Summary

  • Data monitoring is a reactive approach that tracks known metrics against predefined rules and thresholds.
  • Data observability is proactive, providing a holistic view of the entire system to identify unknown anomalies.
  • While monitoring alerts you when a metric fails, observability explains why it failed through root-cause analysis.
  • A successful strategy integrates both to ensure high standards of data health, reliability, and continuous availability.

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 tools 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.


Blog | Databases | | 3 min read

Securing Your Data With Actian Vector, Part 4

securing your data with actian vector

Summary

  • Actian Vector 7.0 upgrade preserves existing encrypted database keys.
  • New main and derived keys are applied automatically.
  • Databases must be temporarily unlocked during upgrade.
  • Uses upgradedb utility with passphrase-based access steps.
  • Ensures secure, seamless upgrades without data exposure.

This fourth blog post in the series explains how an existing encrypted database is upgraded from an older version of Actian Vector to Actian Vector 7.0.

Upgrading an Encrypted Database

When upgrading databases from older versions to Actian Vector 7.0, the internal changes to equip them with a “main key” and appropriate derived keys are, of course, handled automatically. For encrypted databases, this includes preserving their already existing database key because it continues to serve for encrypting and decrypting the data.

When using the in-place upgrade method with the “upgradedb” utility, encrypted databases need to be unlocked during the upgrade procedure. Otherwise, the “upgradedb” utility cannot connect to the locked database to perform the upgrade. These five  steps upgrade an encrypted database:

  1. After installation and start-up of Actian Vector 7.0, an existing encrypted database is locked and not yet upgraded. Therefore, it is not possible to connect directly to the database to unlock it.
  2. Connect to database “iidbdb”. The “iidbdb” database is upgraded automatically during the startup of the new Version 7.0.
  3. In the session connected to “iibdb”, temporarily unlock the encrypted database for the upgrade. Run the statement:ENABLE PASSPHRASE ‘<pass phrase>’ ON DATABASE <name_of_encrypted_database>;
  4. With the encrypted database temporarily unlocked, it is now possible to run the utility “upgradedb” for this database.
  5. After running “upgradedb” for the encrypted database, it is necessary to unlock this database again via a direct connection. Use the Terminal Monitor “sql” with the commandline option “-no_x100” to connect directly to the encrypted database. In this session run the statement:ENABLE PASSPHRASE ‘<pass phrase>’;This last step persists the preservation of the already existing database key.

For more details on securing data with Actian Vector, find out how to:

Trusted Security in Every Upgrade

Upgrading to Actian Vector 7.0 doesn’t mean compromising encryption. The process ensures that existing database keys are preserved, while new key structures are applied automatically. By following a few essential steps, organizations can confidently upgrade their Actian Vector database without disrupting data security or accessibility.

Explore Other Blogs on Securing Your Data With Actian Vector:


Blog | Data Intelligence | | 5 min read

Model Context Protocol Demystified: Why MCP is Everywhere

model context protocol demystified

Summary

  • MCP connects AI to real-time, trusted business context.
  • Enables accurate, actionable insights beyond generic AI responses.
  • Standardizes AI access across data systems and domains.
  • Powered by knowledge graphs for context and explainability.
  • Critical for scaling reliable, enterprise-ready agentic AI.

What is Model Context Protocol (MCP) and why is it suddenly being talked about everywhere? How does it support the future of agentic AI? And what happens to businesses that don’t implement it?

The short answer is MCP is the new universal standard connecting AI to trusted business context, fueling the rise of agentic AI. Organizations that ignore it risk being stuck with slow, unreliable insights while competitors gain a decisive edge.

What is Model Context Protocol?

From boardrooms to shop floors, AI is rewriting how businesses uncover insights, solve problems, and chart their futures. Yet even the most advanced AI models face a critical challenge. Without access to precise, contextualized information, their answers can fall short by being generic and lacking critical insights.

That’s where MCP comes in. MCP is a rapidly emerging standard that gives AI-powered applications, like large language models (LLM) assistants, the ability to connect to structured, real-time business context through a knowledge graph.

Think of MCP as a GPS for AI. It guides models directly to the most relevant and reliable information. Instead of building custom integrations for every tool or dataset, businesses can use MCP to give AI applications secure, standardized access to the information they need.

The result? AI systems that move beyond generic responses to deliver answers rooted in a company’s unique and current reality.

Why MCP Matters for Businesses

The rise of AI data analysts, which are LLM-powered assistants that translate natural-language questions into structured data queries, makes MCP mission-critical. Unlike traditional analytics tools that require SQL skills or dashboard expertise, an AI data analyst allows anyone to simply ask questions and get results.

These questions can be business focused, such as:

  • What’s driving our increase in customer churn?
  • How did supply chain delays impact last quarter’s revenue?
  • Are seasonal promotions improving profitability?

Answering these questions requires more than statistics. It demands contextual intelligence pulled from multiple, current data sources.

MCP ensures AI data analysts can:

  • Converse naturally. Users ask questions in plain language.
  • Ground answers in context. MCP optimizes knowledge graphs for context.
  • Be accessible to all users. No coding or data science expertise is needed.
  • Provide action-oriented insights. Deliver answers that leaders can trust.

In short, MCP is the bridge between decision-makers and the technical complexity of enterprise data.

The Business Advantages of MCP

The value of AI isn’t in generating an answer. It’s in generating the right answer. MCP makes that possible by standardizing how AI connects to business context, turning data into precise, actionable, and trusted insights.

Key benefits of MCP include:

  • Improved accuracy. AI reflects current, trusted business data.
  • Scalability across domains. Each business function, such as finance, operations, and marketing, maintains its own tailored context.
  • Reduced integration complexity. A standard framework replaces costly, custom builds.
  • Future-proof flexibility. MCP ensures continuity as new AI models and platforms emerge.
  • Greater decision confidence. Leaders act on insights that reflect real business conditions.

With MCP, organizations move from AI that’s impressive to AI that’s indispensable.

Knowledge Graphs: The Heart of MCP

At the core of MCP are knowledge graphs, which are structured maps of business entities and their relationships. They don’t just store data. They provide context.

For example:

  • A customer isn’t simply a record. They are linked to orders, support tickets, and loyalty status.
  • A product isn’t only an SKU. It’s tied to suppliers, sales channels, and performance metrics.

By tapping into these connections, AI can answer not only what happened but also why it happened and what’s likely to happen next.

Powering Ongoing Success With MCP

Organizations that put MCP into practice and support it with a knowledge graph can create, manage, and export domain-specific knowledge graphs directly to MCP servers.

With the right approach to MCP, organizations gain:

  • Domain-specific context. Each business unit builds its own tailored graph.
  • Instant AI access. MCP provides secure, standardized entry points to data.
  • Dynamic updates. Continuous refreshes keep insights accurate as conditions shift.
  • Enterprise-wide intelligence. Organizations scale not just data, but contextual intelligence across the business.

MCP doesn’t just enhance AI. It transforms AI from a useful tool into a business-critical advantage.

Supporting Real-World Use Cases Using AI-Ready Data

AI-ready data plays an essential role in delivering fast, trusted results. With this data and MCP powered by a knowledge graph, organizations can deliver measurable outcomes to domains such as:

  • Finance. Quickly explain revenue discrepancies by connecting accounting, sales, and market data.
  • Supply chain. Answer questions such as, “Which suppliers pose the highest risk to production goals?” with context-rich insights on performance, timelines, and quality.
  • Customer service. Recommend personalized strategies using data from purchase history, service records, and sentiment analysis.
  • Executive leadership. Provide faster, more reliable insights to act decisively in dynamic markets.

In an era where the right answer at the right time can define market leadership, MCP ensure AI delivers insights that are accurate, actionable, and aligned with the current business reality. From the boardroom to the shop floor, MCP helps organizations optimize AI for decision-making and use cases.

Find out more by watching a short video about MCP for AI applications.


Summary

  • HCL Informix 15 now available on Microsoft Azure Marketplace.
  • Simplifies deployment, billing, and use of Azure cloud credits.
  • Enables scalable, secure, enterprise-grade cloud performance.
  • Supports modernization ahead of Informix 12.10 end of support.
  • Offers flexible hybrid and multi-cloud deployment options.

We’re excited to announce the general availability of HCL Informix® 15 on the Microsoft Azure Marketplace—bringing powerful, enterprise-grade performance to one of the world’s most trusted cloud platforms.

Now, customers can deploy HCL Informix 15 directly on Azure, take advantage of their Microsoft Azure committed cloud spend, and streamline procurement through Azure’s familiar and secure billing environment.

This milestone marks a major step forward in delivering flexible, modern deployment options for organizations running HCL Informix at scale—while making cloud adoption easier, faster, and more cost-effective.

Why it Matters: Cloud-Enabled on Your Terms

With HCL Informix 15 on Azure, enterprises can modernize their data environments while gaining enhanced performance, control, and reliability. Whether you’re planning new deployments or looking to migrate legacy instances, this Azure Marketplace offering enables:

  • Faster, frictionless procurement via Microsoft Azure billing.
  • Use of Microsoft Azure committed spend (MACCs) to fund licenses—no new budget needed.
  • Enterprise-ready security and scalability in a convenient hyperscaler environment.

HCL Informix is known for its unmatched ability to handle high-throughput OLTP workloads along with time series, spatial, and JSON data—all within a single engine. Now, that same capability is available with the simplicity and elasticity of the Azure cloud.

A Critical Moment for Existing Deployments

This launch comes at a pivotal time. General Availability (GA) support for Informix 12.10 will officially end on April 30, 2026, moving into Extended Support. As costs rise with Extended Support, some organizations may choose to go off-support entirely—but this comes with serious risks to security, compliance, and operational stability.

HCL Informix 15 on Azure gives organizations a modern, supported path forward—with the flexibility to deploy in the cloud and the financial efficiency of leveraging Azure cloud credits.

Built for Azure, Backed by Actian

HCL Informix 15 on Microsoft Azure offers seamless integration with native Azure services and infrastructure. Customers can expect:

  • Azure Marketplace-native deployment, with standard provisioning and scaling.
  • Support for hybrid and multi-cloud strategies.
  • High availability, backup, and monitoring tools, all optimized for Azure environments.
  • Enterprise-grade support from Actian and our global partner network.

For organizations looking to modernize, consolidate, or simply future-proof their Informix environments, this new Azure-based deployment model offers a practical and powerful solution.

Get Started Today

HCL Informix 15 is available now on the Microsoft Azure Marketplace.

If you’re an existing user of version 12.10, 14.10, or evaluating your next data platform strategy, now is the time to explore the benefits of running HCL Informix 15 on Azure. From simplified procurement to built-in modernization capabilities for ongoing value, this launch makes it easier than ever to align your data infrastructure with your cloud strategy.

» Explore the Azure Marketplace Listing
» Contact us for upgrade assistance or deployment support

 

Informix is a trademark of IBM Corporation in at least one jurisdiction and is used under license.


Blog | AI & ML | | 6 min read

Investing in People: Why Mentorship Matters at Actian

mentorships at actian

Summary

  • Mentorship drives career growth, learning, and long-term success.
  • Actian embeds mentorship into culture, onboarding, and teamwork.
  • Programs include buddies, career guidance, and cross-team learning.
  • Internships provide hands-on projects and real business impact.
  • Mentorship fosters collaboration, growth, and employee development.

If there’s been one constant throughout my career, it’s the power of mentorship. From my first job to my current role at Actian, mentors have helped me grow, see new possibilities, and make smart career decisions. As I’ve moved through different roles and companies, I’ve also made it a point to be a mentor for others, which has been a rewarding experience.

At Actian, I’m thrilled to see how much our leadership team values mentorships. This goes beyond a formal program. It covers how we work together, support our peers, and help each other succeed. Mentorships are a core part of Actian’s culture, which is one of the reasons we have an award-winning workplace. 

How Mentorships Shaped My Career

I can trace some of my biggest career growth moments back to the mentors who invested their time and expertise in me. One of my first managers, going all the way back to 2003, made a lasting impact. We worked together early in my career, but we stayed in touch. Even though it’s been more than two decades since we worked together, I consider her a trusted friend and still call her for advice and perspective.

What helped make that relationship so impactful was how intentional her advice was. She helped me map out my three-, five-, and 10-year career goals and encouraged me to think about where I wanted to be in my future, not just what I had to get done that day. This mindset helped shape how I approach my career and how I support others.

I’ve been lucky to have several mentors along the way, including a manager at AWS who I followed through multiple roles. Even today, I regularly call her to get advice on everything from job decisions to how to handle challenging situations. Those relationships have helped me at every stage along my career path, and they’ve inspired me to be just as intentional about mentoring others.

Paying it Forward by Supporting Mentees

No matter where I’ve worked, I’ve always made mentorships a priority. At IBM, AWS, and now Actian, I’ve had mentees whether I was in management or an individual contributor role.

I support having career conversations that go beyond the next task we need to accomplish and instead focus on long-term personal growth. That’s why when one of my mentees from AWS who is based in Sweden took a job at Google, we still continue to connect and share experiences.

I like to map growth and goals on a 2×2 chart. This includes asking questions such as:

  • What are you working on now?
  • What are your short-term goals?
  • Where do you want to be long-term?
  • What skills or experiences will help you get there?

These conversations help people see beyond the daily workload and emphasize building a career they’re excited about.

Mentorship in Action at Actian

One of the things I really appreciate at Actian is how the mentorship program is woven into our culture. Every new hire gets an onboarding buddy, which I view as a really smart approach to accelerating how quickly we learn about Actian processes and priorities, and also solve any potential workplace challenges.

That ongoing buddy is the new employee’s go-to resource for all those early questions and, more importantly, someone who can offer guidance from day one in a safe environment. When I joined Actian, Ron Weber was my onboarding buddy. It was very beneficial to have a seasoned marketing professional and colleague to lean on for insights.

The engagement doesn’t end as employees become acclimated to the organization. Mentorships happen naturally at Actian through everyday interactions, career conversations, and ongoing collaboration. I always encourage people to build relationships outside of their immediate teams.

For instance, some of the best advice I’ve received came from people in sales because hearing different perspectives helps me better understand the impact of our work. Hearing other departments’ viewpoints also enables me to think about aspects of our projects that I might have missed or not fully considered. 

Why I Fully Support the Actian Internship and Mentoring Programs

Our internship program is another way I see Actian’s commitment to developing employees and preparing them for the next stage of their careers. Throughout the year and especially in the summer, we engage interns across various departments. They work on everything from creating marketing campaigns to elevating customer experiences to developing innovative technology.

We pair interns with managers and onboarding buddies, giving them meaningful projects with opportunities to drive business outcomes. This isn’t busy work. We offer hands-on projects that directly contribute to our business, platforms, and customer experiences.

For example, this summer I have an intern on my team. She’s leading an account-based marketing project that will be sent directly to customers. It’s rewarding to see her grow, take ownership of the project, and gain experience that she can use both in the classroom and throughout her career. As a manager, having an intern helps me move forward on strategic projects that I might not otherwise have time to tackle.

That’s why our internship program is a win for everyone. Our interns gain valuable real-world experience, and Actian benefits from fresh perspectives, new ideas, and brand awareness in places we may not normally reach. When interns return to campus and talk about working at Actian, they introduce our company to other students—future business leaders—and their professors. I love that Actian gets this exposure on campuses and reaches an audience we don’t otherwise engage with.

 Why Mentorship Will Always Be Important

Career growth doesn’t happen in isolation. It happens through connections, conversations, and continuous learning. One of the most rewarding ways we bring this belief to life at Actian is through our mentoring culture.

Even after years in the field, I still seek advice from my mentors. I want to continue to learn, grow, and avoid blind spots. Mentorship has been a huge part of my success, and it’s something I’m deeply passionate about for others, regardless of where they are in their career.

At Actian, I see mentorships in action every day, from onboarding new employees to internships with university students to the everyday career conversations we have across the business. It’s part of who we are. It’s also a big reason why Actian is such a great place for people to grow their skill sets and advance their careers.


Summary

  • Federated knowledge graphs unify metadata, context, and relationships.
  • Enable faster discovery, trust, and governance across data domains.
  • Actian delivers real-time, scalable, and decentralized data visibility.
  • Outperforms static catalogs with semantic search and dynamic lineage.
  • Empowers teams with accurate, context-rich, AI-ready data insights.

Data is everywhere, and organizations are awash in information but still struggle to turn it into real business value. Silos persist, data duplication runs rampant, and finding the “right” data often feels like hunting for a needle in a haystack. Enter the federated knowledge graph—a modern approach to unifying data context, lineage, and relationships across domains, platforms, and business units.

Actian’s federated knowledge graph doesn’t just connect data. It connects understanding, enabling every user, from data engineers to business analysts, to explore data intuitively and confidently. It is what differentiates the Actian Data Intelligence Platform—and it’s something most of our competitors still lack.

What is a Federated Knowledge Graph, and Why Does it Matter?

A federated knowledge graph organizes and links metadata, business terms, technical definitions, and usage patterns across distributed data systems. Unlike traditional data catalogs that rely on rigid, centralized schemas, Actian’s knowledge graph architecture embraces federation—aggregating insights from multiple domains without forcing everything into a single model.

The result? A contextualized map of your organization’s data ecosystem that scales as you grow, evolves as your systems change, and remains discoverable by both technical and non-technical users.

Benefits include:

  • Faster data discovery through contextual relationships and semantic search.
  • Enhanced data trust via connected lineage, quality scores, and usage metrics.
  • Smarter governance is powered by visibility into how data is created, transformed, and consumed.
  • Better business alignment by linking KPIs and metrics to the actual data behind them.

How Actian Outperforms the Competition

Many data intelligence vendors offer basic graph features, such as lineage visualizations or relationship mapping. However, these are often centralized and limited in scope, focusing narrowly on either technical lineage or business glossary terms. They fall short of what a federated graph can achieve.

Here’s how Actian differentiates:

Capability Actian Data Intelligence Other Solutions
Federated Graph Architecture Yes – spans domains and tools. No – basic graph capabilities are often centralized and siloed.
Dynamic Lineage and Context Integrated across data catalog + observability. Limited, static lineage.
Semantic Search + NLP Deep contextual discovery. Keyword-based, inconsistent tagging.
Business-to-Technical Mapping Direct links between terms and data. Often requires manual stitching or third-party tools.
Open, Extensible Framework Supports modern data stacks (e.g., Iceberg, dbt, Fivetran, etc.). Often proprietary or restricted.

Competitors may claim to “connect the dots” with third-party solutions, but without a federated model, those dots remain scattered. Actian unifies them—and surfaces the insights your team needs in real time.

Some competitors even claim to include this functionality within their offering, but customers later find out that they simply offer an API to connect with additional solutions. That translates to more cost and, more importantly, additional potential points of failure.

Why the Right Knowledge Graph Matters to Your Business

In a world where data must be treated as an asset, too many organizations still struggle to locate, understand, and trust their data. Why? Because most data platforms stop at superficial integrations and centralized catalogs that can’t keep up with the growing complexity of enterprise ecosystems.

Let’s be clear: centralized metadata models are not built for scale. Competitors may offer lineage maps and glossaries, but their architectures are inherently rigid. They rely on manual curation, brittle connectors, and static representations of data relationships. The result is often a stale catalog that looks good in demos but quickly becomes outdated and irrelevant in practice.

Actian’s federated knowledge graph overcomes these limitations.

While other platforms force all metadata into a central repository—often requiring complex ETL-style ingestion—Actian leverages federation to connect to the source of truth in real-time, allowing metadata to remain decentralized while still being universally discoverable. That means your data catalog is always current, context-rich, and governed by design—not by duct tape.

Let’s dig into a few real-world consequences of those differences:

  • Other solutions rely heavily on keyword-based search, which leads to missed results and user frustration. Actian’s semantic knowledge graph enables natural language queries and contextual exploration, helping users find exactly what they need—even if they don’t know exactly what they’re looking for.
  • Most platforms offer disconnected glossaries and lineage tools, requiring users to mentally bridge the gap between business terms and the underlying technical data. Actian automatically maps these relationships across systems, roles, and tools—removing ambiguity and reducing reliance on tribal knowledge.
  • Manual lineage stitching is still the norm for many vendors, especially in complex hybrid-cloud environments. Actian dynamically updates lineage and usage patterns across data products and contracts, ensuring trustworthy insights and audit-ready governance.
  • Extensibility is a major limitation in other tools—either you use what their proprietary connectors allow, or you’re stuck. Actian’s open, API-first framework integrates seamlessly with modern data stacks, from dbt to Iceberg to Fivetran, without vendor lock-in.

The business impact is profound. With Actian, your teams no longer waste time second-guessing metadata quality, replicating datasets, or manually reconciling conflicting definitions. Instead, they’re empowered with a federated map of your enterprise data landscape, tailored to your architecture, aligned to your governance needs, and optimized for discovery at scale.

See Your Entire Data Universe

In a competitive landscape where most data platforms offer only partial insights, Actian’s federated knowledge graph delivers the full picture. It’s how modern enterprises scale trust, clarity, and collaboration across their entire data ecosystem.

Experience the Actian Data Intelligence Platform and its federated knowledge graph for yourself with a product demo.


Summary

  • Medallion architecture organizes data into Bronze, Silver, Gold layers.
  • Improves data quality, structure, and business-ready insights.
  • Layers are logical, enabling flexible, scalable implementations.
  • Supports federated models across teams and use cases.
  • Remains key for building AI-ready, analytics-driven data platforms.

The Medallion architecture is a popular design pattern for organizing data within a Lakehouse architecture. Many large enterprises use this pattern to logically structure their data. 

In this post, I’ll outline how the architecture works, explore its adaptability in modern enterprise environments, and highlight why it remains relevant, especially as data teams scale and federate.

Understanding the Three Layers

Bronze Layer

This layer acts as the zone for raw data collected from various sources. Data in the Bronze layer is stored in its original structure without any transformation, serving as a historical record and a single source of truth. It ensures that data is reliably captured and stored, making it available for further processing. Its key characteristics include high volume, variety, and veracity. The data is immutable to maintain the integrity of its original state.

Silver Layer

This layer refines, cleanses, and standardizes the raw data, preparing it for more complex operational and analytical tasks. In this layer, data undergoes quality checks, standardization, deduplication, and other enhancements that improve its reliability and usability. The Silver layer acts as a transitional stage where data is still granular but has been processed to ensure quality and consistency. Its key characteristics are that data in the Silver layer is more structured and query-friendly, making it easier for analysts and data scientists to work with.

Gold Layer

This layer delivers refined data optimized for specific business insights and decision-making. The Gold layer involves aggregating, summarizing, and enriching data to support high-level reporting and analytics. This layer focuses on performance, usability, and scalability, providing fast access to key metrics and insights.

Why the Layers are Logical, Not Physical

It’s crucial to think of these layers as logical, not physical. So, when discussing, for example, the Bronze layer, don’t frame it as just one physical layer. Instead, view it as a logical layer that could span across several physical layers. Below is how the Medallion architecture could look in practice:

building medallion architecture all layers

Figure 1 – Building Medallion Architectures, O’Reilly (2025)

 

This conceptual flexibility is vital, particularly in larger organizations. As these organizations expand, they face the challenge of scaling data management to support increased data volumes, accommodate more users, and address a wider variety of use cases.

Federated Medallion Architecture

In this context, it is important to understand that the Medallion architecture should not be viewed as a rigid concept; rather, it represents a spectrum of possibilities that can be adapted to unique circumstances, including the option of having multiple Medallion architectures tailored to different needs, which can influence the design of the overall architecture.

For instance, consider managing two Medallion architectures—one tailored to the source system and the other to consumption. In this case, the interaction between layers becomes crucial. You could argue that the Gold or data product layer in the source-aligned architecture effectively acts as the Bronze layer in the consumption-aligned architecture. This approach creates a more streamlined architecture by eliminating the need to duplicate the data product layer in the Bronze layer of the consumption setup.

The image below illustrates an architectural style that employs two basic consumers: a simple data provider, a single-use complex provider, and a distributor consumer.

building medallion architecture simple data provider chart

Figure 2 – Building Medallion Architectures, O’Reilly (2025)

Managing Complexity Across Teams

Building Medallion architectures can be challenging, especially when many teams are involved, each requiring access to data from others. In such scenarios, you might consider establishing separate Medallion architectures for each team, complete with their own Bronze, Silver, and Gold layers.

However, offering good guidance is essential to prevent the emergence of too many variations, which could hinder interoperability between domains and create silos that complicate data sharing and collaboration.

In conclusion, the Medallion model is not a one-size-fits-all solution. However, it remains one of the most practical and adaptable design patterns for structuring AI-ready, analytics-grade data pipelines—especially in complex, federated, and continually growing environments.


To explore the concepts in more depth, check out my book:
Building Medallion Architectures: Designing With Delta Lake and Spark (O’Reilly, 2025)

 

Or watch the full replay of the webinar:
The Big Medallion Architecture Debate
With Ole Olesen Bagneux, Actian Chief Evangelist


Blog | Databases | | 4 min read

Securing Your Data With Actian Vector, Part 3

securing your data with Actian Vector

Summary

  • Actian Vector uses 256-bit AES encryption for strong data security.
  • PBKDF2 derives secure keys from user-defined passphrases.
  • Main, database, and table keys protect different data layers.
  • Keys are encrypted, rotated, and stored securely in memory.
  • Ensures scalable, multi-layered encryption for database protection.

Following up on my second blog post about Actian Vector’s functional encryption capabilities, this next blog post in the series on data security explains the different encryption keys and how they are used in Actian Vector.

Understanding Different Encryption Keys

The encryption method generally used in Actian Vector is the 256-bit variant of the Advanced Encryption Standard (AES). AES requires an encryption key, and for 256-bit AES, this key is 256 bits long. A longer key means better security. Currently, 256-bit AES is considered “secure enough” and 256 bits is the maximum key length defined for AES.

In Actian Vector, database encryption is a major use case of encryption. As described earlier, its implementation uses different encryption keys for different pieces of data. Besides database encryption, there are also other uses for encryption in the database server, and therefore, still different keys are used for these. And all these encryption keys must be secured.

To provide sufficient security, encryption keys must not be easy to guess. Therefore, encryption keys are usually randomly generated. This makes them secure, but difficult to remember (few people can easily remember a sequence of 32 random bytes). A common solution is to protect encryption keys with a passphrase, where a prudently chosen passphrase can be sufficiently secure but still easy enough to remember.

Still, it would not be safe to use a passphrase directly as an encryption key. Instead, an encryption key gets derived from the passphrase, and advanced algorithms exist for this derivation process to make sure that a secure enough key is the result.

Actian Vector uses Password-Based Key Derivation Function 2 (PBKDF2) for this purpose. PBKDF2 is part of the RSA Laboratories’ Public-Key Cryptography Standards series. This illustration shows the process:

Structure of encryption keys used for encryption at rest

How Keys are Generated, Secured, and Used for Database Encryption 

An individual “main key” is randomly generated for each database. To store the main key securely, it is encrypted with the “protection key.” This protection key results from the passphrase being processed by the PBKDF2 algorithm. The protection key does not need to be stored anywhere because its derivation from the passphrase can be repeated whenever the protection key is needed.

The “database key” for the database encryption is then derived from the main key. Because the main key is already randomly generated, an internal method that is based on a Secure Hash Algorithm (SHA) derives a sufficiently random database key without the need for the more complex PBKDF2 algorithm.

Likewise, other keys for different purposes are derived from the main key. These derived keys are not persisted, but they are kept in memory only. On the other hand, the encrypted main key is persisted, but the decrypted main key is only needed to derive the database key and other keys. Afterwards the decrypted main key is removed from memory.

The database key is used to encrypt and decrypt the container of the individual “table keys.” These table keys are randomly generated for each table and used to (finally) encrypt and decrypt the user data in tables and indexes. Because the table keys are randomly generated, they also need to be persisted and with that, secured by encrypting them with the database key. The container where the table keys are stored also contains other metadata for the database, and therefore it is also secured by encrypting the whole container, rather than individually encrypting just the table keys.

The database administrator can change the passphrase for a database, as well as rotate the main key or individual table keys. I’ll share more on this in later blog posts on key management.

Explore Other Blogs on Securing Your Data With Actian Vector:


Blog | Awards | | 3 min read

Actian Earns Top Marks in ISG Buyers Guide™ for Data Platforms

ISG Research Buyers Guide 2025 - Actian Exemplary Winner for Data Platforms

Summary

  • Actian named “Exemplary” in ISG Buyers Guide for Data Platforms.
  • Recognized across analytic, operational, and overall categories.
  • Leads in manageability and customer validation.
  • Strong performance in usability, reliability, and TCO.
  • Highlights Actian’s innovation and enterprise-grade capabilities.

ISG Software Research, a global analyst research firm, has recognized Actian for the second consecutive year. 

The latest ISG Buyers Guide™ for Data Platforms highlights Actian’s impressive standing in the crowded and competitive data platform market. The annual report, produced by global technology advisory leader ISG, rigorously assesses the market’s top data platform providers, serving as a crucial resource for enterprise buyers navigating solution choices by helping them make informed buying decisions. 

What truly sets Actian apart is a forward-looking roadmap. Actian is continuously evolving its product portfolio to solve both current and future data challenges, ensuring customers can meet today’s demands while preparing for tomorrow’s opportunities. From scaling data trust to enabling data intelligence and supporting AI readiness, Actian’s commitment to innovation ensures that organizations not only keep pace with change but also stay ahead of it.

Actian’s Noteworthy Placement

Actian has been recognized as an “Exemplary” performer in all three major categories evaluated by ISG: 

  • Data Platforms (Overall)
  • Analytic Data Platforms 
  • Operational Data Platforms 

This coveted placement recognizes Actian as not just a leading solutions provider, but a best-in-class vendor for both product and customer experience. In a marketplace that includes tech giants such as Oracle, Microsoft, AWS, Google Cloud, and SAP, this is a significant recognition of the Actian Data Platform’s capabilities and client value, as well as Actian’s approach to innovation.

Leading in Manageability and Customer Validation

ISG’s assessment model uses a robust methodology, scoring vendors on seven distinct dimensions. Within this framework, Actian stands out as a Leader in two key evaluation criteria for both Data Platforms and Operational Data Platforms:

  • Manageability: Reflecting Actian’s strength in enabling seamless platform operation, governance, and support for complex enterprise IT needs.
  • Validation: Recognizing Actian’s ability to deliver tangible business value and foster robust customer relationships through every stage of the client lifecycle. 

These distinctions are especially notable given the technical demands and mission-critical nature of modern data platforms.  

Beyond Actian’s leader status in Manageability and Validation, Actian secured strong marks across the full spectrum of ISG’s criteria, including adaptability, capability, reliability, usability, and total cost of ownership (TCO).

Why These Findings Matter

ISG’s Buyers Guides are known for their objectivity, depth, and focus on real enterprise requirements; badges and rankings are not influenced by vendor marketing or participation alone. Actian’s “Exemplary” placement is a direct result of its technical merit, customer validation, and depth of platform features.

Actian’s performance in the 2025 ISG Buyers Guide™ for Data Platforms validates its status as a well-rounded, enterprise-grade data platform provider, offering businesses an alternative to legacy incumbents without compromise on features, reliability, or value. Unlike legacy solutions that often lock organizations into rigid systems and high costs, Actian takes a more agile and customer-centric approach. Our platform is designed for flexibility and scalability, helping enterprises innovate without the complexity or constraints of traditional providers.

Ready to see what sets Actian apart? Learn more about the Actian Data Platform and discover the innovation and results that have driven Actian’s ongoing industry recognition. Empower your business with the performance, flexibility, and real-world value that leading enterprises rely on. 


Blog | Data Intelligence | | 6 min read

Federated Knowledge Graph: The Missing Link in Your Data Strategy

federated knowledge graph hero

Summary

  • A federated knowledge graph links metadata to provide context and a trusted framework for data integration and discovery.
  • Unlike rigid data catalogs, this approach allows individual domains to maintain their own terminology and governance standards.
  • It acts as an internal search engine that reflects the unique, real-life structure of an organization rather than a static model.
  • The graph serves as a foundational asset for AI, providing the logic and reasoning needed for context-aware recommendations.

When it comes to using data, every organization is striving to achieve the same goal—find and access the right data quickly, and trust that it’s accurate. It sounds like a process that should be simple, but too often, it’s not.

A federated knowledge graph offers value for every data user, making data readily accessible, trusted, and usable. It gives each business domain its own view of your organization’s overall data universe, without sacrificing consistency, security, or collaboration. It’s powerful. It’s flexible. And it enables smarter, safer, faster decision-making across your enterprise by making the right data easy to access.

What is a Federated Knowledge Graph?

In the “Hype Cycle for Artificial Intelligence, 2024,” Gartner notes that the two biggest movers on the cycle are AI engineering and knowledge graphs. “Knowledge graphs are machine-readable representations of the physical and digital worlds,” according to Gartner. “They capture information in a visually intuitive format, yet are still able to represent complex relationships. More importantly, they provide dependable logic and explainable reasoning (as opposed to GenAI’s fallible but powerful predictive capabilities).”

You can think of a federated knowledge graph as your company’s internal Google search engine for data, only smarter. At Actian, we define the knowledge graph as a data structure representing a universe of knowledge through a collection of interlinked concepts, entities, relationships, and events. By linking semantic metadata, a federated knowledge graph provides context to data and offers a trusted framework for data integration, data discovery, and governance.

By using a federated knowledge graph, you benefit from a dynamic, interconnected view of your metadata that reflects how your business actually works. Unlike traditional data catalogs that impose a rigid, static metamodel, which is essentially a preconceived idea of how your organization should be structured, a federated knowledge graph evolves with your business and can be designed to meet your organization’s unique and real-life structure.

Each domain within your organization, such as marketing, finance, and product development, can maintain its own localized knowledge graph that reflects its specific language, workflows, and standards. These “sub-graphs” are interconnected, allowing users to discover and use data across your organization while also having domain autonomy.

In other words, it’s a modern system that respects how your teams operate while ensuring your organization’s data products are discoverable, accessible, and usable at scale.

Why Traditional Data Catalogs are not Effective

Many legacy data platforms use centralized governance models and monolithic architectures that limit flexibility and scalability. These approaches require rigid structures, strict and sometimes lengthy approval processes, and central data ownership.

These models typically require companies to restructure their data to fit the technology, instead of the other way around. That leads to many drawbacks, such as:

  • Low adoption rates. Users don’t understand or trust a system that doesn’t reflect their day-to-day reality, so they don’t use or fully optimize the platform.
  • Data disconnects. Business domains are forced to speak a data language that doesn’t align with their own, creating confusion and limiting collaboration.
  • Costly delays. Making even minor changes, such as updating data definitions, adding new sources, or adjusting to evolving business needs, is time-consuming and expensive.

And worst of all? These static systems often describe a version of your business that doesn’t actually exist in reality, leaving teams to make decisions based on outdated or irrelevant representations of how your organization truly operates.

Why a Federated Approach Works Better

A federated knowledge graph flips the traditional, static model on its head. It enables a more natural, human-centric way of working with data. Instead of forcing teams into a one-size-fits-all structure, it lets each domain:

  • Define its own terminology, KPIs, and governance policies.
  • Maintain its own data catalog and metadata.
  • Build data products using their own standards.
  • Share data seamlessly through governed data contracts.

For example, the federated knowledge graph that powers the Actian Data Intelligence Platform delivers rich, in-depth, contextual search results. It gives you visibility along with a detailed understanding of your enterprise data landscape. In addition, it automatically identifies, classifies, and tracks data based on contextual factors, mapping your data products to key concepts to make them easily discoverable and accessible.

So, what does a federated knowledge graph look like in action? Here are a few examples:

  • Compliance teams define sensitive data policies in their knowledge graph, while clinical teams curate their own operational data products. The two can share information securely through governed contracts without compromising privacy or integrity.
  • The marketing team defines terms and customer segments in its business glossary for a new campaign, while the finance team defines revenue and margin metrics. Each domain maintains its own universe of meaning for data, yet the federated graph connects the dots for executive reporting and recommendations.
  • Various departments such as risk, audit, and sales each maintain their own views of critical data. The federated knowledge graph ensures traceability and data lineage for compliance, while enabling agile data exploration and product recommendations for sales teams.

Decentralized data management can still result in enterprise-wide harmony when it’s powered by the right technology. A modern federated knowledge graph offers a smarter way to align teams, delivers trusted data products faster, and drives tangible business outcomes.

Supporting Data Intelligence in the AI Era

The federated knowledge graph is more than a tool for today’s data needs. As our Chief Evangelist Ole Olesen-Bagneux explains in a short video, our federated knowledge graph is an ontological treasury for generative and agentic AI.

Because it captures rich metadata and relationships across domains, the knowledge graph provides the perfect foundation for:

  • Intelligent, context-aware recommendations.
  • Enhanced semantic search across business glossaries.
  • Greater explainability and transparency in outputs.
  • Enabling modern data governance.
  • A holistic view of data for a better understanding and optimization.

In a world where AI readiness depends on the quality of your metadata, a federated knowledge graph is your best strategic asset. And it’s an area where Actian can help. Actian Data Intelligence Platform is built on a federated knowledge graph that makes scaling enterprise-wide governance and empowering business users simple and intuitive.

If you’re looking to modernize your data strategy and accelerate your journey to AI-readiness, don’t just implement another data catalog. Federate your knowledge. Take a product tour today to experience data intelligence powered by a federated knowledge graph.


Blog | Actian Life | | 9 min read

Celebrating Our Future Leaders: Actian Interns Make an Impact

national intern day

Summary

  • Actian interns gain hands-on experience on real-world projects.
  • Work spans AI, data intelligence, security, and product innovation.
  • Program emphasizes mentorship, ownership, and collaboration.
  • Interns contribute meaningful business value across teams.
  • Experience builds skills, networks, and future career opportunities.

In honor of National Internship Day on July 31st, we’re proud to spotlight the incredible contributions of our Actian interns.

At Actian, internships go beyond basic tasks. They offer hands-on experience, professional growth, and opportunities to deliver ongoing business value. Our interns build real-world skills, make meaningful connections, and drive tangible business outcomes.

Whether it’s optimizing product performance, shaping go-to-market strategies, or exploring cutting-edge AI innovations, these interns are tackling projects that matter to the business, customers, and partners.

Get to know these talented individuals who are making a difference today and shaping the future of technology:

 

Shira Cohen

Shira Cohen

Majoring in Information Systems and Management at the University of Maryland, Cohen’s internship is focused on supporting and optimizing the web presence for the Actian Data Intelligence Platform.

“My favorite takeaway from my internship at Actian is that even in a remote setting, it’s the people and culture that truly make a work experience great,” Cohen said. “Being surrounded by talented, supportive, and genuinely kind individuals has made a lasting impact, and I’m incredibly grateful for the opportunity to work with such an amazing team.”

What friends and coworkers may not know is how Cohen views the ocean. “The ocean is both my favorite place and my biggest fear,” Cohen shared.

 

Jack Donahoo

Jack Donahoo

Hailing from Texas Tech University and majoring in Computer Engineering, Donahoo is working on data intelligence during the Actian internship. Like others in the internship program, Donahoo enjoyed the bonding experience during the onboarding process.

“My favorite memory was getting to meet all of the other interns and doing the group activities together, such as the Legos and building skateboards,” Donahoo explained.

Outside of work, Donahoo is racking up airline miles. “I am part German, so I travel to Europe as much as I can,” Donahoo said.

 

Shatoria Giles

Shatoria Giles

Pursuing a major in Software Engineering in Computer Science at Southern New Hampshire University, Giles is supporting the user interface at Actian and helping drive conversions. One of Giles’ favorite aspects of the internship is centered on an app.

“Getting to work on a large-scale production app is great,” Giles said. “I also got to study while getting paid!”

When not working or studying, Giles attends Sloss Tech meetings and spends time online. “I am active on LinkedIn and have a website I manage called shatoria.org,” Giles explained.

 

Dawit Girma

Dawit Girma

A student at the Massachusetts Institute of Technology, Girma is majoring in Computer Science and Engineering. During the Actian internship, Girma is finding ways to use KEDA,  a Kubernetes-based component that scales containers, to auto scale an application using RabbitMQ metrics as a trigger.

Bonding with other interns has been a highlight for Girma. “The onboarding week was so fun! I loved meeting the interns and doing activities together in and out of the office,” Girma explained. “Out of all of the activities, painting the skateboards might have been the most fun moment for me!”

Girma may have bragging rights when it comes to a series of basketball video games. “I am very good at NBA 2K, especially the Park mode,” Girma said.

 

Hardik Kaushik

Hardik Kaushik

Actian internships are not limited to the United States. Kaushik studies at the Technical University of Munich with a major in Management and Digital Technology. Kaushik helps give Actian a competitive edge by contributing to market research, battlecard creation, and competitor research.

“I am learning a lot about how to properly conduct market and competitor research based on different variables,” Kaushik related.

People may not know that Kaushik has a unique memory capability. “Though not proven but to some extent I feel I have Hyper Active Selective Memory (HASM),” Kaushik said. “I remember random events from a decade ago, down to minute details for no apparent reason.”

 

Iyu Lin

Iyu Lin

Representing the West Coast from the University of California, Berkeley, Lin is majoring in Information Management and Systems. Lin’s internship is exploring how Actian can implement large language models (LLMs) to improve the documentation user experience.

“I’ve been comparing chatbot approaches used by competitors and building prototypes with different LLMs to evaluate their responses,” Lin said.

The support and flexibility throughout the internship have been important. “I’ve had the freedom to try out my ideas, and my mentor always listens and provides thoughtful feedback,” Lin shared. “I’ve also received help and encouragement from people across different teams, which made the experience even more meaningful!”

Those who like boba tea will appreciate Lin’s drink of choice. “I’m a big fan of boba tea,” Lin revealed. “I can happily drink at least one cup a day!”

 

Sai Kalyan Maram

Sai Kalyan Maram

Majoring in Information Technology at the University of New Hampshire, Maram is working on enhancing the Actian Community search experience. This entails using Coveo Quantic and Lightning Web Components (LWC) within Salesforce.

The project involves integrating AI-powered search and creating relevance-tuned pipelines using machine learning models like CRGA and SE. It also includes redesigning the user interface to improve user engagement.

“My favorite takeaway is how much trust and ownership I was given as an intern. I wasn’t just doing small tasks. I was solving real problems impacting our global users,” Maram said. “One memorable moment was getting recognition from the internal team and Coveo support for diagnosing and resolving a major issue with query redirection. It taught me the value of persistence, communication, and cross-team collaboration.”

Two fun facts about Maram are the use of AI for side projects and a love of traveling. “I’m building an AI-powered platform on the side that helps real estate agents automate lead management and property insights,” Maram noted. “Also, I love long road trips, especially when I’m the one behind the wheel!”

 

kelsey mulroneyKelsey Mulrooney

With a major in Cybersecurity and attending the Rochester Institute of Technology, Mulrooney’s internship focuses on helping Actian deploy a security tool called Armo.

“I’m reviewing findings in Armo to evaluate and create tickets and compliance scripts,” Mulrooney said. “I’m gaining hands-on experience with Argo CD, Kubernetes, and Kubernetes Security.”

One highlight of the internship was meeting others at the Actian office. “My favorite memory is the internship orientation in Round Rock, Texas,” Mulrooney explained. “It’s always great meeting the other interns and kicking off the summer together.”

People may be surprised to know about Mulrooney’s additional skills. “I am a percussionist and a figure skater!” Mulrooney revealed.

 

Bhoomi Saraogi

Bhoomi Saraogi

Attending New York University with a major in Economics and a minor in Data Science and Marketing, Saraogi is creating and supporting a direct-mail account-based marketing (ABM) campaign as part of the Actian internship.

“I’m working on the design and copy of my really creative and fun campaign idea, and chatting with my manager,” Saraogi said.

Highlights of the internship so far include orientation, bowling with peers in Austin, and being surprised by birthday goodies that Actian sent. One highlight outside of work and school is going on the Formula Rosse rollercoaster at Ferrari World in Abu Dhabi.

“I’ve been on the world’s fastest roller coaster,” Saraogi said.

 

Ashmit ThakurAshmit Thakur

The Electrical Engineering / Computer Science major at Texas A&M is working on performance and stress testing with Grafana k6, an open-source, developer-friendly, extensible load testing tool.

“One of my favorite memories so far has been the intern dinners we shared after work. It was great to unwind and connect with my fellow interns outside of the office, whether over a meal or during fun activities like bowling,” Thakur explained. “Overall, the entire first week of the internship was an absolute blast and I really appreciate Logan Lou and Rae Coffman-Bueb on Actian’s Employee Experience team for making that happen.”

As a sports enthusiast, Thakur is interested in playing and watching basketball.

“One fun fact about me is that I love basketball and have been playing the sport for a majority of my life,” Thakur noted. “I’m also a huge Houston Rockets fan, so watching the next season of the NBA is definitely going to be a lot of fun.”

 

Christy Yao

Christy Yao

Pursuing a Master’s Degree in Integrated Innovation for Products and Services at Carnegie Mellon University, Yao is optimizing AI during the Actian internship.

“I am working on AI features that deliver a cohesive, intuitive experience through consistent design patterns, engaging interactions, and user-centered functionality,” Yao said.

The work is both rewarding and challenging. “One of my favorite takeaways from this internship is how much I’ve learned by challenging myself on the front lines of evolving AI technology while collaborating with a design-driven team that constantly pushed me to grow.” Yao explained.

Yao has a favorite shopping experience. “I’m a big fan of Trader Joe’s!” Yao shared. “I could probably give a full tour of the store and love introducing every new seasonal item to my friends.”

 

Juana Zhang headshotJuana Zhang

A Brandeis University student majoring in Business Analytics, Zhang is contributing to Actian’s CX user analytics during the internship.

“I gained an in-depth understanding of the platform and conducted an initial user segmentation based on data analysis,” Zhang said. A favorite moment during the internship was working on gifts for kids.

“During the first week, we hand-painted and crafted skateboards to give to children,” Zhang said.

One interesting fact about Zhang is the impressive academic resume. “I completed my three degrees in three different countries,” Zhang said.

 

Ready to Build a Future at Actian?

At Actian, internships are just the beginning. Whether a college student is looking to experience a unique internship or someone is looking to take the next step in their career, Actian offers opportunities to grow skills, make a difference, and work alongside supportive, innovative teams.

Actian offers an award-winning workplace that values an employee-first culture. Explore current openings and learn more about life at Actian on our Careers page.


Blog | AI & ML | | 5 min read

Inside Actian’s Research: Why Data Governance Threatens AI Success

data governance maturity model

Summary

  • 600+ leaders reveal gaps between perceived and actual data maturity.
  • 83% face governance and compliance challenges impacting AI success.
  • Strong governance is critical for trusted, AI-ready data.
  • Barriers include strategy gaps, skills shortages, and data culture.
  • Confidata Index helps benchmark and improve data & AI maturity.

What 600 enterprise data leaders revealed about maturity gaps, AI readiness, and what to do next

This article highlights key insights from Actian’s global data governance study and introduces the Actian Confidata Index, a free assessment for benchmarking your organization’s data and AI maturity.

In today’s AI-driven economy, data maturity is no longer optional. It’s foundational. Yet, according to the Actian State of Data Governance Maturity 2025 research, most enterprise organizations significantly overestimate the maturity of their governance practices. And that overconfidence is putting AI investments at risk.

To better understand where enterprises truly stand, Actian surveyed over 600 senior data, IT, and business leaders from organizations earning more than $1 billion in annual revenue, spanning:

  • 7 countries (U.S., U.K., Germany, France, India, Sweden, and Australia).
  • 12 industries (including financial services, manufacturing, technology, healthcare, retail, and energy/utilities).
  • 4 seniority levels (C-suite, VPs, directors, and managers).
  • 3 business functions (Data/Analytics, IT/Technology, and Line-of-Business).

What we uncovered is both revealing and urgent: while most enterprises believe their data governance maturity is high, the reality paints a more fragmented, fragile picture, especially as pressure mounts to scale AI.

A Confidence Gap With Consequences

At a glance, the results seem promising. Respondents gave themselves a high maturity rating, averaging 4.13 out of 5. But dig deeper, and a troubling disconnect emerges:

  • 83% of organizations admit to facing governance and compliance challenges.
  • C-suite leaders rate maturity 12% higher than frontline managers, who experience the issues daily.
  • Data governance ranks as the least mature of eight foundational data dimensions.

This gap has consequences. When governance is overestimated, organizations may launch AI initiatives based on incomplete, untrusted, or non-compliant data, increasing the risk of failure, legal exposure, and reputational harm.

Why Data Governance is the Differentiator for AI

The research highlights the truth that governance isn’t just about compliance: it’s the critical link between enterprise data and AI success. 

Respondents said strengthening governance would directly lead to:

state of data governance maturity outcomes

Top 5 expected outcomes of better data governance – Actian Global Research 2025

 

The message is clear: AI adoption is a key catalyst for governance improvements, but organizations understand that AI success is impossible without a strong data foundation. Respondents ranked data quality and trust as equally critical to AI enablement, underscoring that governance maturity is the differentiator between AI-driven growth and AI failure.

Additionally, governance improvements are seen as a pathway to faster market execution and better business insights, proving that governance is not just a compliance necessity—it’s a competitive advantage.

Beyond Tech: Strategy, Skills, and Culture Remain the Real Barriers

The research also reveals that data management challenges go beyond technical barriers. The top five challenges holding organizations back are deeply organizational and cultural in nature:

state of data governance maturity obstacles

Top 5 data management obstacles – Actian Global Research 2025

 

The fact that AI and data strategy rank as top challenges reveals that organizations lack clear roadmaps to integrate governance with innovation. Meanwhile, the shortage of skilled staff and weak data literacy hinder adoption and impact. 

Without a holistic, integrated approach that combines people, processes, and technology, governance efforts risk fragmentation and inability to support AI-driven transformation.

Who’s Governing the Data?

While highly centralized governance remains dominant, over half of organizations are exploring center-led and decentralized models to strike a balance between consistency and agility. 

47 percent highly centralized data governance

Enterprise approaches to data governance – Actian Global Research 2025

 

However, successful governance isn’t just about structure, but also about execution. Organizations transitioning to more federated models must invest in training, standardized processes, and stronger cross-functional collaboration to ensure scalable and impactful governance.

Introducing the Actian Confidata Index

To help organizations bridge the gap between perception (overconfidence) and reality, we launched the Actian Confidata Index—a free, research-backed self-assessment tool.

data maturity model chart

The five data & AI maturity categories – Actian Confidata Index

 

Built on the same eight-dimensional framework used in our global research, the Actian Confidata Index enables enterprises to:

  • Benchmark Data & AI Maturity against 600+ industry professionals.
  • Identify strengths and gaps across eight core dimensions.
  • Receive a personalized report with recommendations to accelerate AI adoption and business value.

Those eight dimensions include:

  • Data Strategy – Alignment with business and AI goals.
  • Data Governance – Accountability, compliance, and risk frameworks.
  • Data Culture & Organizational Readiness – Literacy and adoption.
  • Data Management – Structuring, securing, and governing assets.
  • Data Architecture & Integration – Interoperability and scalability.
  • Data Quality & AI Governance – Ensuring AI-ready data.
  • Data Operations – Lifecycle efficiency and agility.
  • Value Realization – Turning data into measurable business outcomes.

data maturity model spider chart

Benchmark against data leaders on the eight dimensions – Actian Confidata Index

 

Whether your team is just getting started or already operating at scale, the Actian Confidata Index gives you a clear view of where you stand—and what to do next.

“Our global survey reveals a compelling paradox: AI acts as both a critical driver and a primary challenge for data governance. The Actian Confidata Index helps organizations find a starting point with an assessment and clear next steps to build strong data foundations and governance frameworks that enable AI success.” 

Emma McGrattan, CTO of Actian

Take the Free Confidata Index Assessment

The bottom line? You can’t succeed at AI without strong governance, and you can’t improve governance without first understanding where you are today.

Benchmark your data and AI maturity. Get personalized recommendations.

Take the Free Assessment