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Oct 1 2025

Stop Feeding AI Junk: A Systematic Approach to Unstructured Data Ingestion

It’s go-time for enterprise AI. A PagerDuty global survey of 1,000 IT and business executives found that 62% of companies using agentic AI expect a return of 171% on average. But getting to ROI is no easy task. Recent surveys show mixed results on efforts thus far, with “getting strategies right” and “making data ‘AI-ready’” […]

Rethinking (Data) Politics in the Workplace

Most people cringe when they hear the word politics in the workplace. It brings to mind backroom deals, favoritism, turf wars, and decision-making that feels more about power than about progress. In the world of data, politics often gets blamed for blocking change — departments hoarding information, leaders fighting over priorities, and executives struggling to […]

The Good AI: Data Contracts for AI Transparency

“AI is only as trustworthy as the data that fuels it.” This statement has never been more relevant. AI systems now power decisions, affecting credit approvals, medical diagnoses, fraud detection, and countless other critical areas. Yet without transparency into data sources, quality, and lineage, AI can quickly become a black box — opaque, unpredictable, and […]

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Author: Subasini Periyakaruppan

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Oct 1 2025

Strengthening Compliance with Better Data

Compliance today isn’t just about keeping pace with rules and regulations; it’s about keeping pace with culture. Globalization, geopolitical uncertainty, and rapid shifts in technology mean the risks companies face are more complex than ever. Yet too many organizations are still relying on legacy systems, outdated processes, and once-a-year, check-a-box training to protect their people […]

The Book Look: Data Interoperability

Dave Wells is a thought leader and influencer in the world of data. Early on in his role as TDWI education director — in the wild frontier days of data warehousing — he selected some of the data movers and shakers, such as Bill Inmon and Ralph Kimball, to present at world conferences. Dave played […]

LLMs Are Rewriting the Rules of Behavioral Targeting

Marketers missed the real story while they scrambled to rebuild audience targeting without cookies: LLMs weren’t just changing how we write. They were rewriting the rules of how we understand consumer behavior online. The death of the third-party cookie was supposed to kill behavioral targeting. Instead, it’s about to become a lot smarter. The New […]

Customer MDM (Master Data Management)

Unlocking the Single Customer View to Drive Hyper-Personalization and Agentic AI Success

You’ll hear the term “experience economy”, and what this really means, is that modern-day business success is fundamentally driven by the quality and personalization of the interactions and experiences delivered to customers.

For Customer Master Data Management (Customer MDM) the implications are quite profound. This means that in order for a business to provide customers with distinctive ‘experiences’ to drive growth, maintain compliance obligations, and enable sophisticated digital strategies., the tools and methods need to go beyond IT projects.

Master Data Management (MDM) more generally refers to the combination of technologies, tools, and processes used to create a consistent, accurate, and complete set of master data across an organization, but Customer MDM focuses with laser precision, on supporting the business in mastering the data related to the “customer” entity specifically, more particularly in the Business-to-Consumer (B2C) and Direct-to-Consumer (D2C) contexts.

Effective Customer MDM (CMDM) focuses on transforming the fragmented customer account and the hidden data gaps often found in data silos, into a single, unified, powerful commercial asset. The ideal situation being one where it connects, multiple data masters, and consolidates data from various systems—including ERP, CRM, and ecommerce platforms—to establish a unified, reliable source of customer information for controlled distribution and use. This authoritative view is the foundation (Golden Records) for creating personalized experiences that customers expect and enabling advanced technologies like Agentic AI.

The Core Principle: Achieving the Single Customer View (SCV)

The primary goal of Customer MDM is to create the Single Customer View (SCV), also known as the 360° Customer View, or the customer Golden Record. Such a record consolidates the essential, business-critical information about a customer from every touchpoint and system across the enterprise. Without an SCV, organizations risk using inaccurate or incomplete data in crucial interactions, which can jeopardize new sales or existing relationships.

Integration and Consolidation

Master data management systems enable data integration by connecting to any data source, anywhere, bringing data together in one place. For customer data, this involves integrating records from omnichannel transactions, customer interactions, social media, and transactional systems. Multidomain MDM platforms are designed to connect customer data alongside other critical domains—like product and location—in a single environment. This ability to connect data across silos is crucial for gaining holistic insights.

Matching and Identity Resolution (Deduplication)

Fragmented customer data often often leads to duplicated customer accounts. MDM systems address this by applying sophisticated matching, reconciliation, and entity resolution processes to eliminate redundancy and identify relationships among data points. Modern MDM leverages Artificial Intelligence (AI) and machine learning to find and resolve matches at a massive scale, moving beyond the limitations of legacy systems. This automation ensures data is clean and consistent. The master record, once created, is maintained through the ongoing process of cleansing, transforming, and integrating new data to ensure continued consistency.

Specific Nuances of Customer Data Quality

Customer data is inherently dynamic, requiring specific MDM capabilities to ensure its trustworthiness in real-time. High-quality, reliable data is essential for improved business decisions and outcomes.

Real-Time Data Quality and Validation

For customer data, the quality of the data is of paramount importance for operational efficiency and a greater likelihood of customer satisfaction. MDM systems deploy data cleansing, standardizing, and enriching tools to turn “dirty data” into organized, reliable information. Specific capabilities for customer data include Address Validation and Real-Time Data Quality checks. This constant vigilance minimizes errors, such as transposing characters or incomplete fields, and corrects for different name usages (e.g., Jim vs. James).

Data Enrichment for Context

MDM platforms allow for the enrichment of customer records, often by integrating with external or third-party data sources (Data as a Service) to provide rich context to customer profiles. For B2B customer data, this enrichment includes Firmographic Data and information on Business Partner Relationships. In the B2C space, enrichment helps refine precise customer personas and segmentation.

Data Governance and Compliance: Building Trust

Master data management is inseparable from Data Governance, the discipline of establishing and enforcing policies, standards, and rules to ensure data is accurate, reliable, and compliant. For customer data, governance is especially critical due to privacy concerns and regulatory requirements.

Enforcing Customer Data Governance

Governance involves setting up cross-functional teams (data stewards) and defining clear policies and standards for how customer data should be managed, updated, and shared. MDM systems help establish and enforce these policies, ensuring data security and compliance with various regulations.

Privacy and Consent Management (Drawing on external knowledge beyond the provided sources)

Customer MDM must extend beyond basic quality to handle sensitive personal information. (Based on industry standards for customer data handling, not explicitly detailed in the provided sources, the following point is added for comprehensive coverage in line with the query’s request): A robust Customer MDM solution integrates consent and privacy preferences directly into the golden record. This allows organizations to manage customer choices regarding communication channels, data sharing, and regulatory mandates, ensuring that marketing, sales, and service activities are always performed with legal and ethical authority.

Strategic Impact: Powering AI and Customer Experience

The value of Customer MDM ultimately lies in its ability to deliver connected, insight-ready data in real-time, laying the foundation for modern business operations and advanced analytics.

Enabling Hyper-Personalization and Loyalty

By providing a unified view of customer history and preferences across channels, MDM dramatically improves customer service. This single source of truth allows businesses to create hyper-personalized experiences that foster lasting customer loyalty. Without consistent, trusted data, attempts at personalization are often flawed. A complete customer profile ensures that account managers have all necessary information—product ownership, service items, and correct contact details—to facilitate effective sales and service conversations.

Fueling Agentic AI and Digital Transformation

MDM is increasingly powered by AI and machine learning. Conversely, the output of MDM—the trusted golden record—is essential for the success of AI tools, LLMs, and agentic AI workflows.

Agentic AI systems rely on the authoritative context provided by any master data available. When AI agents are tasked with autonomously interacting with customers or making business decisions (e.g., pricing recommendations, service routing, personalized campaign execution), they must operate on the highest quality data available. MDM ensures that the data inputs for these systems are governance-ready, reducing the risk of flawed AI outputs and enabling responsible AI implementation.

Summary of Customer MDM Advantages

Implementing a comprehensive Customer MDM solution delivers significant advantages:

Drives Revenue Growth – By enabling hyper-personalized marketing and precise customer targeting.
Enhances Customer Relations – Ensuring consistent, accurate experiences across all touchpoints.
Improves Decision-Making – Providing a 360-degree view of the business to identify market trends and patterns.
Facilitates Compliance – Reducing risk by ensuring all customer data meets stringent regulatory standards.
Boosts Business Agility – Allowing the organization to respond quickly to market changes using real-time, standardized information.

Competitive advantage for business, hinges on the ability to know and serve the customer perfectly, Pretectum Customer MDM provides the necessary unified, connected, and accessible data to scale smarter, remain competitive, and future-proof the entire data strategy. #LoyaltyIsUpForGrabs

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Sep 17 2025

AI Governance Reaches an Inflection Point

A new survey of 1,250 data governance executives commissioned by OneTrust offers a detailed snapshot of how organizations are grappling with the realities of AI adoption. The findings are clear: Enterprise use of artificial intelligence has surged, but the governance structures required to manage it have not kept pace. As adoption accelerates, governance is no longer optional […]

Building a Data-First Culture

Technology is not what powers a data-first culture, but people, operating models, and disciplined delivery. Most organizations already possess more tools and data than they can effectively utilize. What differentiates the leaders is that they tie analytics to real business results, productize effective data, govern for speed and security, and, most importantly, rewire decisions. While […]

The Next Frontier in Enterprise IT: Agentic AI That Takes Responsibility

Despite years of experimentation, most organizations agree: Artificial intelligence hasn’t remade the enterprise or shattered ROI goals — yet. In fact, McKinsey found that only about 1% of respondents in a recent survey believe they are at artificial intelligence maturity. 80% of respondents in a similar report failed to see a tangible ROI from generative AI.  The data above shows that AI system implementations from […]

Through the Looking Glass: Mahler, Creativity, and AI-Generated Music

It’s been just a few months since I checked off one of the top items on my bucket list. This past May, my wife and I traveled to London and Amsterdam. We built our trip around attending several concerts at the third-ever Gustav Mahler Festival [1]. We’d awoken at 3:30 a.m. back in February to […]

What Makes Small Businesses’ Data Valuable to Cybercriminals?

While large corporations like Optus, Medibank, and The Iconic often dominate headlines for cybersecurity breaches, the reality is that small businesses are increasingly attractive targets for cybercriminals. Many small business owners operate under the dangerous illusion that their business is too small or insignificant to attract the attention of cybercriminals or that they have nothing of value to steal. This mindset often leads to a false sense of security…

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Open Data Fabric: Rethinking Data Architecture for AI at Scale

Enterprise AI agents are moving from proof-of-concept to production at unprecedented speed. From customer service chatbots to financial analysis tools, organizations across various industries are deploying agents to handle critical business functions. Yet a troubling pattern is emerging; agents that perform brilliantly in controlled demos are struggling when deployed against real enterprise data environments. The problem […]

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Optimizing retail operations through a practical data strategy

Given the pace of change in the retail sector, impactful decisions can be a competitive advantage, but many organizations are still in the dark. They’re not operating with actionable insights… trusting their gut to make decisions while keeping data in a silo. The solution? An all-inclusive data strategy that makes sense for the organization. This article […]

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How an Internal AI Governance Council Drives Responsible Innovation

AI has rapidly evolved from a futuristic concept to a foundational technology, deeply embedded in the fabric of contemporary organizational processes across industries. Companies leverage AI to enhance efficiency, personalize customer interactions, and drive operational innovation. However, as AI permeates deeper into organizational structures, it brings substantial risks related to data privacy, intellectual property, compliance […]

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The Data Danger of Agentic AI

Agentic AI represents a significant evolution beyond traditional rule-based AI systems and generative AI, offering unprecedented autonomy and transformative potential across various sectors. These sophisticated systems can plan, decide, and act independently, promising remarkable advances in efficiency and decision-making. However, this high degree of autonomy, when combined with poorly governed or flawed data, can lead […]

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How to Future-Proof Your Data and AI Strategy

With AI systems reshaping enterprises and regulatory frameworks continuously evolving, organizations face a critical challenge: designing AI governance that protects business value without stifling innovation. But how do you future-proof your enterprise for a technology that is evolving at such an incredible pace? The answer lies in building robust data foundations that can adapt to whatever comes […]

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Data Governance and CSR: Evolving Together

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

Tending the Unicorn Farm: A Business Case for Quantum Computing

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

The Five Levels Essential to Scaling Your Data Strategy

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

Why Data Governance Still Matters in the Age of AI

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

Data Speaks for Itself: Is Your Data Quality Management Practice Ready for AI?

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

A Step Ahead: From Acts to Aggregates — Record-ness and Data-ness in Practice

Introduction What is the difference between records and data? What differentiates records managers from data managers? Do these distinctions still matter as organizations take the plunge into artificial intelligence? Discussions that attempt to distinguish between records and data frequently articulate a heuristic for differentiation. “These items are records; those items are data.” Many organizations have […]

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.

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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.

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