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Jul 23 2025

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.

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 […]

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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.

Freshness: Is the data up to date?
Volume: Is the expected amount of data present?
Schema: Have there been any unexpected changes to the data structure?
Lineage: Where does the data come from, and how does it flow across systems?
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.

Threshold Alerts: Notifications triggered when data deviates from expected norms.
Dashboards: Visual interfaces showing system performance and data health metrics.
Log Collection: Capturing event logs to track errors and system behavior.
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:

Establish Monitoring First. Track uptime, failures, and thresholds.
Introduce Observability. Add deeper diagnostics like data lineage tracking, quality checks, and schema drift detection.
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.

Understanding Structural Metadata

Today, organizations and individuals face an ever-growing challenge: the sheer volume of data being generated and stored across various systems. This data needs to be properly organized, categorized, and made easily accessible for efficient decision-making. One critical aspect of organizing data is through the use of metadata, which serves as a descriptive layer that helps users understand, find, and utilize data effectively.

Among the various types of metadata, structural metadata plays a crucial role in facilitating improved data management and discovery. This article will define what structural metadata is, why it is useful, and how the Actian Data Intelligence Platform can help organizations better organize and manage their structural metadata to enhance data discovery.

What is Structural Metadata?

Metadata is often classified into various types, such as descriptive metadata, administrative metadata, and structural metadata. While descriptive metadata provides basic information about the data (e.g., title, author, keywords), and administrative metadata focuses on the management and lifecycle of data (e.g., creation date, file size, permissions), structural metadata refers to the organizational elements that describe how data is structured within a dataset or system.

In simpler terms, structural metadata defines the relationships between the different components of a dataset. It provides the blueprint for how data is organized, linked, and formatted, making it easier for users to navigate complex datasets. In a relational database, for example, structural metadata would define how tables, rows, columns, and relationships between entities are arranged. In a document repository, it could describe the format and organization of files, such as chapters, sections, and subsections.

Key Features of Structural Metadata

Here are some key aspects of structural metadata:

Data Relationships: Structural metadata defines the relationships between data elements or files within a dataset. For instance, in a relational database, it describes how tables are linked through keys or indexes, and how columns relate to each other within the same table.
Data Formats and Types: It specifies the data formats used (e.g., text, numeric, date) and helps identify the data types of each element. In data warehousing, structural metadata defines the schema, such as whether data is stored in star or snowflake schema.
Hierarchical Organization: It outlines how data is organized hierarchically or sequentially, such as parent-child relationships between datasets or subfolders within a directory structure.
Data Integrity and Constraints: Structural metadata often includes information on constraints like field lengths, data validation rules, and referential integrity, ensuring that the data is consistent and follows predefined rules.
Access and Navigation: This metadata helps users understand how to access and navigate large datasets by providing information about where and how data is located, allowing for efficient querying and retrieval.

Why is Structural Metadata Important?

Structural metadata plays a fundamental role in ensuring that data is understandable, accessible, and usable. Here are several reasons why it is essential:

Data Discovery and Access: Structural metadata enables users to locate and understand data more efficiently. By understanding how data is organized and the relationships that exist between various components, users can easily navigate large datasets to find relevant information without having to sift through each individual data element.
Data Quality and Consistency: When the structure of a dataset is clearly defined through metadata, it ensures that data is consistently formatted and follows specific rules. This consistency helps maintain data quality and reliability, which is crucial for analysis and decision-making.
Improved Data Integration: Organizations often deal with data spread across different systems, platforms, and applications. Structural metadata facilitates integration by defining how disparate data sources are connected or how they should interact. It helps in joining datasets correctly, enabling better cross-platform analytics.
Data Governance and Compliance: In regulated industries, where data must meet specific legal or industry standards, structural metadata ensures that data complies with necessary rules and regulations. It makes audits, data governance practices, and compliance checks easier and more transparent.
Efficient Querying and Analytics: In analytics and business intelligence tools, having a well-structured dataset enables more efficient querying of data. Structural metadata allows for faster retrieval of data, helping analysts and business users generate insights quickly and accurately.
Enhanced Data Management: By providing clear definitions of data formats, relationships, and constraints, structural metadata streamlines the process of managing, updating, and maintaining datasets. It reduces errors and minimizes the risk of misinterpreting data, which can lead to faulty conclusions.

Challenges of Managing Structural Metadata

Despite its importance, managing structural metadata is not without challenges.

Complexity: As datasets grow and become more complex, it can be difficult to keep track of all the different relationships, hierarchies, and formats. Large organizations may struggle to maintain a consistent structure across numerous datasets.
Data Silos: In many organizations, data is stored in separate systems or applications, each with its own metadata standards. This can create silos where data is not easily discoverable or usable across departments or platforms.
Lack of Standardization: Without a standardized approach to metadata management, organizations may struggle to consistently define structural metadata across their datasets. This inconsistency can lead to confusion and errors, hindering data integration and analysis efforts.
Scalability: As organizations continue to generate more data, the challenge of managing structural metadata at scale becomes more pronounced. This requires robust tools and systems capable of handling increasing volumes of metadata efficiently.

How Actian Can Help Organize and Manage Structural Metadata for Better Data Discovery

The Actian Data Intelligence Platform provides organizations with the tools to handle their metadata efficiently. By enabling centralized metadata management, organizations can easily catalog and manage structural metadata, thereby enhancing data discovery and improving overall data governance. Here’s how the platform can help:

1. Centralized Metadata Repository

The Actian Data Intelligent Platform allows organizations to centralize all metadata, including structural metadata, into a single, unified repository. This centralization makes it easier to manage, search, and access data across different systems and platforms. No matter where the data resides, users can access the metadata and understand how datasets are structured, enabling faster data discovery.

2. Automated Metadata Ingestion

The platform supports the automated ingestion of metadata from a wide range of data sources, including databases, data lakes, and cloud storage platforms. This automation reduces the manual effort required to capture and maintain metadata, ensuring that structural metadata is always up to date and accurately reflects the structure of the underlying datasets.

3. Data Lineage and Relationships

With Actian’s platform, organizations can visualize data lineage and track the relationships between different data elements. This feature allows users to see how data flows through various systems and how different datasets are connected. By understanding these relationships, users can better navigate complex datasets and conduct more meaningful analyses.

4. Data Classification and Tagging

The Actian Data Intelligence Platform provides powerful data classification and tagging capabilities that allow organizations to categorize data based on its structure, type, and other metadata attributes. This helps users quickly identify the types of data they are working with and make more informed decisions about how to query and analyze it.

5. Searchable Metadata Catalog

The platform’s metadata catalog enables users to easily search and find datasets based on specific structural attributes. Whether looking for datasets by schema, data format, or relationships, users can quickly pinpoint relevant data, which speeds up the data discovery process and improves overall efficiency.

6. Collaboration and Transparency

Actian’s platform fosters collaboration by providing a platform where users can share insights, metadata definitions, and best practices. This transparency ensures that everyone in the organization is on the same page when it comes to understanding the structure of data, which is essential for data governance and compliance.

7. Data Governance and Compliance

Using a federated knowledge graph, organizations can automatically identify, classify, and track data assets based on contextual and semantic factors. This makes it easier to map assets to key business concepts, manage regulatory compliance, and mitigate risks.

Get a Tour of the Actian Data Intelligence Platform Today

Managing and organizing metadata is more important than ever in the current technological climate. Structural metadata plays a crucial role in ensuring that datasets are organized, understandable, and accessible. By defining the relationships, formats, and hierarchies of data, structural metadata enables better data discovery, integration, and analysis.

However, managing this metadata can be a complex and challenging task, especially as datasets grow and become more fragmented. That’s where the Actian Data Intelligence Platform comes in. With Actian’s support, organizations can unlock the full potential of their data, streamline their data management processes, and ensure that their data governance practices are aligned with industry standards, all while improving efficiency and collaboration across teams.

Take a tour of the Actian Data Intelligence Platform or sign up for a personalized demonstration today.

The post Understanding Structural Metadata appeared first on Actian.

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 […]

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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 […]

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Author: Subasini Periyakaruppan

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Jul 16 2025

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 […]

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, […]

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 […]

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 […]

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.

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 […]

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