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Aug 26 2025

Optimizing retail operations through a practical data strategy

Given the pace of change in the retail sector, impactful decisions can be a competitive advantage, but many organizations are still in the dark. They’re not operating with actionable insights… trusting their gut to make decisions while keeping data in a silo. The solution? An all-inclusive data strategy that makes sense for the organization. This article […]

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How an Internal AI Governance Council Drives Responsible Innovation

AI has rapidly evolved from a futuristic concept to a foundational technology, deeply embedded in the fabric of contemporary organizational processes across industries. Companies leverage AI to enhance efficiency, personalize customer interactions, and drive operational innovation. However, as AI permeates deeper into organizational structures, it brings substantial risks related to data privacy, intellectual property, compliance […]

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The Data Danger of Agentic AI

Agentic AI represents a significant evolution beyond traditional rule-based AI systems and generative AI, offering unprecedented autonomy and transformative potential across various sectors. These sophisticated systems can plan, decide, and act independently, promising remarkable advances in efficiency and decision-making. However, this high degree of autonomy, when combined with poorly governed or flawed data, can lead […]

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How to Future-Proof Your Data and AI Strategy

With AI systems reshaping enterprises and regulatory frameworks continuously evolving, organizations face a critical challenge: designing AI governance that protects business value without stifling innovation. But how do you future-proof your enterprise for a technology that is evolving at such an incredible pace? The answer lies in building robust data foundations that can adapt to whatever comes […]

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Data Governance and CSR: Evolving Together

In a world where every claim your organization makes — about sustainability, equity, or social impact — is scrutinized by regulators, investors, and the public, one truth stands out: Your data has never mattered more. Corporate Social Responsibility (CSR) isn’t just about good intentions — it is about trustworthy, transparent data that stands up to […]

Why Data Governance Still Matters in the Age of AI

At a recent conference, I witnessed something that’s become far too common in data leadership circles: genuine surprise that chief data officers consistently cite culture — not technology — as their greatest challenge. Despite a decade of research and experience pointing to the same root cause, conversations still tend to focus on tools rather than […]

Why Federated Knowledge Graphs are the Missing Link in Your AI Strategy

A recent McKinsey report titled “Superagency in the workplace: Empowering people to unlock AI’s full potential ” notes that “Over the next three years, 92 percent of companies plan to increase their AI investments”. They go on to say that companies need to think strategically about how they incorporate AI. Two areas that are highlighted are “federated governance models” and “human centricity.” Where teams can create and understand AI models that work for them, while having a centralized framework to monitor and manage these models. This is where the federated knowledge graph comes into play.

For data and IT leaders architecting modern enterprise platforms, the federated knowledge graph is a powerful architecture and design pattern for data management, providing semantic integration across distributed data ecosystems. When implemented with the Actian Data Intelligence Platform, a federated knowledge graph becomes the foundation for context-aware automation, bridging your data mesh or data fabric with scalable and explainable AI.

Knowledge Graph vs. Federated Knowledge Graph

A knowledge graph represents data as a network of entities (nodes) and relationships (edges), enriched with semantics (ontologies, taxonomies, metadata). Rather than organizing data by rows and columns, it models how concepts relate to one another.

An example being, “Customer X purchased Product Y from Store Z on Date D.”

A federated knowledge graph goes one step further. It connects disparate, distributed datasets across your organization into a virtual semantic graph without moving the underlying data from the systems.

In other words:

You don’t need a centralized data lake.
You don’t need to harmonize all schemas up front.
You build a logical layer that connects data using shared meaning.

This enables both humans and machines to navigate the graph to answer questions, infer new knowledge, or automate actions, all based on context that spans multiple systems.

Real-World Example of a Federated Knowledge Graph in Action

Your customer data lives in a cloud-based CRM, order data in SAP, and web analytics in a cloud data warehouse. Traditionally, you’d need a complex extract, transform, and load (ETL) pipeline to join these datasets.

With a federated knowledge graph:

“Customer,” “user,” and “client” can be resolved as one unified entity.
The relationships between their behaviors, purchases, and support tickets are modeled as edges.
More importantly, AI can reason with questions like “Which high-value customers have experienced support friction that correlates with lower engagement?”

This kind of insight is what drives intelligent automation.

Why Federated Knowledge Graphs Matter

Knowledge graphs are currently utilized in various applications, particularly in recommendation engines. However, the federated approach addresses cross-domain integration, which is especially important in large enterprises.

Federation in this context means:

Data stays under local control (critical for a data mesh structure).
Ownership and governance remain decentralized.
Real-time access is possible without duplication.
Semantics are shared globally, enabling AI systems to function across domains.

This makes federated knowledge graphs especially useful in environments where data is distributed by design–across departments, cloud platforms, and business units.

How Federated Knowledge Graphs Support AI Automation

AI automation relies not only on data, but also on understanding. A federated knowledge graph provides that understanding in several ways:

Semantic Unification: Resolves inconsistencies in naming, structure, and meaning across datasets.
Inference and Reasoning: AI models can use graph traversal and ontologies to derive new insights.
Explainability: Federated knowledge graphs store the paths behind AI decisions, allowing for greater transparency and understanding. This is critical for compliance and trust.

For data engineers and IT teams, this means less time spent maintaining pipelines and more time enabling intelligent applications.

Complementing Data Mesh and Data Fabric

Federated knowledge graphs are not just an addition to your modern data architecture; they amplify its capabilities. For instance:

In a data mesh architecture, domains retain control of their data products, but semantics can become fragmented. Federated knowledge graphs provide a global semantic layer that ensures consistent meaning across those domains, without imposing centralized ownership.
In a data fabric design approach, the focus is on automated data integration, discovery, and governance. Federated knowledge graphs serve as the reasoning layer on top of the fabric, enabling AI systems to interpret relationships, not just access raw data.

Not only do they complement each other in a complex architectural setup, but when powered by a federated knowledge graph, they enable a scalable, intelligent data ecosystem.

A Smarter Foundation for AI

For technical leaders, AI automation is about giving models the context to reason and act effectively. A federated knowledge graph provides the scalable, semantic foundation that AI needs, and the Actian Data Intelligence Platform makes it a reality.

The Actian Data Intelligence Platform is built on a federated knowledge graph, transforming your fragmented data landscape into a connected, AI-ready knowledge layer, delivering an accessible implementation on-ramp through:

Data Access Without Data Movement: You can connect to distributed data sources (cloud, on-prem, hybrid) without moving or duplicating data, enabling semantic integration.
Metadata Management: You can apply business metadata and domain ontologies to unify entity definitions and relationships across silos, creating a shared semantic layer for AI models.
Governance and Lineage: You can track the origin, transformations, and usage of data across your pipeline, supporting explainable AI and regulatory compliance.
Reusability: You can accelerate deployment with reusable data models and power multiple applications (such as customer 360 and predictive maintenance) using the same federated knowledge layer.

Get Started With Actian Data Intelligence

Take a product tour today to experience data intelligence powered by a federated knowledge graph.

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External Data Strategy: Governance, Implementation, and Success (Part 2)

In Part 1 of this series, we established the strategic foundation for external data success: defining your organizational direction, determining specific data requirements, and selecting the right data providers. We also introduced the critical concept of external data stewardship — identifying key stakeholders who bridge the gap between business requirements and technical implementation. This second part […]

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Author: Subasini Periyakaruppan

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Jul 16 2025

A Leadership Blueprint for Driving Trusted, AI-Ready Data Ecosystems

As AI adoption accelerates across industries, the competitive edge no longer lies in building better models; it lies in governing data more effectively. Enterprises are realizing that the success of their AI and analytics ambitions hinges not on tools or algorithms, but on the quality, trustworthiness, and accountability of the data that fuels them. Yet, […]

All in the Data: Where Good Data Comes From

Let’s start with a truth that too many people still overlook — not all data is good data. Just because something is sitting in a database or spreadsheet doesn’t mean it’s accurate, trustworthy, or useful. In the age of AI and advanced analytics, we’ve somehow convinced ourselves that data — any data — can be […]

Future-Proofing AI Under a Federal Umbrella: What a 10-Year State Regulation Freeze Means

The federal government’s proposal to impose a 10-year freeze on state-level AI regulation isn’t happening in a vacuum but in direct response to California. The state’s AI Accountability Act (SB 1047) has been making waves for its ambition to hold developers of powerful AI models accountable through mandatory safety testing, public disclosures, and the creation of a new regulatory […]

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External Data Strategy: From Vision to Vendor Selection (Part 1)

In today’s data-driven business environment, the ability to leverage external information sources has become a critical differentiator between market leaders and laggards. Organizations that successfully harness external data don’t just gather more information – they transform how they understand their customers, anticipate market shifts, and identify growth opportunities. However, the path from recognizing the need for […]

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Author: Subasini Periyakaruppan

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ACTIAN data governance data intelligence Data Management data observability Product Launches

Jun 24 2025

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.

Tackling Complex Data Governance Challenges in the Banking Industry

The banking industry is one of the most heavily regulated sectors, and as financial services evolve, the challenges of managing, governing, and ensuring compliance with vast amounts of information have grown exponentially. With the introduction of stringent regulations, increasing data privacy concerns, and growing customer expectations for seamless service, banks face complex data governance challenges. These challenges include managing large volumes of sensitive data, maintaining data integrity, ensuring compliance with regulatory frameworks, and improving data transparency for both internal and external stakeholders.

In this article, we explore the core data governance challenges faced by the banking industry and how the Actian Data Intelligence Platform helps banking organizations navigate these challenges. From ensuring compliance with financial regulations to improving data transparency and integrity, the platform offers a comprehensive solution to help banks unlock the true value of their data while maintaining robust governance practices.

The Data Governance Landscape in Banking

The financial services sector generates and manages massive volumes of data daily, spanning customer accounts, transactions, risk assessments, compliance checks, and much more. Managing this data effectively and securely is vital to ensure the smooth operation of financial institutions and to meet regulatory and compliance requirements. Financial institutions must implement robust data governance to ensure data quality, security, integrity, and transparency.

At the same time, banks must balance regulatory requirements, operational efficiency, and customer satisfaction. This requires implementing systems that can handle increasing amounts of data while maintaining compliance with local and international regulations, such as GDPR, CCPA, Basel III, and MiFID II.

Key Data Governance Challenges in the Banking Industry

Below are some common hurdles and challenges facing organizations in the banking industry.

Data Privacy and Protection

With the rise of data breaches and increasing concerns about consumer privacy, banks are under immense pressure to safeguard sensitive customer information. Regulations such as the General Data Protection Regulation (GDPR) in the EU and the California Consumer Privacy Act (CCPA) have made data protection a top priority for financial institutions. Ensuring that data is appropriately stored, accessed, and shared is vital for compliance, but it’s also vital for maintaining public trust.

Regulatory Compliance

Banks operate in a highly regulated environment, where compliance with numerous financial regulations is mandatory. Financial regulations are continuously evolving, and keeping up with changes in the law requires financial institutions to adopt efficient data governance practices that allow them to demonstrate compliance.

For example, Basel III outlines requirements for the management of banking risk and capital adequacy, while MiFID II requires detailed reporting on market activities and transaction records. In this landscape, managing compliance through data governance is no small feat.

Data Silos and Fragmentation

Many financial institutions operate in a fragmented environment, where data is stored across multiple systems, databases, and departments. This lack of integration can make it difficult to access and track data effectively. For banks, this fragmentation into data silos complicates the management of data governance processes, especially when it comes to ensuring data accuracy, consistency, and completeness.

Data Transparency and Integrity

Ensuring the integrity and transparency of data is a major concern in the banking industry. Banks need to be able to trace the origins of data, understand how it’s been used and modified, and provide visibility into its lifecycle. This is particularly important for audits, regulatory reporting, and risk management processes.

Operational Efficiency

As financial institutions grow and manage increasing amounts of data, operational efficiency in data management becomes increasingly challenging. Ensuring compliance with regulations, conducting audits, and reporting on data use can quickly become burdensome without the right data governance tools in place. Manual processes are prone to errors and inefficiencies, which can have costly consequences for banks.

How the Actian Data Intelligence Platform Tackles Data Governance Challenges in the Banking Industry

The Actian Data Intelligence Platform is designed to help organizations tackle the most complex data governance challenges. With its comprehensive set of tools, The platform supports banks by helping ensure compliance with regulatory requirements, improving data transparency and integrity, and creating a more efficient and organized data governance strategy.

Here’s how the Actian Data Intelligence Platform helps the banking industry overcome its data governance challenges.

1. Ensuring Compliance With Financial Regulations

The Actian Data Intelligence Platform helps banks achieve regulatory compliance by automating compliance monitoring, data classification, and metadata management.

Regulatory Compliance Automation: The Actian Data Intelligence Platform enables banks to automate compliance tracking and continuously monitor data access and usage. This helps banks ensure that they are consistently meeting the requirements of regulatory frameworks like GDPR, Basel III, MiFID II, and others. The Actian Data Intelligence Platform’s compliance monitoring tools also automatically flag any data access or changes to data that may violate compliance rules, giving banks the ability to react quickly and mitigate risks.
Data Classification for Compliance: The Actian Data Intelligence Platform allows banks to classify and categorize data based on its sensitivity and compliance requirements. By tagging data with relevant metadata, such as classification labels (e.g., personal data, sensitive data, financial data), The Actian Data Intelligence Platform ensures that sensitive information is handled in accordance with regulatory standards.
Audit Trails and Reporting: The Actian Data Intelligence Platform’s audit trail functionality creates comprehensive logs of data access, usage, and modifications. These logs are crucial for financial institutions when preparing for audits or responding to regulatory inquiries. The Actian Data Intelligence Platform automates the creation of compliance reports, making it easier for banks to demonstrate their adherence to regulatory standards.

2. Improving Data Transparency and Integrity

Data transparency and integrity are critical for financial institutions, particularly when it comes to meeting regulatory requirements for reporting and audit purposes. The Actian Data Intelligence Platform offers tools that ensure data is accurately tracked and fully transparent, which helps improve data governance practices within the bank.

Data Lineage: The Actian Data Intelligence Platform’s data lineage functionality provides a visual map of how data flows through the bank’s systems, helping stakeholders understand where the data originated, how it has been transformed, and where it is currently stored. This is essential for transparency, especially when it comes to auditing and compliance reporting.
Metadata Management: The Actian Data Intelligence Platform’s metadata management capabilities enable banks to organize, track, and maintain metadata for all data assets across the organization. This not only improves transparency but also ensures that data is properly classified and described, reducing the risk of errors and inconsistencies. With clear metadata, banks can ensure that data is correctly used and maintained across systems.
Data Quality Monitoring: The Actian Data Intelligence Platform continuously monitors data quality, ensuring that data remains accurate, complete, and consistent across systems. Data integrity is crucial for banks, as decisions made based on poor-quality data can lead to financial losses, reputational damage, and non-compliance.

3. Eliminating Data Silos and Improving Data Integration

Fragmented data and siloed systems are a common challenge for financial institutions. Data often resides in disparate databases or platforms across different departments, making it difficult to access and track efficiently. The platform provides the tools to integrate data governance processes and eliminate silos.

Centralized Data Catalog: The Actian Data Intelligence Platform offers a centralized data catalog that enables banks to consolidate and organize all their data assets using a single platform. This centralized repository improves the discoverability of data across departments and systems, helping banks streamline data access and reduce inefficiencies.
Cross-Department Collaboration: With the Actian Data Intelligence Platform, departments across the organization can collaborate on data governance. By centralizing governance policies, data access, and metadata, The Actian Data Intelligence Platform encourages communication between data owners, stewards, and compliance officers to ensure that data governance practices are consistent across the institution.

4. Enhancing Operational Efficiency

Manual processes in data governance can be time-consuming and prone to errors, making it challenging for banks to keep pace with the growing volumes of data and increasing regulatory demands. The Actian Data Intelligence Platform’s platform automates and streamlines key aspects of data governance, allowing banks to work more efficiently and focus on higher-value tasks.

Automation of Compliance Monitoring: The Actian Data Intelligence Platform automates compliance checks, data audits, and reporting, which reduces the manual workload for compliance teams. Automated alerts and reports help banks quickly identify potential non-compliance issues and rectify them before they escalate.
Workflow Automation: The Actian Data Intelligence Platform enables banks to automate workflows around data governance processes, including data classification, metadata updates, and access management. By streamlining these workflows, The Actian Data Intelligence Platform ensures that banks can efficiently manage their data governance tasks without relying on manual intervention.
Data Access Control: The Actian Data Intelligence Platform helps banks define and enforce fine-grained access controls for sensitive data. With The Actian Data Intelligence Platform’s robust access control mechanisms, banks can ensure that only authorized personnel can access specific data, reducing the risk of data misuse and enhancing operational security.

The Actian Data Intelligence Platform and the Banking Industry: A Perfect Partnership

The banking industry faces a range of complex data governance challenges. To navigate these challenges, they need robust data governance frameworks and powerful tools to help manage their vast data assets.

The Actian Data Intelligence Platform offers a comprehensive data governance solution that helps financial institutions tackle these challenges head-on. By providing automated compliance monitoring, metadata tracking, data lineage, and a centralized data catalog, the platform ensures that banks can meet regulatory requirements while improving operational efficiency, data transparency, and data integrity.

Actian offers an online product tour of the Actian Data Intelligence Platform as well as personalized demos of how the data intelligence platform can transform and enhance financial institutions’ data strategies.

The post Tackling Complex Data Governance Challenges in the Banking Industry appeared first on Actian.

Data Is Risky Business: Sustainability and Resilience in Data Governance

This quarter’s column is co-authored with Anthony Mazzarella, a fellow practitioner-academic doing research into what makes data governance “tick.” There has been a lot of commentary on social media and elsewhere in recent months about how data governance has failed and how we need to reframe the discussion on what it means to govern data, […]

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 […]

Legal Issues for Data Professionals: Preventive Healthcare and Data

This column addresses the role of data in the field of healthcare known as “preventive healthcare.” Preventive healthcare is undergoing changes as data increases its scope and the role it plays in healthcare. What Is Preventive Healthcare and Its Data? For the purpose of this article, traditional healthcare refers to patient care received from a […]

From Silos to Self-Service: Data Governance in the AI Era

As enterprises double down on AI, many are discovering an uncomfortable truth: their biggest barrier isn’t technology—it’s their data governance model.

While 79% of corporate strategists rank AI and analytics as critical, Gartner predicts that 60% will fall short of their goals because their governance frameworks can’t keep up.

Siloed data, ad hoc quality practices, and reactive compliance efforts create bottlenecks that stifle innovation and limit effective data governance. The future demands a different approach: data treated as a product, governance embedded in data processes including self-service experiences, and decentralized teams empowered by active metadata and intelligent automation.

From Data Silos to Data Products: Why Change is Urgent

Traditional data governance frameworks were not designed for today’s reality. Enterprises operate across hundreds, sometimes thousands, of data sources: cloud warehouses, lakehouses, SaaS applications, on-prem systems, and AI models all coexist in sprawling ecosystems.

Without a modern approach to managing and governing data, silos proliferate. Governance becomes reactive—enforced after problems occur—rather than proactive. And AI initiatives stumble when teams are unable to find trusted, high-quality data at the speed the business demands.

Treating data as a product offers a way forward. Instead of managing data purely as a siloed, domain-specific asset, organizations shift toward delivering valuable and trustworthy data products to internal and external consumers. Each data product has an owner and clear expectations for quality, security, and compliance.

This approach connects governance directly to business outcomes—driving more accurate analytics, more precise AI models, and faster, more confident decision-making.

Enabling Domain-Driven Governance: Distributed, Not Fragmented

Achieving this future requires rethinking the traditional governance model. Centralized governance teams alone cannot keep pace with the volume, variety, and velocity of data creation. Nor can fully decentralized models, where each domain sets its own standards without alignment.

The answer is federated governance, a model in which responsibility is distributed to domain teams but coordinated through a shared framework of policies, standards, and controls.

In a federated model:

Domain teams own their data products, from documentation to quality assurance to access management.
Central governance bodies set enterprise-wide guardrails, monitor compliance, and enable collaboration across domains.
Data intelligence platforms serve as the connective tissue, providing visibility, automation, and context across the organization.

This balance of autonomy and alignment ensures that governance scales with the organization—without becoming a bottleneck to innovation.

The Rise of Active Metadata and Intelligent Automation

Active metadata is the fuel that powers modern governance. Unlike traditional data catalogs and metadata repositories, which are often static and siloed, active metadata is dynamic, continuously updated, and operationalized into business processes.

By tapping into active metadata, organizations can:

Automatically capture lineage, quality metrics, and usage patterns across diverse systems.
Enforce data contracts between producers and consumers to ensure shared expectations.
Enable intelligent access controls based on data sensitivity, user role, and regulatory requirements.
Proactively detect anomalies, schema changes, and policy violations before they cause downstream issues.

When governance processes are fueled by real-time, automated metadata, they no longer slow the business down—they accelerate it.

Embedding Governance into Everyday Work

The ultimate goal of modern governance is to make high-quality data products easily discoverable, understandable, and usable, without requiring users to navigate bureaucratic hurdles.

This means embedding governance into self-service experiences with:

Enterprise data marketplaces where users browse, request, and access data products with clear SLAs and usage guidelines.
Business glossaries that standardize and enforce consistent data definitions across domains.
Interactive lineage visualizations that trace data from its source through each transformation stage in the pipeline.
Automated data access workflows that enforce granular security controls while maintaining compliance.

In this model, governance becomes an enabler, not an obstacle, to data-driven work.

Observability: Enabling Ongoing Trust

Data observability is a vital component of data governance for AI because it ensures the quality, integrity, and transparency of the data that powers AI models. By integrating data observability, organizations reduce AI failure rates, accelerate time-to-insight, and maintain alignment between model behavior.

Data observability improves data intelligence and helps to:

Ensure high-quality data is used for AI model training by continuously monitoring data pipelines, quickly detecting anomalies, errors, or bias before they impact AI outputs.
Provide transparency and traceability of data flow and transformations, which is essential for building trust, ensuring regulatory compliance, and demonstrating accountability in AI systems.
Reduce model bias by monitoring data patterns and lineage; data observability helps identify and address potential biases in datasets and model outputs. This is key to ensuring AI systems are fair, ethical, and do not perpetuate discrimination.
Improve model explainability by making it easier to understand and explain AI model behavior, providing insights into the data and features that influence model predictions.

Building for the Future: Adaptability is Key

The pace of technological change—especially in AI, machine learning, and data infrastructure—shows no signs of slowing. Regulatory environments are also evolving rapidly, from GDPR to CCPA to emerging AI-specific legislation.

To stay ahead, organizations must build governance frameworks with data intelligence tools that are flexible by design:

Flexible metamodeling capabilities to customize governance models as business needs evolve.
Open architectures that connect seamlessly across new and legacy systems.
Scalable automation to handle growing data volumes without growing headcount.
Cross-functional collaboration between governance, engineering, security, and business teams.

By building adaptability into the core of their governance strategy, enterprises can future-proof their investments and support innovation for years to come.

Conclusion: Turning Governance into a Competitive Advantage

Data governance is no longer about meeting minimum compliance requirements—it’s about driving business value and building a data-driven culture. Organizations that treat data as a product, empower domains with ownership, and activate metadata across their ecosystems will set the pace for AI-driven innovation. Those that rely on outdated, centralized models will struggle with slow decision-making, mounting risks, and declining trust. The future will be led by enterprises that embed governance into the fabric of how data is created, shared, and consumed—turning trusted data into a true business advantage.

The post From Silos to Self-Service: Data Governance in the AI Era appeared first on Actian.

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.

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Mind the Gap: AI-Driven Data and Analytics Disruption

We are at the threshold of the most significant changes in information management, data governance, and analytics since the inventions of the relational database and SQL. Most advances over the past 30 years have been the result of Moore’s Law: faster processing, denser storage, and greater bandwidth. At the core, though, little has changed. The basic […]

The post Mind the Gap: AI-Driven Data and Analytics Disruption appeared first on DATAVERSITY.

Is the Scope of Data Governance Enough?

Data governance has long been the backbone of responsible data management, ensuring that organizations maintain high standards in data quality, security, and compliance. According to Jonathan Reichental in “Data Governance for Dummies,” the scope of governance extends well beyond data ownership and stewardship. It encompasses metadata, data architecture, master and reference data management, storage, integration, […]

Through the Looking Glass: On the Data (and Lack of Such) About Busking

I started playing cello in fourth grade, in the suburbs of Hartford, Conn. I can’t remember when I discovered that musicians played in the New York City streets and subways. But I recall that by high school, I had decided that would be the absolute coolest thing to do. Sometime later, I learned that this […]

Why Every Data-Driven Business Needs a Data Intelligence Platform

As data users can attest, success doesn’t come from having more data. It comes from having the right data. Yet for many organizations, finding this data can feel like trying to locate a specific book in a library without a catalog. You know the information is there, but without an organized way to locate it, you’re stuck guessing, hunting, or duplicating work. That’s where a data intelligence platform comes into play. This powerful but often underappreciated tool helps you organize, understand, and trust your data.

Whether you’re building AI applications, launching new analytics initiatives, or ensuring you meet compliance requirements, a well-implemented data intelligence platform can be the difference between success and frustration. That’s why they’ve become critical for modern businesses that want to ensure data products are easily searchable and available for all users.

What is a Data Intelligence Platform?

At its core, a data intelligence platform offers a centralized inventory of your organization’s data assets. Think of it as a searchable index that helps data consumers—like analysts, data scientists, business users, and engineers—discover, understand, and trust the data they’re working with.

A data intelligence platform goes far beyond simple documentation and is more than a list of datasets. It’s an intelligent, dynamic system that organizes, indexes, and contextualizes your data assets across the enterprise. For innovative companies that rely on data to drive decisions, power AI initiatives, and deliver trusted business outcomes, it’s quickly becoming indispensable.

With a modern data intelligence platform, you benefit from:

Federated knowledge graph. Gain better search results—as simple as shopping on an online e-commerce site—along with visualization of data relationships, and enhanced data exploration
Robust metadata harvesting automation. See your entire data landscape, reduce manual documentation efforts, ensure current metadata, and power data discovery.
Graph-based business glossary. Drive GenAI and other use cases with high-quality business context, ensure consistent terminology across your organization, accelerate insights, and enable semantic search capabilities.
Smart data lineage. Have visibility into where data comes from, how it changes, and where it goes. Up-to-date lineage enhances compliance and governance while improving root cause analysis of data quality issues.
Unified data catalog and marketplace. Use Google-like search capabilities to locate and access data for intuitive user experiences, while ensuring governance with permission-controlled data products.
Ready-to-use data products and contracts. Accelerate data democratization, support governance without compromising agility, create contracts only when relevant data products exist, and support a shift-left approach to data quality and governance.
Comprehensive data quality and observability. Reduce data quality incidents, experience faster issue resolution and remediation, increase your trust in data products, and benefit from proactive quality management instead of firefighting issues.
AI + knowledge graph. Leverage the powerful combination to manage metadata, improve data discovery, and fuel agentic AI.

The result is a single source of truth that supports data discovery, fosters trust in data, and promotes governance without slowing innovation. Simply stated, a data intelligence platform connects people to trusted data. In today’s business environment when data volume, variety, and velocity are all exploding, that connection is critical.

5 Reasons Data Intelligence Platforms Matter More Than Ever

Traditional approaches to data management are quickly becoming obsolete because they cannot keep pace with fast-growing data volumes and new sources. You need a smart, fast way to make data available and usable—without losing control. Here’s how data intelligence platforms help:

Eliminate data silos. One of the biggest challenges facing enterprises today is fragmentation. Data lives in multiple systems across cloud, on-premises, and hybrid environments. Without a data intelligence platform, it’s hard to know what data exists, let alone who owns it, how it’s being used, or whether it can be trusted.

A data intelligence platform creates a single view of all enterprise data assets. It breaks down silos and enables better collaboration between business and IT teams.

Accelerate analytics and AI. When analysts or data scientists spend more time finding, cleaning, or validating data than using it, productivity and innovation suffer. A data intelligence platform not only reduces time-to-insights but improves the quality of those insights by ensuring users start with accurate, trusted, connected data.

For AI initiatives, the value is even greater. Models are only as good as the data they’re trained on. Data intelligence platforms make it easier to identify high-quality, AI-ready data and track its lineage to ensure transparency and compliance.

Enable Governance Without Slowing Processes. Organizations must meet data privacy regulations like GDPR, HIPAA, and CCPA. A data intelligence platform can help teams understand where sensitive data resides, who has access to it, and how it flows across systems.

Unlike traditional governance methods, a data intelligence platform doesn’t create bottlenecks. It supports self-service access while enforcing data policies behind the scenes—balancing control and agility.

Drive Trust and Data Literacy. One of the most underrated benefits of a data intelligence platform is cultural. By making data more transparent, accessible, and understandable, data intelligence platforms empower all users across your business, not just data specialists.

Data intelligence platforms often include business glossaries and definitions, helping users interpret data correctly and leverage it confidently. That’s a huge step toward building a data-literate organization.

Empower Self-Service Analytics. A well-implemented data intelligence platform enables business users to search for and use data without waiting for IT or data teams to step in. This reduces delays and enables more people across the organization to make data-informed decisions.

When users can confidently find and understand the data they need, they’re more likely to contribute to data-driven initiatives. This democratization of data boosts agility and fosters a culture of innovation where teams across departments can respond faster to market changes, customer needs, and operational challenges. A data intelligence platform turns data from a bottleneck into a catalyst for smarter, faster decisions.

Real-World Data Intelligence Platform Use Cases

Here are a few ways organizations are using data intelligence platforms:

A healthcare provider tracks patient data across systems and ensures compliance with health data privacy laws. Metadata tagging helps the compliance team identify where sensitive information lives and how it’s accessed.
A retail company accelerates analytics for marketing campaigns. Data analysts can quickly find the most up-to-date product, pricing, and customer data, without waiting for IT support.
A financial services firm relies on data lineage features in its data intelligence platform to trace the origin of critical reports. This audit trail helps the firm maintain regulatory compliance and improves internal confidence in reporting.
In manufacturing, engineers and analysts explore equipment data, maintenance logs, and quality metrics across systems to identify patterns that can reduce downtime and improve efficiency.

As more organizations embrace hybrid and multi-cloud architectures, data intelligence platforms are becoming part of an essential infrastructure for trusted, scalable data operations.

Optimize a Data Intelligence Platform

Implementing and fully leveraging a data intelligence platform isn’t just about buying the right technology. It requires the right strategy, governance, and user engagement. These tips can help you get started:

Define your goals and scope. Determine if you want to support self-service analytics, improve governance, prepare for AI initiatives, or undertake other use cases.
Start small, then scale. Focus on high-impact use cases first to build momentum and show value early, then scale your success.
Engage both business and technical users. A data intelligence platform is more than an IT tool and should be usable and provide value to business teams, too.
Automate metadata collection. Manual processes will not scale. Look for a data intelligence platform that can automatically keep metadata up to date.
Focus on data quality and observability. A platform is only as good as the data it manages. Integrate quality checks and data lineage tools to make sure users can trust what they find.

In a data-driven business, having data isn’t enough. You need to find it, trust it, and use it quickly and confidently. A modern data intelligence platform makes this possible.

Actian’s eBook “10 Traps to Avoid for a Successful Data Catalog Project” is a great resource to implement and fully optimize a modern solution. It provides practical guidance to help you avoid common pitfalls, like unclear ownership, low adoption rates for users, or underestimating data complexity, so your project delivers maximum value.

The post Why Every Data-Driven Business Needs a Data Intelligence Platform appeared first on Actian.

Unlocking Unstructured Data: Fueling AI with Insights

IDC reports that around 90% of the data in the digital world is unstructured. This encompasses data like PDFs, PowerPoints, emails and images, all containing valuable information that traditional structured databases can’t gather. As artificial intelligence (AI) becomes more widespread, the importance of unstructured data grows. Businesses now face the challenge of organizing and utilizing these diverse data sources so AI models can fully leverage their potential, which is much easier said than done. […]

The post Unlocking Unstructured Data: Fueling AI with Insights appeared first on DATAVERSITY.

Beyond Visibility: How Actian Data Observability Redefines the Standard

In today’s data-driven world, ensuring data quality, reliability, and trust has become a mission-critical priority. But as enterprises scale, many observability tools fall short, introducing blind spots, spiking cloud costs, or compromising compliance.

Actian Data Observability changes the game.

This blog explores how Actian’s next-generation observability capabilities outperform our competitors, offering unmatched scalability, cost-efficiency, and precision for modern enterprises.

Why Data Observability Matters Now More Than Ever

Data observability enables organizations to:

Detect data issues before they impact dashboards or models.
Build trust in analytics, AI, and regulatory reporting.
Maintain pipeline SLAs in complex architectures.
Reduce operational risk, rework, and compliance exposure.

Yet most tools still trade off depth for speed or precision for price. Actian takes a fundamentally different approach, offering full coverage without compromise.

What Actian Data Observability Provides

Actian Data Observability delivers on four pillars of enterprise value:

1. Achieve Proactive Data Reliability

Actian shifts data teams from reactive firefighting to proactive assurance. Through continuous monitoring, intelligent anomaly detection, and automated diagnostics, the solution enables teams to catch and often resolve data issues before they reach downstream systems—driving data trust at every stage of the pipeline.

2. Gain Predictable Cloud Economics

Unlike tools that cause unpredictable cost spikes from repeated scans and data movement, Actian’s zero-copy, workload-isolated architecture ensures stable, efficient operation. Customers benefit from low total cost of ownership without compromising coverage or performance.

3. Boost Data Team Productivity and Efficiency

Actian empowers data engineers and architects to “shift left”—identifying issues early in the pipeline and automating tedious tasks like validation, reconciliation, and monitoring. This significantly frees up technical teams to focus on value-added activities, from schema evolution to data product development.

4. Scale Confidently With Architectural Freedom

Built for modern, composable data stacks, Actian Data Observability integrates seamlessly with cloud data warehouses, lakehouses, and open table formats. Its decoupled architecture scales effortlessly—handling thousands of data quality checks in parallel without performance degradation. With native Apache Iceberg support, it’s purpose-built for next-gen data platforms.

Actian Data Observability: What Sets it Apart

Actian Data Observability stands apart from its competitors in several critical dimensions. Most notably, Actian is the only platform that guarantees 100% data coverage without sampling, whereas tools from other vendors often rely on partial or sampled datasets, increasing the risk of undetected data issues. Additional vendors, while offering tools strong in governance, do not focus on observability and lacks this capability entirely.

In terms of cost control, Actian Data Observability uniquely offers a “no cloud cost surge” guarantee. Its architecture ensures compute efficiency and predictable cloud billing, unlike some vendors which can trigger high scan fees and unpredictable cost overruns. Smaller vendors’ pricing models are still maturing and may not be transparent at scale.

Security and governance are also core strengths for Actian. Its secured zero-copy architecture enables checks to run in-place—eliminating the need for risky or costly data movement. In contrast, other vendors typically require data duplication or ingestion into their own environments. Others offer partial support here, but often with tradeoffs in performance or integration complexity.

When it comes to scaling AI/ML workloads for observability, Actian’s models are designed for high-efficiency enterprise use, requiring less infrastructure and tuning. Some other models, while powerful, can be compute-intensive. Others offer moderate scalability, and have limited native ML support in this context.

A standout differentiator is Actian’s native support for Apache Iceberg—a first among observability platforms. While others are beginning to explore Iceberg compatibility, Actian’s deep, optimized integration provides immediate value for organizations adopting or standardizing on Iceberg. Many other vendors currently offer no meaningful support here.

Finally, Actian Data Observability’s decoupled data quality engine enables checks to scale independently of production pipelines—preserving performance while ensuring robust coverage. This is a clear edge over some other solutions, who tightly couple checks with pipeline workflows.

Why Modern Observability Capabilities Matter

Most observability tools were built for a different era—before Iceberg, before multi-cloud, and before ML-heavy data environments. As the stakes rise, the bar for observability must rise too.

Actian meets that bar. And then exceeds it.

With full data coverage, native modern format support, and intelligent scaling—all while minimizing risk and cost—Actian Data Observability is not just a tool. It’s the foundation for data trust at scale.

Final Thoughts

If you’re evaluating data observability tools and need:

Enterprise-grade scalability.
Modern format compatibility (Iceberg, Parquet, Delta).
ML-driven insights without resource drag.
Secure, in-place checks.
Budget-predictable deployment.

Then Actian Data Observability deserves a serious look.

Learn more about how we can help you build trusted data pipelines—at scale, with confidence.

The post Beyond Visibility: How Actian Data Observability Redefines the Standard appeared first on Actian.

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