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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 […]


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Author: Robert S. Seiner

Tending the Unicorn Farm: A Business Case for Quantum Computing
Welcome to the whimsical wide world of unicorn farming. Talking about quantum computing is a bit like tending to your unicorn farm, in that a lossless chip (at the time of writing) does not exist. So, largely, the realm of quantum computing is just slightly faster than normal compute power. The true parallel nature of […]


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Author: Mark Horseman

The Five Levels Essential to Scaling Your Data Strategy
Scaling your data strategy will inevitably result in winners and losers. Some work out the system to apply in their organization and skillfully tailor it to meet the demands and context of their organization, and some don’t or can’t. It’s something of a game.  But how can you position yourself as a winner? Read on […]


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Author: Jason Foster

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 […]


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Author: Christine Haskell

Data Speaks for Itself: Is Your Data Quality Management Practice Ready for AI?
While everyone is asking if their data is ready for AI, I want to ask a somewhat different question: Is your data quality management (DQM) program ready for AI?  In my opinion, you need to be able to answer yes to the following four questions before you can have any assurance you are ready to […]


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Author: Dr. John Talburt

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 […]


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Author: The MITRE Corporation

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. 

The post Why Federated Knowledge Graphs are the Missing Link in Your AI Strategy appeared first on Actian.


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Author: Actian Corporation

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 […]

The post External Data Strategy: Governance, Implementation, and Success (Part 2) appeared first on DATAVERSITY.


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Author: Subasini Periyakaruppan

Understanding Data Pipelines: Why They Matter, and How to Build Them
Building effective data pipelines is critical for organizations seeking to transform raw research data into actionable insights. Businesses rely on seamless, efficient, scalable pipelines for proper data collection, processing, and analysis. Without a well-designed data pipeline, there’s no assurance that the accuracy and timeliness of data will be available to empower decision-making.   Companies face several […]


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Author: Ramalakshmi Murugan

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, […]


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Author: Gopi Maren

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 […]


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Author: Robert S. Seiner

The Book Look: Rewiring Your Mind for AI
I collect baseball and non-sport cards. I started collecting when I was a kid, stopped for about 40 years, and returned to collecting again, maybe as part of a mid-life crisis. I don’t have the patience today though, that I had when I was 12. For example, yesterday I wanted to find out the most […]


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Author: Steve Hoberman

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 […]

The post Future-Proofing AI Under a Federal Umbrella: What a 10-Year State Regulation Freeze Means appeared first on DATAVERSITY.


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Author: Dev Nag

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 […]

The post External Data Strategy: From Vision to Vendor Selection (Part 1) appeared first on DATAVERSITY.


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Author: Subasini Periyakaruppan

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

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


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Author: Dee Radh

Improving Data Quality Using AI and ML


In our fast-paced, interconnected digital world, data is truly the heartbeat of how organizations make decisions. However, the rapid explosion of data in terms of volume, speed, and diversity has brought about significant challenges in keeping that data reliable and high-quality. Relying on traditional manual methods for data governance just doesn’t cut it anymore; in […]

The post Improving Data Quality Using AI and ML appeared first on DATAVERSITY.


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Author: Udaya Veeramreddygari

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.


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Author: Actian Corporation

Rethinking Data-Driven Leadership
After reading a piece a while back on why people “don’t trust data, they only trust other people,” I found myself agreeing — but also seeing another side to the story.  In my experience, leaders don’t trust data directly — they trust the story data helps them tell. Sometimes that story reinforces what they already […]


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Author: Christine Haskell

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, […]


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Author: Daragh O Brien

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 […]


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Author: Larry Burns

The Role of AI in Mitigating Next-Generation Cyber Threats
The digital age has witnessed an exponential increase in data creation and interconnectivity, resulting in unprecedented challenges in cybersecurity. Businesses, governments, and individuals are perpetually at risk of cyber-attacks ranging from data breaches and financial theft to espionage and infrastructure sabotage. While necessary, traditional cybersecurity measures are often reactive rather than proactive, struggling to adapt […]


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Author: Srinivasa Bogireddy

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 […]


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Author: William A. Tanenbaum

AI and Business Transformation: Balancing Innovation and Control
AI is no longer just a concept or a futuristic tool. It’s here and it’s likely already integrated into many aspects of your business, potentially in ways you might not even realize. AI’s potential to transform how we operate, deliver services, and optimize workflows offers significant benefits, but it also comes with responsibilities — and […]


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Author: Ben Hunter III

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.

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Author: Actian Corporation