Data Governance and CSR: Evolving Together
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Author: Robert S. Seiner
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Author: Robert S. Seiner
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Author: Mark Horseman
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Author: Jason Foster
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Author: Dr. John Talburt
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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
Synthetic data sounds like something out of science fiction, but it’s fast becoming the backbone of modern machine learning and data privacy initiatives. It enables faster development, stronger security, and fewer ethical headaches – and it’s evolving quickly. So if you’ve ever wondered what synthetic data really is, how it’s made, and why it’s taking center […]
The post Everything You Need to Know About Synthetic Data appeared first on DATAVERSITY.
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Author: Nahla Davies
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
In Part 1 of this series, we established the strategic foundation for external data success: defining your organizational direction, determining specific data requirements, and selecting the right data providers. We also introduced the critical concept of external data stewardship — identifying key stakeholders who bridge the gap between business requirements and technical implementation. This second part […]
The post External Data Strategy: Governance, Implementation, and Success (Part 2) appeared first on DATAVERSITY.
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Author: Subasini Periyakaruppan
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Author: Ramalakshmi Murugan
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Author: Gopi Maren
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Author: Robert S. Seiner
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Author: Steve Hoberman
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
As agentic AI and autonomous systems transform the enterprise landscape, organizations face a new imperative: Fundamentally reimagining data architecture is no longer optional; it’s required for AI success. Many enterprises are coming to the realization that traditional data architectures, which are built for structured data and deterministic workloads, are ill-equipped to support agentic AI’s demands […]
The post Reimagining Data Architecture for Agentic AI appeared first on DATAVERSITY.
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Author: Tami Fertig
The federal government’s proposal to impose a 10-year freeze on state-level AI regulation isn’t happening in a vacuum but in direct response to California. The state’s AI Accountability Act (SB 1047) has been making waves for its ambition to hold developers of powerful AI models accountable through mandatory safety testing, public disclosures, and the creation of a new regulatory […]
The post Future-Proofing AI Under a Federal Umbrella: What a 10-Year State Regulation Freeze Means appeared first on DATAVERSITY.
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Author: Dev Nag
In today’s data-driven business environment, the ability to leverage external information sources has become a critical differentiator between market leaders and laggards. Organizations that successfully harness external data don’t just gather more information – they transform how they understand their customers, anticipate market shifts, and identify growth opportunities. However, the path from recognizing the need for […]
The post External Data Strategy: From Vision to Vendor Selection (Part 1) appeared first on DATAVERSITY.
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Author: Subasini Periyakaruppan
Data 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
AIOps, the practice of enhancing IT and DevOps with help from artificial intelligence and machine learning, is not an especially new idea. It has been nearly a decade since Gartner coined the term in 2016. Yet, the growing sophistication of AI technology is making AIOps much more powerful. Gone are the days when AIOps was mostly a […]
The post Why and How to Enhance DevOps with AIOps appeared first on DATAVERSITY.
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Author: Derek Ashmore
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 global healthcare AI market is projected to grow from $32.34 billion in 2024 to $431 billion by 2032. It is evident that artificial intelligence (AI) is transforming the healthcare sector, one workflow at a time. Even so, hospitals and clinics struggle to successfully integrate the technology into their workflows, as real-world deployment is fraught […]
The post Deploying AI Models in Clinical Workflows: Challenges and Best Practices appeared first on DATAVERSITY.
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Author: Gaurav Belani
In our fast-paced, interconnected digital world, data is truly the heartbeat of how organizations make decisions. However, the rapid explosion of data in terms of volume, speed, and diversity has brought about significant challenges in keeping that data reliable and high-quality. Relying on traditional manual methods for data governance just doesn’t cut it anymore; in […]
The post Improving Data Quality Using AI and ML appeared first on DATAVERSITY.
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Author: Udaya Veeramreddygari