Sep 26 2025

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 10 2025

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

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The Five Levels Essential to Scaling Your Data Strategy

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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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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

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A Leadership Blueprint for Driving Trusted, AI-Ready Data Ecosystems

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All in the Data: Where Good Data Comes From

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The Book Look: Rewiring Your Mind for AI

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

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

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Author: Subasini Periyakaruppan

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Jun 24 2025

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

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

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

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

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

What’s new in the Actian Cloud Portfolio

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

Actian Data Intelligence

Decentralized Data Management Without Sacrificing Governance

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

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

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

Ask AI Transforms How Teams Find and Understand Data

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

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

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

Chrome Extension Brings Context Directly to Your Workflow

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

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

Enhanced Tableau and Power BI Integration

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

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

Actian Data Observability

Custom SQL Metrics Eliminate Data Blind Spots

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

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

Actionable Notifications With Embedded Visuals

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

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

Automated JIRA Integration and a new Centralized Incident Management Hub

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

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

Redesigned Connection Flow Empowers Distributed Teams

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

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

Expanded Google Cloud Storage Support

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

What’s New in the Actian On-Premises Portfolio

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

DataConnect v12.4

Automated Rule Creation with Inspect and Recommend

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

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

Advanced Multi-Field Conditional Rules

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

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

Data Quality Index Provides Executive Visibility

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

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

Streamlined Schema Evolution

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

Flexible Masking and Anonymization

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

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

Take Action

Join the Corinium webinar Data Contract First Governance: Building Trust Through Data Products
Download the eBook Putting Data Contracts First: The Engineer’s Guide to Data Trust
Contact us to schedule a demo and see these new features in action

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

Improving Data Quality Using AI and ML

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

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

Tackling Complex Data Governance Challenges in the Banking Industry

The banking industry is one of the most heavily regulated sectors, and as financial services evolve, the challenges of managing, governing, and ensuring compliance with vast amounts of information have grown exponentially. With the introduction of stringent regulations, increasing data privacy concerns, and growing customer expectations for seamless service, banks face complex data governance challenges. These challenges include managing large volumes of sensitive data, maintaining data integrity, ensuring compliance with regulatory frameworks, and improving data transparency for both internal and external stakeholders.

In this article, we explore the core data governance challenges faced by the banking industry and how the Actian Data Intelligence Platform helps banking organizations navigate these challenges. From ensuring compliance with financial regulations to improving data transparency and integrity, the platform offers a comprehensive solution to help banks unlock the true value of their data while maintaining robust governance practices.

The Data Governance Landscape in Banking

The financial services sector generates and manages massive volumes of data daily, spanning customer accounts, transactions, risk assessments, compliance checks, and much more. Managing this data effectively and securely is vital to ensure the smooth operation of financial institutions and to meet regulatory and compliance requirements. Financial institutions must implement robust data governance to ensure data quality, security, integrity, and transparency.

At the same time, banks must balance regulatory requirements, operational efficiency, and customer satisfaction. This requires implementing systems that can handle increasing amounts of data while maintaining compliance with local and international regulations, such as GDPR, CCPA, Basel III, and MiFID II.

Key Data Governance Challenges in the Banking Industry

Below are some common hurdles and challenges facing organizations in the banking industry.

Data Privacy and Protection

With the rise of data breaches and increasing concerns about consumer privacy, banks are under immense pressure to safeguard sensitive customer information. Regulations such as the General Data Protection Regulation (GDPR) in the EU and the California Consumer Privacy Act (CCPA) have made data protection a top priority for financial institutions. Ensuring that data is appropriately stored, accessed, and shared is vital for compliance, but it’s also vital for maintaining public trust.

Regulatory Compliance

Banks operate in a highly regulated environment, where compliance with numerous financial regulations is mandatory. Financial regulations are continuously evolving, and keeping up with changes in the law requires financial institutions to adopt efficient data governance practices that allow them to demonstrate compliance.

For example, Basel III outlines requirements for the management of banking risk and capital adequacy, while MiFID II requires detailed reporting on market activities and transaction records. In this landscape, managing compliance through data governance is no small feat.

Data Silos and Fragmentation

Many financial institutions operate in a fragmented environment, where data is stored across multiple systems, databases, and departments. This lack of integration can make it difficult to access and track data effectively. For banks, this fragmentation into data silos complicates the management of data governance processes, especially when it comes to ensuring data accuracy, consistency, and completeness.

Data Transparency and Integrity

Ensuring the integrity and transparency of data is a major concern in the banking industry. Banks need to be able to trace the origins of data, understand how it’s been used and modified, and provide visibility into its lifecycle. This is particularly important for audits, regulatory reporting, and risk management processes.

Operational Efficiency

As financial institutions grow and manage increasing amounts of data, operational efficiency in data management becomes increasingly challenging. Ensuring compliance with regulations, conducting audits, and reporting on data use can quickly become burdensome without the right data governance tools in place. Manual processes are prone to errors and inefficiencies, which can have costly consequences for banks.

How the Actian Data Intelligence Platform Tackles Data Governance Challenges in the Banking Industry

The Actian Data Intelligence Platform is designed to help organizations tackle the most complex data governance challenges. With its comprehensive set of tools, The platform supports banks by helping ensure compliance with regulatory requirements, improving data transparency and integrity, and creating a more efficient and organized data governance strategy.

Here’s how the Actian Data Intelligence Platform helps the banking industry overcome its data governance challenges.

1. Ensuring Compliance With Financial Regulations

The Actian Data Intelligence Platform helps banks achieve regulatory compliance by automating compliance monitoring, data classification, and metadata management.

Regulatory Compliance Automation: The Actian Data Intelligence Platform enables banks to automate compliance tracking and continuously monitor data access and usage. This helps banks ensure that they are consistently meeting the requirements of regulatory frameworks like GDPR, Basel III, MiFID II, and others. The Actian Data Intelligence Platform’s compliance monitoring tools also automatically flag any data access or changes to data that may violate compliance rules, giving banks the ability to react quickly and mitigate risks.
Data Classification for Compliance: The Actian Data Intelligence Platform allows banks to classify and categorize data based on its sensitivity and compliance requirements. By tagging data with relevant metadata, such as classification labels (e.g., personal data, sensitive data, financial data), The Actian Data Intelligence Platform ensures that sensitive information is handled in accordance with regulatory standards.
Audit Trails and Reporting: The Actian Data Intelligence Platform’s audit trail functionality creates comprehensive logs of data access, usage, and modifications. These logs are crucial for financial institutions when preparing for audits or responding to regulatory inquiries. The Actian Data Intelligence Platform automates the creation of compliance reports, making it easier for banks to demonstrate their adherence to regulatory standards.

2. Improving Data Transparency and Integrity

Data transparency and integrity are critical for financial institutions, particularly when it comes to meeting regulatory requirements for reporting and audit purposes. The Actian Data Intelligence Platform offers tools that ensure data is accurately tracked and fully transparent, which helps improve data governance practices within the bank.

Data Lineage: The Actian Data Intelligence Platform’s data lineage functionality provides a visual map of how data flows through the bank’s systems, helping stakeholders understand where the data originated, how it has been transformed, and where it is currently stored. This is essential for transparency, especially when it comes to auditing and compliance reporting.
Metadata Management: The Actian Data Intelligence Platform’s metadata management capabilities enable banks to organize, track, and maintain metadata for all data assets across the organization. This not only improves transparency but also ensures that data is properly classified and described, reducing the risk of errors and inconsistencies. With clear metadata, banks can ensure that data is correctly used and maintained across systems.
Data Quality Monitoring: The Actian Data Intelligence Platform continuously monitors data quality, ensuring that data remains accurate, complete, and consistent across systems. Data integrity is crucial for banks, as decisions made based on poor-quality data can lead to financial losses, reputational damage, and non-compliance.

3. Eliminating Data Silos and Improving Data Integration

Fragmented data and siloed systems are a common challenge for financial institutions. Data often resides in disparate databases or platforms across different departments, making it difficult to access and track efficiently. The platform provides the tools to integrate data governance processes and eliminate silos.

Centralized Data Catalog: The Actian Data Intelligence Platform offers a centralized data catalog that enables banks to consolidate and organize all their data assets using a single platform. This centralized repository improves the discoverability of data across departments and systems, helping banks streamline data access and reduce inefficiencies.
Cross-Department Collaboration: With the Actian Data Intelligence Platform, departments across the organization can collaborate on data governance. By centralizing governance policies, data access, and metadata, The Actian Data Intelligence Platform encourages communication between data owners, stewards, and compliance officers to ensure that data governance practices are consistent across the institution.

4. Enhancing Operational Efficiency

Manual processes in data governance can be time-consuming and prone to errors, making it challenging for banks to keep pace with the growing volumes of data and increasing regulatory demands. The Actian Data Intelligence Platform’s platform automates and streamlines key aspects of data governance, allowing banks to work more efficiently and focus on higher-value tasks.

Automation of Compliance Monitoring: The Actian Data Intelligence Platform automates compliance checks, data audits, and reporting, which reduces the manual workload for compliance teams. Automated alerts and reports help banks quickly identify potential non-compliance issues and rectify them before they escalate.
Workflow Automation: The Actian Data Intelligence Platform enables banks to automate workflows around data governance processes, including data classification, metadata updates, and access management. By streamlining these workflows, The Actian Data Intelligence Platform ensures that banks can efficiently manage their data governance tasks without relying on manual intervention.
Data Access Control: The Actian Data Intelligence Platform helps banks define and enforce fine-grained access controls for sensitive data. With The Actian Data Intelligence Platform’s robust access control mechanisms, banks can ensure that only authorized personnel can access specific data, reducing the risk of data misuse and enhancing operational security.

The Actian Data Intelligence Platform and the Banking Industry: A Perfect Partnership

The banking industry faces a range of complex data governance challenges. To navigate these challenges, they need robust data governance frameworks and powerful tools to help manage their vast data assets.

The Actian Data Intelligence Platform offers a comprehensive data governance solution that helps financial institutions tackle these challenges head-on. By providing automated compliance monitoring, metadata tracking, data lineage, and a centralized data catalog, the platform ensures that banks can meet regulatory requirements while improving operational efficiency, data transparency, and data integrity.

Actian offers an online product tour of the Actian Data Intelligence Platform as well as personalized demos of how the data intelligence platform can transform and enhance financial institutions’ data strategies.

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