Read More
Author: Robert S. Seiner
Read More
Author: Subasini Periyakaruppan
Read More
Author: Ty Francis
Read More
Author: Myles Suer
Read More
Author: Chirag Agrawal
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 […]
The post Optimizing retail operations through a practical data strategy appeared first on LightsOnData.
Read More
Author: George Firican
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 […]
The post How an Internal AI Governance Council Drives Responsible Innovation appeared first on DATAVERSITY.
Read More
Author: Nichole Windholz
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 […]
The post The Data Danger of Agentic AI appeared first on DATAVERSITY.
Read More
Author: Samuel Bocetta
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 […]
The post How to Future-Proof Your Data and AI Strategy appeared first on DATAVERSITY.
Read More
Author: Ojas Rege
Read More
Author: Robert S. Seiner
Read More
Author: Christine Haskell
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.Â
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.Â
An example being, “Customer X purchased Product Y from Store Z on Date D.” Â
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. Â
In other words:Â
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.Â
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.  Â
With a federated knowledge graph:Â
This kind of insight is what drives intelligent automation. Â
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.Â
Federation in this context means:Â
This makes federated knowledge graphs especially useful in environments where data is distributed by design–across departments, cloud platforms, and business units.Â
AI automation relies not only on data, but also on understanding. A federated knowledge graph provides that understanding in several ways:Â
For data engineers and IT teams, this means less time spent maintaining pipelines and more time enabling intelligent applications. Â
Federated knowledge graphs are not just an addition to your modern data architecture; they amplify its capabilities. For instance:Â
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.Â
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:Â
Take a product tour today to experience data intelligence powered by a federated knowledge graph.Â
The post Why Federated Knowledge Graphs are the Missing Link in Your AI Strategy appeared first on Actian.
Read More
Author: Actian Corporation
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.
Read More
Author: Subasini Periyakaruppan
Read More
Author: Gopi Maren
Read More
Author: Robert S. Seiner
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.
Read More
Author: Dev Nag
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.
Read More
Author: Subasini Periyakaruppan
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 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.
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.
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.Â
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.Â
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.
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.
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 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.Â
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.Â
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.
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.
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.
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.
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.
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.Â
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.
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.
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.Â
The post Data Contracts, AI Search, and More: Actian’s Spring ’25 Product Launch appeared first on Actian.
Read More
Author: Dee Radh
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 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.
Below are some common hurdles and challenges facing organizations in the banking industry.
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.
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.
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.
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.
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.
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.
The Actian Data Intelligence Platform helps banks achieve regulatory compliance by automating compliance monitoring, data classification, and metadata management.
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.
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.
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.
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.
Read More
Author: Actian Corporation
Read More
Author: Daragh O Brien
Read More
Author: Larry Burns
Read More
Author: William A. Tanenbaum
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.
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.
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:
This balance of autonomy and alignment ensures that governance scales with the organization—without becoming a bottleneck to innovation.
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:
When governance processes are fueled by real-time, automated metadata, they no longer slow the business down—they accelerate it.
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:
In this model, governance becomes an enabler, not an obstacle, to data-driven work.
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:
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:
By building adaptability into the core of their governance strategy, enterprises can future-proof their investments and support innovation for years to come.
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.
Read More
Author: Nick Johnson
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.
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.
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.
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:
| Â | 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. |
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:
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.
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:
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.
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.
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.
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.
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.
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.
Understanding the roles of data owner vs. data steward is crucial for establishing an effective data governance strategy. Data owners are responsible for the strategic oversight of data, ensuring its security, compliance, and alignment with business goals, while data stewards manage the day-to-day operations of data, focusing on its quality, metadata, and accessibility.
Actian supports both roles by providing a centralized platform for data governance, automated data quality monitoring, comprehensive metadata management, and collaborative tools. By enabling both data owners and data stewards to manage their responsibilities effectively, the platform helps organizations maintain high-quality, compliant, and accessible data, which is essential for making informed, data-driven decisions.
Tour the Actian Data Intelligence Platform or schedule a personalized demonstration of its capabilities today.
The post Data Owner vs. Data Steward: What’s the Difference? appeared first on Actian.
Read More
Author: Actian Corporation
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.
Read More
Author: Mark Cooper