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


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Author: Robert S. Seiner

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


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Author: Mark Horseman

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


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Author: Jason Foster

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


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Author: Christine Haskell

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


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Author: Dr. John Talburt

A Step Ahead: From Acts to Aggregates — Record-ness and Data-ness in Practice
Introduction  What is the difference between records and data? What differentiates records managers from data managers? Do these distinctions still matter as organizations take the plunge into artificial intelligence? Discussions that attempt to distinguish between records and data frequently articulate a heuristic for differentiation. “These items are records; those items are data.” Many organizations have […]


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Author: The MITRE Corporation

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

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

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

Knowledge Graph vs. Federated Knowledge Graph

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

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

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

In other words: 

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

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

Real-World Example of a Federated Knowledge Graph in Action

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

With a federated knowledge graph: 

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

This kind of insight is what drives intelligent automation.  

Why Federated Knowledge Graphs Matter

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

Federation in this context means: 

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

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

How Federated Knowledge Graphs Support AI Automation

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

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

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

Complementing Data Mesh and Data Fabric

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

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

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

A Smarter Foundation for AI

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

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

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

Get Started With Actian Data Intelligence

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

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


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Author: Actian Corporation

Everything You Need to Know About Synthetic Data


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

Data Observability vs. Data Monitoring

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.

What is Data Observability?

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.

Components of Data Observability

Data observability comprises five key pillars, which answer five key questions about datasets.

  1. Freshness: Is the data up to date?
  2. Volume: Is the expected amount of data present?
  3. Schema: Have there been any unexpected changes to the data structure?
  4. Lineage: Where does the data come from, and how does it flow across systems?
  5. Distribution: Are data values within expected ranges and formats?

These pillars allow teams to gain end-to-end visibility across pipelines, supporting proactive incident detection and root cause analysis.

Benefits of Implementing Data Observability

  • Proactive Issue Detection: Spot anomalies before they affect downstream analytics or decision-making.
  • Reduced Downtime: Quickly identify and resolve data pipeline issues, minimizing business disruption.
  • Improved Trust in Data: Enhanced transparency and accountability increase stakeholders’ confidence in data assets.
  • Operational Efficiency: Automation of anomaly detection reduces manual data validation.

What is Data Monitoring?

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.

Components of Data Monitoring

Core elements of data monitoring include the following.

  1. Threshold Alerts: Notifications triggered when data deviates from expected norms.
  2. Dashboards: Visual interfaces showing system performance and data health metrics.
  3. Log Collection: Capturing event logs to track errors and system behavior.
  4. Metrics Tracking: Monitoring KPIs such as latency, uptime, and throughput.

Monitoring tools are commonly used to catch operational failures or data issues after they occur.

Benefits of Data Monitoring

  • Real-Time Awareness: Teams are notified immediately when something goes wrong.
  • Improved SLA Management: Ensures systems meet service-level agreements by tracking uptime and performance.
  • Faster Troubleshooting: Log data and metrics help pinpoint issues.
  • Baseline Performance Management: Helps maintain and optimize system operations over time.

Key Differences Between Data Observability and Data Monitoring

While related, data observability and data monitoring are not interchangeable. They serve different purposes and offer unique value to modern data teams.

Scope and Depth of Analysis

  • Monitoring offers a surface-level view based on predefined rules and metrics. It answers questions like, “Is the data pipeline running?”
  • Observability goes deeper, allowing teams to understand why an issue occurred and how it affects other parts of the system. It analyzes metadata and system behaviors to provide contextual insights.

Proactive vs. Reactive Approaches

  • Monitoring is largely reactive. Alerts are triggered after an incident occurs.
  • Observability is proactive, enabling the prediction and prevention of failures through pattern analysis and anomaly detection.

Data Insights and Decision-Making

  • Monitoring is typically used for operational awareness and uptime.
  • Observability helps drive strategic decisions by identifying long-term trends, data quality issues, and pipeline inefficiencies.

How Data Observability and Monitoring Work Together

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.

Complementary Roles in Data Management

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.

Enhancing System Reliability and Performance

When integrated, observability and monitoring ensure:

  • Faster MTTR (Mean Time to Resolution).
  • Reduced false positives.
  • More resilient pipelines.
  • Clear accountability for data errors.

Organizations can shift from firefighting data problems to implementing long-term fixes and improvements.

Choosing the Right Strategy for An Organization

An organization’s approach to data health should align with business objectives, team structure, and available resources. A thoughtful strategy ensures long-term success.

Assessing Organizational Needs

Start by answering the following questions.

  • Is the organization experiencing frequent data pipeline failures?
  • Do stakeholders trust the data they use?
  • How critical is real-time data delivery to the business?

Organizations with complex data flows, strict compliance requirements, or customer-facing analytics need robust observability. Smaller teams may start with monitoring and scale up.

Evaluating Tools and Technologies

Tools for data monitoring include:

  • Prometheus
  • Grafana
  • Datadog

Popular data observability platforms include:

  • Monte Carlo
  • Actian Data Intelligence Platform
  • Bigeye

Consider ease of integration, scalability, and the ability to customize alerts or data models when selecting a platform.

Implementing a Balanced Approach

A phased strategy often works best:

  1. Establish Monitoring First. Track uptime, failures, and thresholds.
  2. Introduce Observability. Add deeper diagnostics like data lineage tracking, quality checks, and schema drift detection.
  3. Train Teams. Ensure teams understand how to interpret both alert-driven and context-rich insights.

Use Actian to Enhance Data Observability and Data Monitoring

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:

  • Monitor Data Pipelines in Real-Time. Track performance metrics, latency, and failures across hybrid and cloud environments.
  • Gain Deep Observability. Leverage built-in tools for data lineage, anomaly detection, schema change alerts, and freshness tracking.
  • Simplify Integration. Seamlessly connect to existing data warehouses, ETL tools, and BI platforms.
  • Automate Quality Checks. Establish rule-based and AI-driven checks for consistent data reliability.

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

Beyond Pilots: Reinventing Enterprise Operating Models with AI


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

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


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

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


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Author: Subasini Periyakaruppan

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


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Author: Ramalakshmi Murugan

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


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Author: Gopi Maren

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


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Author: Robert S. Seiner

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


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Author: Steve Hoberman

What Today’s Data Events Reveal About Tomorrow’s Enterprise Priorities

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:

1. AI has a Data Problem, and Everyone Knows It

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.

2. Old Data Challenges are Now Bigger and Move Faster

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.

3. Metadata is the Unsung Hero of Data Intelligence

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.

4. Data Intelligence is Catching On

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.

5. The Culture Around Data Access is Changing

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.

Data Intelligence is More Than a Trend

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

Reimagining Data Architecture for Agentic AI


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

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


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

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


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Author: Dev Nag

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


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

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


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Author: Subasini Periyakaruppan

What is Data Downtime?

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.

The Definition of Data Downtime

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.

Why Data Downtime Matters to Organizations

Organizations depend on reliable data to:

  • Power real-time dashboards.
  • Make strategic decisions.
  • Serve personalized customer experiences.
  • Maintain compliance.
  • Run predictive models.

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.

Causes of Data Downtime

Understanding the root causes of data downtime is key to preventing it. The causes generally fall into three broad categories.

Technical Failures

These include infrastructure or system issues that prevent data from being collected, processed, or delivered correctly. Examples include:

  • Broken ETL (Extract, Transform, Load) pipelines.
  • Server crashes or cloud outages.
  • Schema changes that break data dependencies.
  • Latency or timeout issues in APIs and data sources.

Even the most sophisticated data systems can experience downtime if not properly maintained and monitored.

Human Errors

Humans are often the weakest link in any system, and data systems are no exception. Common mistakes include:

  • Misconfigured jobs or scripts.
  • Deleting or modifying data unintentionally.
  • Incorrect logic in data transformations.
  • Miscommunication between engineering and business teams.

Without proper controls and processes, even a minor mistake can cause major data reliability issues.

External Factors

Sometimes, events outside the organization’s control contribute to data downtime. These include:

  • Third-party vendor failures.
  • Regulatory changes affecting data flow or storage.
  • Cybersecurity incidents such as ransomware attacks.
  • Natural disasters or power outages.

While not always preventable, the impact of these events can be mitigated with the right preparations and redundancies.

Impact of Data Downtime on Businesses

Data downtime is not just a technical inconvenience; it can also be a significant business disruption with serious consequences.

Operational Disruptions

When business operations rely on data to function, data downtime can halt progress. For instance:

  • Sales teams may lose visibility into performance metrics.
  • Inventory systems may become outdated, leading to stockouts.
  • Customer service reps may lack access to accurate information.

These disruptions can delay decision-making, reduce productivity, and negatively impact customer experience.

Financial Consequences

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:

  • A flawed pricing model due to incorrect data could lead to lost sales.
  • Delayed reporting may result in regulatory fines.
  • A faulty recommendation engine could hurt conversion rates.

Reputational Damage

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.

  • Customers may experience problems with ordering or receiving goods.
  • Investors may question the reliability of reporting.
  • Internal teams may lose confidence in data-driven strategies.

Data transparency is a differentiator for businesses, and reputational damage can be more costly than technical repairs in the long run.

Calculating the Cost of Data Downtime

Understanding the true cost of data downtime requires a comprehensive look at both direct and indirect impacts.

Direct and Indirect Costs

Direct costs include things like:

  • SLA penalties.
  • Missed revenue.
  • Extra staffing hours for remediation.

Indirect costs are harder to measure but equally damaging:

  • Loss of customer trust.
  • Delays in decision-making.
  • Decreased employee morale.

Quantifying these costs can help build a stronger business case for investing in data reliability solutions.

Industry-Specific Impacts

The cost of data downtime varies by industry.

  • Financial Services: A delayed or incorrect trade execution can result in millions of dollars in losses.
  • Retail: A single hour of product pricing errors during a sale can lead to thousands of missed sales or customer churn.
  • Healthcare: Inaccurate patient data can lead to misdiagnoses or regulatory violations.

Understanding the specific stakes for an organization’s industry is crucial when prioritizing investment in data reliability.

Long-Term Financial Implications

Recurring or prolonged data downtime doesn’t just cause short-term losses; it erodes long-term value. Over time, companies may experience:

  • Slower product development due to data mistrust.
  • Reduced competitiveness from poor decision-making.
  • Higher acquisition costs from churned customers.

Ultimately, organizations that cannot ensure consistent data quality will struggle to scale effectively.

How to Prevent Data Downtime

Preventing data downtime requires a holistic approach that combines technology, processes, and people.

Implementing Data Observability

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:

  • Detect anomalies before they cause damage.
  • Monitor end-to-end data flows.
  • Understand the root cause of data issues.

This proactive approach is essential in preventing and minimizing data downtime.

Enhancing Data Governance

Strong data governance ensures that roles, responsibilities, and standards are clearly defined. Key governance practices include:

  • Data cataloging and classification.
  • Access controls and permissions.
  • Audit trails and version control.
  • Clear ownership for each dataset or pipeline.

When governance is embedded into the data culture of an organization, errors and downtime become less frequent and easier to resolve.

Regular System Maintenance

Proactive system maintenance can help avoid downtime caused by technical failures. Best practices include:

  • Routine testing and validation of pipelines.
  • Scheduled backups and failover plans.
  • Continuous integration and deployment practices.
  • Ongoing performance optimization.

Just like physical infrastructure, data infrastructure needs regular care to remain reliable.

More on Data Observability as a Solution

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:

  • Automated anomaly detection.
  • Alerts on schema drift or missing data.
  • Data lineage tracking to understand downstream impacts.
  • Detailed diagnostics for faster resolution.

By implementing observability tools, organizations gain real-time insight into their data ecosystem, helping them move from reactive firefighting to proactive reliability management.

Actian Can Help Organize Data and Reduce Data Downtime

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:

  • Actian Data Intelligence Platform: A cloud-native platform that supports real-time analytics, data integration, and pipeline management across hybrid environments.
  • End-to-End Visibility: Monitor data freshness, volume, schema changes, and performance in one unified interface.
  • Automated Recovery Tools: Quickly detect and resolve issues with intelligent alerts and remediation workflows.
  • Secure, Governed Data Access: Built-in governance features help ensure data integrity and regulatory compliance.

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

Why and How to Enhance DevOps with AIOps


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

Data Contracts, AI Search, and More: Actian’s Spring ’25 Product Launch

This blog introduces Actian’s Spring 2025 launch, featuring 15 new capabilities that improve data governance, observability, productivity, and end-to-end integration across the data stack.

  • Actian’s new federated data contracts give teams full control over distributed data product creation and lifecycle management.
  • Ask AI and natural language search integrations boost productivity for business users across BI tools and browsers.
  • Enhanced observability features deliver real-time alerts, SQL-based metrics, and auto-generated incident tickets to reduce resolution time.

Actian’s Spring 2025 launch introduces 15 powerful new capabilities across our cloud and on-premises portfolio that help modern data teams navigate complex data landscapes while delivering ongoing business value.

Whether you’re a data steward working to establish governance at the source, a data engineer seeking to reduce incident response times, or a business leader looking to optimize data infrastructure costs, these updates deliver immediate, measurable impact.

What’s new in the Actian Cloud Portfolio

Leading this launch is an upgrade to our breakthrough data contract first functionality that enables true decentralized data management with enterprise-wide federated governance, allowing data producers to build and publish trusted data assets while maintaining centralized control. Combined with AI-powered natural language search through Ask AI and enhanced observability with custom SQL metrics, our cloud portfolio delivers real value for modern data teams.

Actian Data Intelligence

Decentralized Data Management Without Sacrificing Governance

The Actian Data Intelligence Platform (formerly Zeenea) now supports a complete data products and contracts workflow. Achieve scalable, decentralized data management by enabling individual domains to design, manage, and publish tailored data products into a federated data marketplace for broader consumption.

Combined with governance-by-design through data contracts integrated into CI/CD pipelines, this approach ensures governed data from source to consumption, keeping metadata consistently updated. 

Organizations no longer need to choose between development velocity and catalog accuracy; they can achieve both simultaneously. Data producers who previously spent hours on labor-intensive tasks can now focus on quickly building data products, while business users gain access to consistently trustworthy data assets with clear contracts for proper usage. 

Ask AI Transforms How Teams Find and Understand Data

Ask AI, an AI-powered natural language query system, changes how users interact with their data catalog. Users can ask questions in plain English and receive contextually relevant results with extractive summaries.

This semantic search capability goes far beyond traditional keyword matching. Ask AI understands the intent, searches across business glossaries and data models, and returns not just matching assets but concise summaries that directly answer the question. The feature automatically identifies whether users are asking questions versus performing keyword searches, adapting the search mechanism accordingly.

Business analysts no longer need to rely on data engineers to interpret data definitions, and new team members can become productive immediately without extensive training on the data catalog.

Chrome Extension Brings Context Directly to Your Workflow

Complementing Ask AI, our new Chrome Extension automatically highlights business terms and KPIs within BI tools. When users hover over highlighted terms, they instantly see standardized definitions pulled directly from the data catalog, without leaving their reports or dashboards.

For organizations with complex BI ecosystems, this feature improves data literacy while ensuring consistent interpretation of business metrics across teams.

Enhanced Tableau and Power BI Integration

Our expanded BI tool integration provides automated metadata extraction and detailed field-to-field lineage for both Tableau and Power BI environments.

For data engineers managing complex BI environments, this eliminates the manual effort required to trace data lineage across reporting tools. When business users question the accuracy of a dashboard metric, data teams can now provide complete lineage information in seconds.

Actian Data Observability

Custom SQL Metrics Eliminate Data Blind Spots

Actian Data Observability now supports fully custom SQL metrics. Unlike traditional observability tools that limit monitoring to predefined metrics, this capability allows teams to create unlimited metric time series using the full expressive power of SQL.

The impact on data reliability is immediate and measurable. Teams can now detect anomalies in business-critical metrics before they affect downstream systems or customer-facing applications. 

Actionable Notifications With Embedded Visuals

When data issues occur, context is everything. Our enhanced notification system now embeds visual representations of key metrics directly within email and Slack alerts. Data teams get immediate visual context about the severity and trend of issues without navigating to the observability tool.

This visual approach to alerting transforms incident response workflows. On-call engineers can assess the severity of issues instantly and prioritize their response accordingly. 

Automated JIRA Integration and a new Centralized Incident Management Hub

Every detected data incident now automatically creates a JIRA ticket with relevant context, metrics, and suggested remediation steps. This seamless integration ensures no data quality issues slip through the cracks while providing a complete audit trail for compliance and continuous improvement efforts.

Mean time to resolution (MTTR) improves dramatically when incident tickets are automatically populated with relevant technical context, and the new incident management hub facilitates faster diagnosis and resolution.

Redesigned Connection Flow Empowers Distributed Teams

Managing data connections across large organizations has always been a delicate balance between security and agility. Our redesigned connection creation flow addresses this challenge by enabling central IT teams to manage credentials and security configurations while allowing distributed data teams to manage their data assets independently.

This decoupled approach means faster time-to-value for new data initiatives without compromising security or governance standards.

Expanded Google Cloud Storage Support

We’ve added wildcard support for Google Cloud Storage file paths, enabling more flexible monitoring of dynamic and hierarchical data structures. Teams managing large-scale data lakes can now monitor entire directory structures with a single configuration, automatically detecting new files and folders as they’re created.

What’s New in the Actian On-Premises Portfolio

Our DataConnect 12.4 release delivers powerful new capabilities for organizations that require on-premises data management solutions, with enhanced automation, privacy protection, and data preparation features.

DataConnect v12.4

Automated Rule Creation with Inspect and Recommend

The new Inspect and Recommend feature analyzes datasets and automatically suggests context-appropriate quality rules.

This capability addresses one of the most significant barriers to effective data quality management: the time and expertise required to define comprehensive quality rules for diverse datasets. Instead of requiring extensive manual analysis, users can now generate, customize, and implement effective quality rules directly from their datasets in minutes.

Advanced Multi-Field Conditional Rules

We now support multi-field, conditional profiling and remediation rules, enabling comprehensive, context-aware data quality assessments. These advanced rules can analyze relationships across multiple fields, not just individual columns, and automatically trigger remediation actions when quality issues are detected.

For organizations with stringent compliance requirements, this capability is particularly valuable. 

Data Quality Index Provides Executive Visibility

The new Data Quality Index feature provides a simple, customizable dashboard that allows non-technical stakeholders to quickly understand the quality level of any dataset. Organizations can configure custom dimensions and weights for each field, ensuring that quality metrics align with specific business priorities and use cases.

Instead of technical quality metrics that require interpretation, the Data Quality Index provides clear, business-relevant indicators that executives can understand and act upon.

Streamlined Schema Evolution

Our new data preparation functionality enables users to augment and standardize schemas directly within the platform, eliminating the need for separate data preparation tools. This integrated approach offers the flexibility to add, reorder, or standardize data as needed while maintaining data integrity and supporting scalable operations.

Flexible Masking and Anonymization

Expanded data privacy capabilities provide sophisticated masking and anonymization options to help organizations protect sensitive information while maintaining data utility for analytics and development purposes. These capabilities are essential for organizations subject to regulations such as GDPR, HIPAA, CCPA, and PCI-DSS.

Beyond compliance requirements, these capabilities enable safer data sharing with third parties, partners, and research teams. 

Take Action

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


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Author: Dee Radh

Deploying AI Models in Clinical Workflows: Challenges and Best Practices


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

Improving Data Quality Using AI and ML


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

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


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Author: Udaya Veeramreddygari