental framework you establish from day one. 1. Building […]

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Author: Subasini Periyakaruppan

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Feb 5 2025

The State of Data Governance

In 2024, our research at Dresner Advisory Services revealed that only 32% of organizations have a formal data governance organization in place. This statistic highlights a critical gap, especially as machine learning (ML) and artificial intelligence (AI) are increasingly integrated into operations, expanding business reliance on data and analytic content. Despite the growing importance of […]

Data Speaks for Itself: Data Quality Management in the Age of Language Models

Unsurprisingly, my last two columns discussed artificial intelligence (AI), specifically the impact of language models (LMs) on data curation. My August 2024 column, “The Shift from Syntactic to Semantic Data Curation and What It Means for Data Quality,” and my November 2024 column, “Data Validation, the Data Accuracy Imposter or Assistant?” addressed some of the […]

Empowering Organizations Through Data Literacy, Governance, and Business Literacy

In my journey as a data management professional, I’ve come to believe that the road to becoming a truly data-centric organization is paved with more than just tools and policies — it’s about creating a culture where data literacy and business literacy thrive. Data governance, long regarded as a compliance-driven function, is now the backbone […]

Identifying and Addressing Data Overload

Increased data generation requires modern businesses to manage vast volumes of information. All this data holds immense potential for insights and informed decision-making, but its value depends on effective utilization. Without the right tools, frameworks, and strategies, even established companies risk being overwhelmed by data overload. Let’s take a closer look at data overload and […]

Data Professional Introspective: Your Organization Can’t Create an EDM Strategy

Some countries successfully create long-term strategic plans. For example, China’s first 100-year plan was aimed at the elimination of extreme poverty by 2020. In 1980, there were 540M people living in extreme poverty; by 2014, there were only 80 million. The second 100-year plan, targeted for 2050, calls for achieving 30% of global GDP, to […]

The 7 Fundamentals That Are Crucial for CDO Success in 2025

As data volumes continue to rapidly grow and organizations become increasingly data driven in the AI age, the data landscape of 2025 is poised to be more dynamic and complex than ever before.

For businesses to excel in this fast-evolving environment, chief data officers (CDOs) of the future must move beyond their traditional roles to become strategic transformation leaders. Key priorities will shape their agenda and be a driving force for success in an era of sweeping change.

The eBook “Seven Chief Data Officer (CDO) Priorities for 2025,” explores seven key priorities that will define successful data leadership in 2025. From crafting unified data strategies that feel less like governance manifestos and more like business transformation blueprints, to preparing trusted data for the AI revolution, you will learn:

What tomorrow’s successful CDOs look like.
The seven fundamentals that are crucial for CDO success.
Practical strategies for data management in 2025.

Expanding from Data Custodian to Strategic Visionary

The role of the CDO has undergone a significant change over the last few years—and it’s continuing to be redefined as CDOs prove their value. CDOs are now unlocking competitive advantages by implementing and optimizing comprehensive data initiatives. That’s part of the reason why organizations with a dedicated CDO are better equipped to handle the complexities of modern data ecosystems and maintain a competitive edge than those without this role.

As noted in our eBook “Seven Chief Data Officer (CDO) Priorities for 2025,” this critical position will become even more strategic. The role will highlight a distinct difference between good companies that use data and great companies that rely on data to drive every business decision, accelerate growth, and confidently embrace whatever is next.

The idea for this eBook began with a simple observation: The role of CDO has become a sort of organizational Rorschach test. Ask 10 executives what a CDO should do, and you’ll get 11 different answers, three strategic frameworks, and at least one person insisting it’s all about AI (it’s not).

While researching this piece, a fascinating pattern emerged. Data strategy isn’t just about governance and quality metrics, but about fundamental business transformation. But perhaps most intriguing is the transformation of the CDO role itself. What started as a data custodian and governance guru has morphed into something far more nuanced: Part strategist, part innovator, part ethicist, and increasingly, part business transformer.

The eBook dives deeper into these themes, offering insights and frameworks for navigating this evolution. But more than that, it attempts to capture this moment of transformation–where data leadership is becoming something new and, potentially, revolutionary.

The seven priorities outlined in the eBook aren’t just predictions; they’re emerging patterns. When McKinsey tells us that 72% of organizations struggle with managing data for AI use cases, they’re really telling us something profound about the gap between our technological ambitions and our organizational readiness. We’re all trying to build the plane while flying it–and some of us are still debating whether we need wings.

This eBook is for leaders who find themselves at this fascinating intersection of technology, strategy, and organizational change. Whether you’re a CDO looking to validate your roadmap, or an executive trying to understand why your data initiatives feel like pushing boulders uphill, we hope you’ll find something here that makes you think differently about the journey ahead.

Download the eBook if you’re curious about what data leadership looks like when we stop treating it like a technical function and start seeing it as a strategic imperative.

The post The 7 Fundamentals That Are Crucial for CDO Success in 2025 appeared first on Actian.

The Data-Centric Revolution: Putting Knowledge Into Our Knowledge Graphs

I recently gave a presentation called “Knowledge Management and Knowledge Graphs” at a KMWorld conference, and a new picture of the relationship between knowledge management and knowledge graphs gradually came into focus. I recognized that the knowledge graph community has gotten quite good at organizing and harmonizing data and information, but there is little knowledge […]

Accelerating Innovation: Data Discovery in Manufacturing

The manufacturing industry is in the midst of a digital revolution. You’ve probably heard these buzzwords: Industry 4.0, IoT, AI, and machine learning– all terms that promise to revolutionize everything from assembly lines to customer service. Embracing this digital transformation is key in improving your competitive advantage, but new technology doesn’t come without its own challenges. Each new piece of technology needs one thing to deliver innovation: data.

Data is the fuel powering your tech engines. Without the ability to understand where your data is, whether it’s trustworthy, or who owns the datasets, even the most powerful tools can overcomplicate and confuse the best data teams. That’s where modern data discovery solutions come in. They’re like the backstage crew making sure everything runs smoothly– connecting systems, tidying up the data mess, and making sure everyone has exactly what they need, when they need it. That means faster insights, streamlined operations, and a lower total cost of ownership (TCO). In other words, data access is the key to staying ahead in today’s fast-paced, highly competitive, increasingly sensitive manufacturing market.

The Problem

Data from all aspects of your business is siloed– whether it’s coming from sensors, legacy systems, cloud applications, suppliers or customers– trying to piece it all together is daunting, time-consuming, and just plain hard. Traditional methods are slow, cumbersome, and definitely not built for today’s needs. This fragmented approach not only slows down decision-making, but keeps you from tapping into valuable insights that could drive innovation. And in a market where speed is everything, that’s a recipe for falling behind.

So the big question is: how can you unlock the true potential of your data?

The Solution

So how do you make data intelligence into a streamlined, efficient process? The answer lies in modern data discovery solutions– the unsung catalyst of a digital transformation motion. Rather than simply integrating data sources, data discovery solutions excel in metadata management, offering complete visibility into your company’s data ecosystem. They enable users– regardless of skill level– to locate where data resides and assess the quality and relevance of the information. By providing this detailed understanding of data context and lineage, organizations can confidently leverage accurate, trustworthy datasets, paving the way for informed decision-making and innovation,

Key Components

Easy-to-Connect Data Sources for Metadata Management

One of the biggest hurdles in data integration is connecting to a variety of data sources, including legacy systems, cloud applications, and IoT devices. Modern data discovery tools like Zeenea offer easy connectivity, allowing you to extract metadata from various sources seamlessly. This unified view eliminates silos and enables faster, more informed decision-making across the organization.

Advanced Metadata Management

Metadata is the backbone of effective data discovery. Advanced metadata management capabilities ensure that data is well-organized, tagged, and easily searchable. This provides a clear context for data assets, helping you understand the origin, quality, and relevance of your data. This means better data search and discoverability.

Data Discovery Knowledge Graph

A data discovery knowledge graph serves as an intelligent map of your metadata, illustrating the intricate relationship and connections between data assets. It provides users with a comprehensive view of how data points are linked across systems, offering a clear picture of data lineage– from origin to current state. The visibility into the data journey is invaluable in manufacturing, where understanding the flow of information between production data, supply chain metrics, and customer feedback is critical. By tracing the lineage of data, you can quickly assess its accuracy, relevance, and context, leading to more precise insights and informed decision-making.

Quick Access to Quality Data Through Data Marketplace

A data marketplace provides a centralized hub where you can easily search, discover, and access high-quality data. This self-service model empowers your teams to find the information they need without relying on IT, accelerating time to insight. The result? Faster product development cycles, improved process efficiency, and enhanced decision-making capabilities.

User-Friendly Interface With Natural Language Search

Modern data discovery platforms prioritize user experience with intuitive, user-friendly interfaces. Features like natural language search allow users to query data using everyday language, making it easier for non-technical users to find what they need. This democratizes access to data across the organization, fostering a culture of data-driven decision-making.

Low Total Cost of Ownership (TCO)

Traditional metadata management solutions often come with a hefty price tag due to high infrastructure costs and ongoing maintenance. In contrast, modern data discovery tools are designed to minimize TCO with automated features, cloud-based deployment, and reduced need for manual intervention. This means more efficient operations and a greater return on investment.

Benefits

By leveraging a comprehensive data discovery solution, manufacturers can achieve several key benefits:

Enhanced Innovation

With quick access to quality data, teams can identify trends and insights that drive product development and process optimization.

Faster Time to Market

Automated implementation and seamless data connectivity reduce the time required to gather and analyze data, enabling faster decision-making.

Improved Operational Efficiency

Advanced metadata management and knowledge graphs help streamline data governance, ensuring that users have access to reliable, high-quality data.

Increased Competitiveness

A user-friendly data marketplace democratizes data access, empowering teams to make data-driven decisions and stay ahead of industry trends.

Cost Savings

With low TCO and reduced dependency on manual processes, manufacturers can maximize their resources and allocate budgets towards strategic initiatives.

Data is more than just a resource—it’s a catalyst for innovation. By embracing advanced metadata management and data discovery solutions, you can find, trust, and access data. This not only accelerates time to market but also drives operational efficiency and boosts competitiveness. With powerful features like API-led automation, a data discovery knowledge graph, and an intuitive data marketplace, you’ll be well-equipped to navigate the challenges of Industry 4.0 and beyond.

Call to Action

Ready to accelerate your innovation journey? Explore how Actian Zeenea can transform your manufacturing processes and give you a competitive edge.

Learn more about how our advanced data discovery solutions can help you unlock the full potential of your data. Sign up for a live product demo and Q&A.

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Mind the Gap: Architecting Santa’s List – The Naughty-Nice Database

You never know what’s going to happen when you click on a LinkedIn job posting button. I’m always on the lookout for interesting and impactful projects, and one in particular caught my attention: “Far North Enterprises, a global fabrication and distribution establishment, is looking to modernize a very old data environment.” I clicked the button […]

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