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Author: Robert S. Seiner
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Author: The MITRE Corporation
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.
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Author: Actian Corporation
Two pivotal concepts have emerged at the forefront of modern data infrastructure management, both aimed at protecting the integrity of datasets and data pipelines: data observability and data monitoring. While they may sound similar, these practices differ in their objectives, execution, and impact. Understanding their distinctions, as well as how they complement each other, can empower teams to make informed decisions, detect issues faster, and improve overall data trustworthiness.
Data Observability is the practice of understanding and monitoring data’s behavior, quality, and performance as it flows through a system. It provides insights into data quality, lineage, performance, and reliability, enabling teams to detect and resolve issues proactively.
Data observability comprises five key pillars, which answer five key questions about datasets.
These pillars allow teams to gain end-to-end visibility across pipelines, supporting proactive incident detection and root cause analysis.
Data monitoring involves the continuous tracking of data and systems to identify errors, anomalies, or performance issues. It typically includes setting up alerts, dashboards, and metrics to oversee system operations and ensure data flows as expected.
Core elements of data monitoring include the following.
Monitoring tools are commonly used to catch operational failures or data issues after they occur.
While related, data observability and data monitoring are not interchangeable. They serve different purposes and offer unique value to modern data teams.
Despite their differences, data observability and monitoring are most powerful when used in tandem. Together, they create a comprehensive view of system health and data reliability.
Monitoring handles alerting and immediate issue recognition, while observability offers deep diagnostics and context. This combination ensures that teams are not only alerted to issues but are also equipped to resolve them effectively.
For example, a data monitoring system might alert a team to a failed ETL job. A data observability platform would then provide lineage and metadata context to show how the failure impacts downstream dashboards and provide insight into what caused the failure in the first place.
When integrated, observability and monitoring ensure:
Organizations can shift from firefighting data problems to implementing long-term fixes and improvements.
An organization’s approach to data health should align with business objectives, team structure, and available resources. A thoughtful strategy ensures long-term success.
Start by answering the following questions.
Organizations with complex data flows, strict compliance requirements, or customer-facing analytics need robust observability. Smaller teams may start with monitoring and scale up.
Tools for data monitoring include:
Popular data observability platforms include:
Consider ease of integration, scalability, and the ability to customize alerts or data models when selecting a platform.
A phased strategy often works best:
Data observability and data monitoring are both essential to ensuring data reliability, but they serve distinct functions. Monitoring offers immediate alerts and performance tracking, while observability provides in-depth insight into data systems’ behavior. Using both concepts together with the tools and solutions provided by Actian, organizations can create a resilient, trustworthy, and efficient data ecosystem that supports both operational excellence and strategic growth.
Actian offers a suite of solutions that help businesses modernize their data infrastructure while gaining full visibility and control over their data systems.
With the Actian Data Intelligence Platform, organizations can:
Organizations using Actian benefit from increased system reliability, reduced downtime, and greater trust in their analytics. Whether through building data lakes, powering real-time analytics, or managing compliance, Actian empowers data teams with the tools they need to succeed.
The post Data Observability vs. Data Monitoring appeared first on Actian.
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Author: Actian Corporation
Today, organizations and individuals face an ever-growing challenge: the sheer volume of data being generated and stored across various systems. This data needs to be properly organized, categorized, and made easily accessible for efficient decision-making. One critical aspect of organizing data is through the use of metadata, which serves as a descriptive layer that helps users understand, find, and utilize data effectively.
Among the various types of metadata, structural metadata plays a crucial role in facilitating improved data management and discovery. This article will define what structural metadata is, why it is useful, and how the Actian Data Intelligence Platform can help organizations better organize and manage their structural metadata to enhance data discovery.
Metadata is often classified into various types, such as descriptive metadata, administrative metadata, and structural metadata. While descriptive metadata provides basic information about the data (e.g., title, author, keywords), and administrative metadata focuses on the management and lifecycle of data (e.g., creation date, file size, permissions), structural metadata refers to the organizational elements that describe how data is structured within a dataset or system.
In simpler terms, structural metadata defines the relationships between the different components of a dataset. It provides the blueprint for how data is organized, linked, and formatted, making it easier for users to navigate complex datasets. In a relational database, for example, structural metadata would define how tables, rows, columns, and relationships between entities are arranged. In a document repository, it could describe the format and organization of files, such as chapters, sections, and subsections.
Here are some key aspects of structural metadata:
Structural metadata plays a fundamental role in ensuring that data is understandable, accessible, and usable. Here are several reasons why it is essential:
Despite its importance, managing structural metadata is not without challenges.
The Actian Data Intelligence Platform provides organizations with the tools to handle their metadata efficiently. By enabling centralized metadata management, organizations can easily catalog and manage structural metadata, thereby enhancing data discovery and improving overall data governance. Here’s how the platform can help:
The Actian Data Intelligent Platform allows organizations to centralize all metadata, including structural metadata, into a single, unified repository. This centralization makes it easier to manage, search, and access data across different systems and platforms. No matter where the data resides, users can access the metadata and understand how datasets are structured, enabling faster data discovery.
The platform supports the automated ingestion of metadata from a wide range of data sources, including databases, data lakes, and cloud storage platforms. This automation reduces the manual effort required to capture and maintain metadata, ensuring that structural metadata is always up to date and accurately reflects the structure of the underlying datasets.
With Actian’s platform, organizations can visualize data lineage and track the relationships between different data elements. This feature allows users to see how data flows through various systems and how different datasets are connected. By understanding these relationships, users can better navigate complex datasets and conduct more meaningful analyses.
The Actian Data Intelligence Platform provides powerful data classification and tagging capabilities that allow organizations to categorize data based on its structure, type, and other metadata attributes. This helps users quickly identify the types of data they are working with and make more informed decisions about how to query and analyze it.
The platform’s metadata catalog enables users to easily search and find datasets based on specific structural attributes. Whether looking for datasets by schema, data format, or relationships, users can quickly pinpoint relevant data, which speeds up the data discovery process and improves overall efficiency.
Actian’s platform fosters collaboration by providing a platform where users can share insights, metadata definitions, and best practices. This transparency ensures that everyone in the organization is on the same page when it comes to understanding the structure of data, which is essential for data governance and compliance.
Using a federated knowledge graph, organizations can automatically identify, classify, and track data assets based on contextual and semantic factors. This makes it easier to map assets to key business concepts, manage regulatory compliance, and mitigate risks.
Managing and organizing metadata is more important than ever in the current technological climate. Structural metadata plays a crucial role in ensuring that datasets are organized, understandable, and accessible. By defining the relationships, formats, and hierarchies of data, structural metadata enables better data discovery, integration, and analysis.
However, managing this metadata can be a complex and challenging task, especially as datasets grow and become more fragmented. That’s where the Actian Data Intelligence Platform comes in. With Actian’s support, organizations can unlock the full potential of their data, streamline their data management processes, and ensure that their data governance practices are aligned with industry standards, all while improving efficiency and collaboration across teams.
Take a tour of the Actian Data Intelligence Platform or sign up for a personalized demonstration today.
The post Understanding Structural Metadata appeared first on Actian.
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Author: Actian Corporation
The enterprise AI landscape has reached an inflection point. After years of pilots and proof-of-concepts, organizations are now committing unprecedented resources to AI, with double-digit budget increases expected across industries in 2025. This isn’t merely about technological adoption. It reflects a deep rethinking of how businesses operate at scale. The urgency is clear: 70% of the software used […]
The post Beyond Pilots: Reinventing Enterprise Operating Models with AI appeared first on DATAVERSITY.
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Author: Gautam Singh
After attending several industry events over the last few months—from Gartner® Data & Analytics Summit in Orlando to the Databricks Data + AI Summit in San Francisco to regional conferences—it’s clear that some themes are becoming prevalent for enterprises across all industries. For example, artificial intelligence (AI) is no longer a buzzword dropped into conversations—it is the conversation.
Granted, we’ve been hearing about AI and GenAI for the last few years, but the presentations, booth messaging, sessions, and discussions at events have quickly evolved as organizations are now implementing actual use cases. Not surprisingly, at least to those of us who have advocated for data quality at scale throughout our careers, the launch of AI use cases has given rise to a familiar but growing challenge. That challenge is ensuring data quality and governance for the extremely large volumes of data that companies are managing for AI and other uses.
As someone who’s fortunate enough to spend a lot of time meeting with data and business leaders at conferences, I have a front-row seat to what’s resonating and what’s still frustrating organizations in their data ecosystems. Here are five key takeaways:
At every event I’ve attended recently, a familiar phrase kept coming up: “garbage in, garbage out.” Organizations are excited about AI’s potential, but they’re worried about the quality of the data feeding their models. We’ve moved from talking about building and fine-tuning models to talking about data readiness, specifically how to ensure data is clean, governed, and AI-ready to deliver trusted outcomes.
“Garbage in, garbage out” is an old adage, but it holds true today, especially as enterprises look to optimize AI across their business. Data and analytics leaders are emphasizing the importance of data governance, metadata, and trust. They’re realizing that data quality issues can quickly cause major downstream issues that are time-consuming and expensive to fix. The fact is everyone is investing or looking to invest in AI. Now the race is on to ensure those investments pay off, which requires quality data.
Issues such as data governance and data quality aren’t new. The difference is that they have now been amplified by the scale and speed of today’s enterprise data environments. Fifteen years ago, if something went wrong with a data pipeline, maybe a report was late. Today, one data quality issue can cascade through dozens of systems, impact customer experiences in real time, and train AI on flawed inputs. In other words, problems scale.
This is why data observability is essential. Only monitoring infrastructure is not enough anymore. Organizations need end-to-end visibility into data flows, lineage, quality metrics, and anomalies. And they need to mitigate issues before they move downstream and cause disruption. At Actian, we’ve seen how data observability capabilities, including real-time alerts, custom metrics, and native integration with tools like JIRA, resonate strongly with customers. Companies must move beyond fixing problems after the fact to proactively identifying and addressing issues early in the data lifecycle.
While AI and observability steal the spotlight at conferences, metadata is quietly becoming a top differentiator. Surprisingly, metadata management wasn’t front and center at most events I attended, but it should be. Metadata provides the context, traceability, and searchability that data teams need to scale responsibly and deliver trusted data products.
For example, with the Actian Data Intelligence Platform, all metadata is managed by a federated knowledge graph. The platform enables smart data usage through integrated metadata, governance, and AI automation. Whether a business user is searching for a data product or a data steward is managing lineage and access, metadata makes the data ecosystem more intelligent and easier to use.
I’ve seen a noticeable uptick in how vendors talk about “data intelligence.” It’s becoming increasingly discussed as part of modern platforms, and for good reason. Data intelligence brings together cataloging, governance, and collaboration in a way that’s advantageous for both IT and business teams.
While we’re seeing other vendors enter this space, I believe Actian’s competitive edge lies in our simplicity and scalability. We provide intuitive tools for data exploration, flexible catalog models, and ready-to-use data products backed by data contracts. These aren’t just features. They’re business enablers that allow users at all skill levels to quickly and easily access the data they need.
One of the most interesting shifts I’ve noticed is a tradeoff, if not friction, between data democratization and data protection. Chief data officers and data stewards want to empower teams with self-service analytics, but they also need to ensure sensitive information is protected.
The new mindset isn’t “open all data to everyone” or “lock it all down” but instead a strategic approach that delivers smart access control. For example, a marketer doesn’t need access to customer phone numbers, while a sales rep might. Enabling granular control over data access based on roles and context, right down to the row and column level, is a top priority.
Some of the most meaningful insights I gain at events take place through unstructured, one-on-one interactions. Whether it’s chatting over dinner with customers or striking up a conversation with a stranger before a breakout session, these moments help us understand what really matters to businesses.
While AI may be the main topic right now, it’s clear that data intelligence will determine how well enterprises actually deliver on AI’s promise. That means prioritizing data quality, trust, observability, access, and governance, all built on a foundation of rich metadata. At the end of the day, building a smart, AI-ready enterprise starts with something deceptively simple—better data.
When I’m at events, I encourage attendees who visit with Actian to experience a product tour. That’s because once data leaders see what trusted, intelligent data can do, it changes the way they think about data, use cases, and outcomes.
The post What Today’s Data Events Reveal About Tomorrow’s Enterprise Priorities appeared first on Actian.
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Author: Liz Brown
Data downtime occurs when data is missing, inaccurate, delayed, or otherwise unusable. The effects ripple through an organization by disrupting operations, misleading decision-makers, and eroding trust in systems. Understanding what data downtime is, why it matters, and how to prevent it is essential for any organization that relies on data to drive performance and innovation.
Data downtime refers to any period during which data is inaccurate, missing, incomplete, delayed, or otherwise unavailable for use. This downtime can affect internal analytics, customer-facing dashboards, automated decision systems, or machine learning pipelines.
Unlike traditional system downtime, which is often clearly measurable, data downtime can be silent and insidious. Data pipelines may continue running, dashboards may continue loading, but the information being processed or displayed may be wrong, incomplete, or delayed. This makes it even more dangerous, as issues can go unnoticed until they cause significant damage.
Organizations depend on reliable data to:
When data becomes unreliable, it undermines each of these functions. Whether it’s a marketing campaign using outdated data or a supply chain decision based on faulty inputs, the result is often lost revenue, inefficiency, and diminished trust.
Understanding the root causes of data downtime is key to preventing it. The causes generally fall into three broad categories.
These include infrastructure or system issues that prevent data from being collected, processed, or delivered correctly. Examples include:
Even the most sophisticated data systems can experience downtime if not properly maintained and monitored.
Humans are often the weakest link in any system, and data systems are no exception. Common mistakes include:
Without proper controls and processes, even a minor mistake can cause major data reliability issues.
Sometimes, events outside the organization’s control contribute to data downtime. These include:
While not always preventable, the impact of these events can be mitigated with the right preparations and redundancies.
Data downtime is not just a technical inconvenience; it can also be a significant business disruption with serious consequences.
When business operations rely on data to function, data downtime can halt progress. For instance:
These disruptions can delay decision-making, reduce productivity, and negatively impact customer experience.
The financial cost of data downtime can be staggering, especially in sectors such as finance, e-commerce, and logistics. Missed opportunities, incorrect billing, and lost transactions all have a direct impact on the bottom line. For example:
Trust is hard to earn and easy to lose. When customers, partners, or stakeholders discover that a company’s data is flawed or unreliable, the reputational hit can be long-lasting.
Data transparency is a differentiator for businesses, and reputational damage can be more costly than technical repairs in the long run.
Understanding the true cost of data downtime requires a comprehensive look at both direct and indirect impacts.
Direct costs include things like:
Indirect costs are harder to measure but equally damaging:
Quantifying these costs can help build a stronger business case for investing in data reliability solutions.
The cost of data downtime varies by industry.
Understanding the specific stakes for an organization’s industry is crucial when prioritizing investment in data reliability.
Recurring or prolonged data downtime doesn’t just cause short-term losses; it erodes long-term value. Over time, companies may experience:
Ultimately, organizations that cannot ensure consistent data quality will struggle to scale effectively.
Preventing data downtime requires a holistic approach that combines technology, processes, and people.
Data observability is the practice of understanding the health of data systems through monitoring metadata like freshness, volume, schema, distribution, and lineage. By implementing observability platforms, organizations can:
This proactive approach is essential in preventing and minimizing data downtime.
Strong data governance ensures that roles, responsibilities, and standards are clearly defined. Key governance practices include:
When governance is embedded into the data culture of an organization, errors and downtime become less frequent and easier to resolve.
Proactive system maintenance can help avoid downtime caused by technical failures. Best practices include:
Just like physical infrastructure, data infrastructure needs regular care to remain reliable.
More than just a buzzword, data observability is emerging as a mission-critical function in modern data architectures. It shifts the focus from passive monitoring to active insight and prediction.
Observability platforms provide:
By implementing observability tools, organizations gain real-time insight into their data ecosystem, helping them move from reactive firefighting to proactive reliability management.
Data downtime is a serious threat to operational efficiency, decision-making, and trust in modern organizations. While its causes are varied, its consequences are universally damaging. Fortunately, by embracing tools like data observability and solutions like the Actian Data Intelligence Platform, businesses can detect issues faster, prevent failures, and build resilient data systems.
Actian offers a range of products and solutions to help organizations manage their data and reduce or prevent data downtime. Key capabilities include:
Organizations that use Actian can improve data trust, accelerate analytics, and eliminate costly disruptions caused by unreliable data.
The post What is Data Downtime? appeared first on Actian.
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Author: Actian Corporation
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.
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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.
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Author: Actian Corporation
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Author: Larry Burns
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Author: William A. Tanenbaum
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.
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.
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.
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.
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.
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.
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.
Data stewards oversee the entire data lifecycle, from creation and storage to deletion and archiving.
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.
Data stewards are responsible for documenting data governance policies, standards, and procedures. This documentation is essential for audits, regulatory compliance, and internal training.
Data stewards play a crucial role in the success of an organization’s data governance framework. They are responsible for managing data quality, ensuring compliance, monitoring data access, and maintaining data integrity. By leveraging the Actian Data Intelligence Platform, data stewards can streamline their responsibilities and more effectively govern data across the organization.
With the platform’s centralized data catalog, automated data quality monitoring, data lineage tracking, and compliance tools, data stewards are empowered to maintain high-quality data, ensure regulatory compliance, and foster collaboration between stakeholders.
Request a personalized demo of the Actian Data Intelligence Platform today.
The post The Role of Data Stewards in Data Governance appeared first on Actian.
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Author: Actian Corporation
In today’s hyper-competitive economy, data is a critical asset that drives innovation, strategic decision-making, and competitive advantage. However, for many mid-sized organizations, turning raw data into actionable business intelligence (BI) is challenging. The rapid pace of technological advancements, coupled with increasingly complex data environments, presents significant hurdles, particularly for those with limited resources to build […]
The post Turning Data into Insights: A Smarter Playbook for Mid-Size Businesses appeared first on DATAVERSITY.
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Author: Ken Ammon
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.
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Author: Nick Johnson
The data that is stored in vector databases is key to the success of generative AI (GenAI) for enterprises in all industries. Up-to-date, private data in company data sources, including unstructured data and structured data, is what is required during AI inferencing to make GenAI models more accurate and relevant. To make the data systematically […]
The post Generative AI Calls for a Master Class in Enterprise Storage appeared first on DATAVERSITY.
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Author: Eric Herzog
Think of a bank’s treasurer responsible for international cash movement across its global accounts. He receives a notification that a significant amount has been credited to one of the accounts in Asia. A few minutes later, the funds have been transferred to clear up a cash requirement on the other side of the world in Europe. […]
The post Real-Time Financial Data: Transforming Decision-Making in the Banking Sector appeared first on DATAVERSITY.
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Author: Gaurav Belani
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.
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Author: Actian Corporation
Do you want your business users to embrace and use analytics? You want your business to enjoy the benefits of fact-based decision making? You want your business to use the tools of business intelligence to improve market presence, customer satisfaction and team productivity and collaboration? A scarcity of data scientists will no longer hinder the […]
The post Analytics and Citizen Data Scientists Ensure Business Advantage appeared first on DATAVERSITY.
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Author: Kartik Patel
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.Â
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:
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.
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:
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.
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.
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.
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.
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.
Here are a few ways organizations are using data intelligence platforms:
As more organizations embrace hybrid and multi-cloud architectures, data intelligence platforms are becoming part of an essential infrastructure for trusted, scalable data operations.
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:
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.
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Author: Dee Radh
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.
Data observability enables organizations to:
Yet most tools still trade off depth for speed or precision for price. Actian takes a fundamentally different approach, offering full coverage without compromise.
Actian Data Observability delivers on four pillars of enterprise value:
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.
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.
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.
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 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.
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.
If you’re evaluating data observability tools and need:
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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Author: Phil Ostroff
In a world where data is the new oil, most enterprises still operate in the dark—literally. Estimates suggest that up to 80% of enterprise data remains “dark”: unused, unknown, or invisible to teams that need it most. Dark Data is the untapped information collected through routine business activities but left unanalyzed—think unused log files, untagged cloud storage, redundant CRM fields, or siloed operational records.
Understanding and managing this type of data isn’t just a matter of hygiene—it’s a competitive imperative. Dark Data obscures insights, introduces compliance risk, and inflates storage costs. Worse, it erodes trust in enterprise data, making transformation efforts slower and costlier.
That’s where the Actian Data Intelligence Platform stands apart. While many solutions focus narrowly on metadata governance or data quality alone, Actian’s integrated approach is engineered to help you surface, understand, and operationalize your hidden data assets with precision and speed.
Traditional data catalogs offer discovery—but only for data already known or documented. Data observability tools track quality—but typically only for data actively moving through pipelines. This leaves a blind spot: static, historical, or misclassified data, often untouched by either tool.
That’s the problem with relying on siloed solutions offered by other vendors. These platforms may excel at metadata management but often lack deep integration with real-time anomaly detection, making them blind to decaying or rogue data sources. Similarly, standalone observability tools identify schema drifts and freshness issues but don’t reveal the context or lineage needed to re-integrate that data.
Actian Data Intelligence Platform closes this gap. By combining metadata management and data observability, the  platform, when combined with Actian Data Observability, offers a dual-lens approach:
Most platforms only solve part of the Dark Data challenge. Here are five ways the Actian Data Intelligence Platform stands apart:
Dark Data is more than a nuisance—it’s a barrier to agility, trust, and innovation. As enterprises strive for data-driven cultures, tools that only address part of the problem are no longer enough.
Actian Data Intelligence Platform, containing both metadata management and data observability, provides a compelling and complete solution to discover, assess, and activate data across your environment—even the data you didn’t know you had. Don’t just manage your data—illuminate it.
Find out more about Actian’s data observability capabilities.
The post Shedding Light on Dark Data With Actian Data Intelligence appeared first on Actian.
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Author: Phil Ostroff
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