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Aug 19 2025
Customer MDM and the SMB

Small and Medium Businesses (SMBs) face several core challenges when adopting enterprise Master Data Management (MDM) solutions, whether this be a Reltio, Stibo, Profisee or one of the even bigger multidomain solutions. These challenges are often due to a misalignment between the complex nature of these tools and the more limited resources and simpler needs of SMBs.

The main challenges are these:

  • Cost and Complexity: Historically, MDM was seen as prohibitively expensive with lengthy deployments. While modern cloud native SaaS models reduce upfront hardware costs, the total cost of ownership (TCO) and consulting fees can remain high for many SMBs with limited budgets and IT expertise. SMBs often find the full scope of features and complexity of enterprise MDM to be overkill for their simpler needs, such as basic deduplication or customer 360, leading to them paying for advanced features they don’t use.
  • Time to Value: Implementations of enterprise MDM solutions can stretch for months or even years, which strains SMB patience and expectations for return on investment (ROI). New features and customizations may also have to wait for product-wide releases, delaying time-to-value for SMBs.
  • Resource Burden and Skills Gaps: Enterprise MDM typically requires deep organization-wide data governance, custom data stewardship, and significant post-launch maintenance, which can be overwhelming for lean SMB teams. Effective use of these tools demands specialized training for administrators, developers, and data stewards, a stretch for smaller teams not solely dedicated to data management. SMBs particularly feel the pain of vendor-dependent workflow changes, as they often lack in-house MDM experts.
  • Support Responsiveness and Vendor Dependency: SMBs often experience support delays, sometimes waiting days or longer for vendors to deploy or fix critical system components. Even basic customizations, like workflow updates or expanding data domains, frequently require vendor action, slowing SMB agility and innovation.
  • Usability and Integration Issues: Complex features such as survivorship configuration, match rules, and large data exports can be cumbersome for smaller IT teams, especially when technical resources are limited and the user interface or documentation falls short. Slow large data downloads and JSON formats as default outputs place extra strain on SMBs who lack robust Business Intelligence (BI) or integration resources. SMBs need simple, pre-built connectors and quick integrations, and solutions requiring heavy customization can leave them behind.
  • Unpredictable Operating Expenses: Many SaaS MDM offerings including Pretectum CMDM, impose quotas or API call limits, which may force SMBs to incur extra costs as their own data volumes and integrations expand, creating unplanned operational expenses.

Despite technical advancements in SaaS solutions, SMB customers commonly complain about excessive complexity, a high reliance on the vendor, support delays, and a fundamental mismatch between what enterprise MDM offers and what SMBs truly value. The “DNA” of enterprise MDM, with its inherent complexity and resource demands, persists even in “SMB-friendly” SaaS wrappers.

This is why we think Pretectum may present as a generally better fit for the SMB market with its equally complex needs for Customer Master Data Management (MDM), but less resources.

Uli LOKSHIN 0 Comments
Aug 18 2025
What is Customer MDM?

Customer MDM (Master Data Management) is a technology-enabled practice and this is often encapsulated in some solution. Pretectum CMDM is a SaaS solution that centralizes, standardizes, and synchronizes customer data across an organization. It creates a single point of reference or a Single Customer View (SCV) by integrating data from various sources, ensuring that all departments, systems, and applications have access to consistent, accurate, and up-to-date customer information.

Pretectum CMDM acts as both a system of reference and a system of entry for customer data. It allows businesses to maintain authoritative and trusted customer master data, which supports operations like customer service, marketing, sales, compliance, and decision-making. The platform features a rich, searchable UI and API access for managing and curating customer data, with strong emphasis on data security, privacy (automatic PII masking), and collaborative governance.

By using Pretectum CMDM, organizations benefit from improved customer insights, personalized customer experiences, operational efficiencies, and risk reduction. It enables continuous improvement of the customer data model, adapting to evolving business needs while maintaining data accuracy and compliance.

Customer Master Data Management (MDM) with Pretectum CMDM means managing customer data in a centralized, secure, and standardized way to provide businesses with a holistic and reliable understanding of their customers, empowering better business decisions and enhanced customer interactions. This makes it a foundational practice to achieve data-driven excellence and personalized customer engagement.

Centralized customer MDM (Master Data Management) offers a range of benefits for organizations by creating a single, trusted source of customer information that is accessible across departments and systems. Here are the key advantages:

  • 360-Degree Customer View: A central repository consolidates data from multiple sources, giving a holistic view of each customer. This enables better understanding of customer preferences, behaviors, and purchase histories, leading to more personalized marketing and improved service.
  • Improved Data Quality and Accuracy: Centralization reduces duplicate records, eliminates inconsistencies, and standardizes data entry. This ensures every team is working with reliable, up-to-date customer information.
  • Enhanced Customer Experience: With consistent and complete customer data, organizations can tailor products, communications, and services, increasing satisfaction and loyalty.
  • Operational Efficiency: Teams spend less time reconciling data or searching for information. Streamlined, automated processes and data governance reduce manual effort and improve productivity.
  • Stronger Data Governance and Compliance: Centralized MDM supports compliance with data privacy regulations, offering better control, security, and audit trails for customer information.
  • Better Decision-Making: High-quality, consistent data supports robust analytics, reporting, and predictive models, enabling leaders to make data-driven decisions with confidence.
  • Cost Reduction: By eliminating redundant data, streamlining infrastructure, and optimizing IT processes, organizations can cut operational costs and avoid unnecessary expenditures.
  • Agility and Scalability: Centralized data enables organizations to respond quickly to business changes, expand into new markets, and embrace digital transformation with greater ease and less risk.
  • Breaks Down Data Silos: Makes customer data instantly accessible company-wide, resulting in cohesive strategies and aligned customer experiences across all channels.
  • Supports Innovation: Clean and unified customer data lays the groundwork for deploying emerging technologies like AI/ML and supports development of new products, loyalty programs, and business models.

Organizations leveraging solutions like Pretectum CMDM benefit from all these advantages, positioning themselves to provide superior customer engagement, operate more efficiently, and adapt faster to market changes.

Uli LOKSHIN 0 Comments
Aug 18 2025
What Exactly is Customer Loyalty?


Here’s our hot take on #customer #loyalty – we believe that it is all about trust, satisfaction and an emotional connection with your brand and is often achieved through personalized experiences.

There are of course different types of loyalty, behavioural, attitudinal and transactional.

Developing loyalty is not without its challenges though – developing it may be hampered by fragmented data, a lack of a personalized engagement plan and execution, accompanied by trust and data privacy concerns.

That’s why we think Pretectum CMDM is a perfect complement to your existing tech and data stack.

With Pretectum, you get a single customer view that enables personalized and timely engagement; real-time data integration as a push or a pull – for dynamic loyalty program adaptation and strengthened data security to foster customer trust.

This all achieved with #AI-powered data tags and data classification, deterministic and fuzzy record matching and flexible data duplicated blending, harmonization, merge and survivorship.

For a loyalty program the operational benefits are pretty clear; increased efficiency through automation with data governance.Cross departmental collaboration with consistent customer information and empowered teams that have actionable insights.

The impact for your targeted business outcomes are pretty clear too – improved customer retention and customer lifetime value (CLV); higher customer advocacy and brand loyalty and competitive differentiation through superior data-driven loyalty management.

Our vision is loyalty as a strategic business asset powered by data mastery. Pretectum CMDM’s role is in redefining loyalty in the digital age and leveraging your customer data to build lasting customer loyalty.

Learn more by visiting www.pretectum.com
#loyaltyisupforgrabs

Juwel Tan 0 Comments
Aug 18 2025
Empowering employees – The Power of a Unified Customer View


This isn’t just a dream; it’s the reality offered by Pretectum CMDM (Customer Master Data Management). By providing a single, unified customer view, Pretectum CMDM empowers your teams with an unparalleled edge.

Here’s how it transforms your operations:
Proactive Risk Mitigation: With all customer data centralized and securely managed, the risk of data breaches is drastically reduced. Pretectum CMDM provides robust security features and consistent data governance, giving you peace of mind.
Enhanced System Stability: A unified view eliminates data silos and inconsistencies, leading to more stable and reliable systems. This means fewer system failures and disruptions, allowing your teams to work seamlessly.
Streamlined Compliance: Navigating complex compliance obligations becomes effortless. Pretectum CMDM ensures data accuracy, traceability, and adherence to regulations, transforming a potential crisis into a well-managed process.
Unleashed Efficiency: Imagine your sales, marketing, and customer service teams all working from the same accurate, real-time customer information. This eliminates redundant efforts, improves decision-making, and significantly boosts overall organizational efficiency.
Superior Customer Experiences: With a complete understanding of each customer, your teams can deliver personalized and proactive experiences, fostering stronger relationships and driving customer loyalty.

In essence, Pretectum CMDM allows you to shift your focus from firefighting operational crises to innovating, growing, and serving your customers better than ever before. It’s about empowering your teams with the insights and tools they need to thrive in a fast-paced world, rather than being bogged down by its complexities.

#LoyaltyIsUpForGrabs

Juwel Tan 0 Comments
Aug 18 2025
Introducing Pretectum Cognito Search


Pretectum CMDM offers a sophisticated search experience with "Pretectum Cognito Search," which integrates large language models (LLMs) and Elasticsearch to provide intuitive data retrieval.

Here’s a breakdown of its key features:

Triple Combination Search: This allows users to initiate a search by simply entering a string. Under the hood, it leverages Elasticsearch for initial matches.

LLM-Constructed Query Builder: This is the core innovation of Pretectum Cognito Search. When a simple string search might not yield optimal results, especially when users aren’t familiar with specific field names or tags, the LLM-constructed query builder steps in.

It uses its knowledge of the application’s schemas and tags to interpret natural language questions.

It then constructs a more precise query based on this understanding, aiming to deliver better results.

This eliminates the need for users to learn complex query syntax, making data access more accessible.

Interactive Query Builder: If users receive results from the LLM-constructed query and wish to refine it further without posing another natural language question, they can seamlessly switch to an interactive query builder. This builder still doesn’t require knowledge of query syntax, offering a user-friendly way to adjust the search criteria.

Essentially, Pretectum Cognito Search aims to bridge the gap between user intent (expressed in natural language) and the technical complexity of data querying, by leveraging AI to facilitate more accurate and efficient searches within the Pretectum CMDM platform. This is particularly beneficial for achieving a "Single Customer View" by consolidating and making customer data easily searchable.

Juwel Tan 0 Comments
Aug 18 2025
Customer Recognition #customerdata #customerdetails


To succeed, businesses must prioritize recognizing and retaining existing customers, not just acquiring new ones.

While the "Customer Recognition Ratio" isn’t a formal metric, the concept of understanding customers through their past interactions is crucial. Metrics like CRR, CLV, and RPR highlight how customer recognition drives profitability by reducing acquisition costs.

Platforms like Pretectum CMDM are vital for achieving this by centralizing and enhancing customer data, enabling a deeper understanding and stronger relationships.

Click on the article to read more.
https://www.pretectum.com/the-customer-recognition-ratio/

Juwel Tan 0 Comments
Aug 6 2025
Data Governance and CSR: Evolving Together
In a world where every claim your organization makes — about sustainability, equity, or social impact — is scrutinized by regulators, investors, and the public, one truth stands out: Your data has never mattered more. Corporate Social Responsibility (CSR) isn’t just about good intentions — it is about trustworthy, transparent data that stands up to […]


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Author: Robert S. Seiner

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Aug 6 2025
Tending the Unicorn Farm: A Business Case for Quantum Computing
Welcome to the whimsical wide world of unicorn farming. Talking about quantum computing is a bit like tending to your unicorn farm, in that a lossless chip (at the time of writing) does not exist. So, largely, the realm of quantum computing is just slightly faster than normal compute power. The true parallel nature of […]


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Author: Mark Horseman

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Aug 6 2025
The Five Levels Essential to Scaling Your Data Strategy
Scaling your data strategy will inevitably result in winners and losers. Some work out the system to apply in their organization and skillfully tailor it to meet the demands and context of their organization, and some don’t or can’t. It’s something of a game.  But how can you position yourself as a winner? Read on […]


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Author: Jason Foster

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Aug 6 2025
Why Data Governance Still Matters in the Age of AI
At a recent conference, I witnessed something that’s become far too common in data leadership circles: genuine surprise that chief data officers consistently cite culture — not technology — as their greatest challenge. Despite a decade of research and experience pointing to the same root cause, conversations still tend to focus on tools rather than […]


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Author: Christine Haskell

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Aug 6 2025
Data Speaks for Itself: Is Your Data Quality Management Practice Ready for AI?
While everyone is asking if their data is ready for AI, I want to ask a somewhat different question: Is your data quality management (DQM) program ready for AI?  In my opinion, you need to be able to answer yes to the following four questions before you can have any assurance you are ready to […]


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Author: Dr. John Talburt

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Aug 6 2025
A Step Ahead: From Acts to Aggregates — Record-ness and Data-ness in Practice
Introduction  What is the difference between records and data? What differentiates records managers from data managers? Do these distinctions still matter as organizations take the plunge into artificial intelligence? Discussions that attempt to distinguish between records and data frequently articulate a heuristic for differentiation. “These items are records; those items are data.” Many organizations have […]


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Author: The MITRE Corporation

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Aug 2 2025
Customer Master Data Model
The image is a creative, whimsical visual pun for "Customer Master Data Model." It features a miniature, stylized figure of a data scientist in a tiny suit and spectacles. This figure is diligently sculpting a human figure out of what appears to be clay or play-doh.

To truly drive informed decision-making and unlock the full potential of your customer relationships, a well-structured and comprehensive customer master data model is absolutely essential. This model, at the heart of your Customer Master Data Management, acts as the blueprint for how you understand and interact with your customers. With the Pretectum Customer Data Management solution, organizations have the flexible SaaS platform to build and manage such a sophisticated model, ensuring data accuracy, accessibility, and the ability to generate valuable insights.

A adaptable customer master data model, built within Pretectum’s adaptable framework, can go beyond just basic contact information. It may encompass a wide array of attributes that paint a complete picture of your customer.

Pretectum’s ability to define one or more data models (schemas) for data drawn from diverse sources, coupled with its strong data typing and data validations, makes it the ideal environment for constructing such a detailed and dynamic model. While the primary intent of Pretectum is people master data management, its flexibility means data can even be transactional, allowing for a truly holistic view.

Consider then, some of the most common customer data attributes and how you might set them up. These also become a foundational aspect of how you might think about data tags and classifying your attributes through metadata.

We’ve grouped these but they could appear at any point in the evolution of your customer profiles.

Basic Identification Attributes

These are the foundational elements for uniquely identifying and contacting your customers. Pretectum’s schema definition allows for precise data typing to ensure these critical fields are always accurate.

  • CustomerID: A unique identifier for each customer. Pretectum’s ability to ingest data from various sources means it can consolidate and manage these identifiers across disparate systems.
  • FirstName & LastName: Essential for personalized communication.
  • FullName: A consolidated field often derived or assembled for display purposes.
  • Email: A primary digital contact point. Pretectum supports email as a content data type.
  • Phone: Another crucial contact method.
  • DateOfBirth: Important for age-based segmentation and compliance.
  • Gender: A demographic identifier that can be validated against business area data in Pretectum.
A screenshot of a schema wherein a patient or participant record is defined for the purposes of clinical or medical trials in health care

Demographic Attributes

Demographic information provides context about your customers, enabling segmentation and targeted marketing. Pretectum’s flexible schema can easily incorporate these, and business area data can be used for validation.

  • Address, City, State, ZipCode, Country: Geographical identifiers vital for localized marketing, shipping, and understanding regional trends. Pretectum supports text and ISO codes for country validation.
  • Language: Key for delivering content in the customer’s preferred language.
  • MaritalStatus: Can be valuable for specific product or service offerings.

Transactional Attributes

These attributes capture the history of customer interactions that involve purchases or financial exchanges. Pretectum, while primarily for master data, can ingest transactional data if defined in the schema, making these attributes manageable. There are a number of use cases that benefit from Transaction Attributes.

  • TotalSpent: Cumulative spending over time, indicating customer value.
  • AverageOrderValue: Insights into spending habits per transaction.
  • NumberOfOrders: Frequency of purchases.
  • LastOrderDate & FirstOrderDate: Markers for customer engagement and longevity.
  • HighestOrderValue & LowestOrderValue: Indicators of purchasing range.
Editorial and Audience Analytics Infographic

Behavioral Attributes

Understanding how customers interact with your digital properties offers deep insights into their preferences and intentions. Pretectum’s flexible schema allows for the inclusion of diverse data types like text for search history or clickstream data. There are a number of use cases that benefit from Behavioral Attributes.

  • NumberOfVisits: Frequency of website or app engagement.
  • AverageTimeOnSite: Indicates engagement depth.
  • MostViewedPages: Highlights product or content interest.
  • SearchHistory: Reveals specific customer needs or desires.
  • ClickStreamData: Detailed navigation paths providing granular behavioral insights.
Customer Facing Metrics Infographic

Engagement Attributes

These attributes measure how customers interact with your brand beyond transactions, focusing on communication and loyalty. Pretectum’s schema can hold text-based communication preferences or numerical feedback scores. There are a number of use cases that benefit from Engagement Attributes.

  • CommunicationPreferences: Opt-in/out status for various communication channels.
  • SubscriptionStatus & SubscriptionStartDate/EndDate: Relevant for recurring service models.
  • FeedbackScore: Direct sentiment from customer surveys or reviews.
  • LoyaltyPoints: Track participation in loyalty programs.
Content Recommendations Infographic

Derived Attributes

These are not directly collected but are calculated or inferred from other attributes. They offer powerful predictive capabilities and segmentation opportunities. While Pretectum ingests the raw data, these derivations would typically happen in a connected analytics layer, leveraging Pretectum’s high-quality output. There are a number of use cases that benefit from Derived Attributes.

  • CustomerLifetimeValue (CLV): A projection of the total revenue a customer is expected to generate over their relationship with your business.
  • ChurnProbability: The likelihood of a customer discontinuing their relationship.
  • NextBestAction: Suggested actions for sales, marketing, or service based on customer profiles.
  • SegmentMembership: Assigning customers to specific segments for targeted strategies.
Infographic Next Best Action

Best Practices

Beyond simply identifying attributes, the success of any customer data management platform hinges on how you design and maintain your data model. Pretectum is engineered with capabilities that directly support these best practices, transforming theoretical ideals into operational realities.

Ensure Data Quality Through Regular Cleansing, Validation, and Enrichment

Data quality is paramount. Pretectum addresses this head-on:

  • Validation: Its data models can be enhanced with strong data typing and data validations. During manual record entry, bad data is actively blocked. For data brought in via CSV imports, integrations, or streams, lightweight ETL processes use Excel-like syntax for attribute-level transformation, and data is then flagged as matching validation or not, allowing immediate identification of quality issues.
Quick Entry Screen Validation in the Pretectum CMDM
Quick Entry Screen Validation in the Pretectum CMDM

Cleansing: Pretectum features a powerful batch-based duplicate matching process that can identify commonalities across one or more business areas and datasets. These identified matchsets can then be used to nominate a “survivor” record, with a second batch process allowing users to merge or purge records based on configurable survivorship rules, effectively cleaning duplicates.

  • Self-Service Validation: Pretectum uniquely offers a self-service data validation and consent granting process. This empowers the customer directly to receive a one-time use email, allowing them to edit, redact, and self-consent to the data held in the system, ensuring data accuracy from the source and fostering trust.
Screenshot consumer record verification consent

Implement Data Governance and Stewardship to Define Standards and Ownership

Robust data governance is crucial for consistent and reliable data management. Pretectum provides the tools:

  • Data Tagging/Business Glossary: The platform’s flexible and configurable data tagging functionality serves as a business glossary, allowing attributes of the models to be classified. This aids in defining clear data standards and understanding data meaning across the organization. AI prompts can even accelerate the creation of these focused tags.
  • Role-Based Access Controls (RBAC): Pretectum offers a sophisticated and configurable permissions matrix. Users can have view-only access, or specific permissions for editing. Importantly, PII masking is automatic, and unmasking requires re-entering credentials for users with the specific “PII data unmasking” permission, with all such actions logged in an audit log. This ensures clear ownership and controlled access to sensitive information.
Tagged Attributes in a Schema Definition accompanied by a Picklist based on ISO State Codes

Integrate Data from Various Touchpoints to Provide a Unified View of the Customer

A complete customer view requires integrating data from diverse sources. Pretectum is built for this:

  • The platform is designed to draw data in from diverse sources, including CSV imports, integrations via JDBC or REST APIs in batches, or via subscribed streams.
  • Its ability to define multiple data models across partitioned business areas allows for the consolidation and harmonization of customer data, truly working towards that single, accurate, and complete view of your customers across all touchpoints and systems.
Flow Mapper with JDBC mapping

Enable Data Security and Privacy to Protect Sensitive Information

Protecting sensitive customer information is non-negotiable. Pretectum prioritizes this:

  • Automatic PII Masking: If data is marked as PII in the schema, it’s automatically masked once it lands in the dataset.
  • Controlled Unmasking: Revealing masked data requires specific user permissions and re-authentication, and this crucial action is logged in an audit log, providing a clear trail for compliance and security monitoring.
  • Granular Permissions: The RBAC matrix allows for precise control over who can view, edit, or unmask data, ensuring adherence to privacy regulations.
Password Challenge for user wishing to see masked PII data.

Foster Cross-Functional Collaboration

Align Customer Data Usage Across your whole Organization

Effective CMDM is a collaborative effort. Pretectum facilitates this by providing a common platform and shared understanding:

  • The data tagging functionality acts as a shared business glossary, ensuring everyone uses the same definitions and classifications for data attributes.
  • The AI-powered elastic search allows users across departments to build complex searches using natural language questions and contextual tags, reducing the need for specialized knowledge and fostering broader data utilization and collaboration. The search results are scoped by user permissions, ensuring relevant and secure access.

Continuously Monitor and Improve the Model Based on Evolving Business Needs and Industry Trends

A master data model is not static; it must evolve. Pretectum supports continuous improvement:

  • Flexible Data Models: Data models (schemas) can be enhanced and every dataset can be edited, appended to, or replaced according to the needs of the business, allowing for agile adaptation.
  • Audit Logging: The comprehensive audit log for sensitive actions (like PII unmasking, self-service consent) provides insights into data usage and changes, supporting ongoing monitoring.
  • Validation Feedback: The flagging of data against validation rules during ingestion provides continuous feedback on data quality, guiding improvements to the model and ingestion processes.

By incorporating these attributes and leveraging Pretectum’s inherent capabilities and data modeling best practices, organizations can create a truly comprehensive customer master data model. This empowers them to make informed decisions, deliver personalized customer experiences, and achieve data-driven excellence, ensuring their CMDM practice is not just robust but also future-ready. Contact us to learn more. #LoyaltyIsUpForGrabs

Uli LOKSHIN 0 Comments
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.


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Author: Actian Corporation

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Jul 22 2025
Everything You Need to Know About Synthetic Data


Synthetic data sounds like something out of science fiction, but it’s fast becoming the backbone of modern machine learning and data privacy initiatives. It enables faster development, stronger security, and fewer ethical headaches – and it’s evolving quickly.  So if you’ve ever wondered what synthetic data really is, how it’s made, and why it’s taking center […]

The post Everything You Need to Know About Synthetic Data appeared first on DATAVERSITY.


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Author: Nahla Davies

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Jul 21 2025
Data Observability vs. Data Monitoring

Two pivotal concepts have emerged at the forefront of modern data infrastructure management, both aimed at protecting the integrity of datasets and data pipelines: data observability and data monitoring. While they may sound similar, these practices differ in their objectives, execution, and impact. Understanding their distinctions, as well as how they complement each other, can empower teams to make informed decisions, detect issues faster, and improve overall data trustworthiness.

What is Data Observability?

Data Observability is the practice of understanding and monitoring data’s behavior, quality, and performance as it flows through a system. It provides insights into data quality, lineage, performance, and reliability, enabling teams to detect and resolve issues proactively.

Components of Data Observability

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

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

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

Benefits of Implementing Data Observability

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

What is Data Monitoring?

Data monitoring involves the continuous tracking of data and systems to identify errors, anomalies, or performance issues. It typically includes setting up alerts, dashboards, and metrics to oversee system operations and ensure data flows as expected.

Components of Data Monitoring

Core elements of data monitoring include the following.

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

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

Benefits of Data Monitoring

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

Key Differences Between Data Observability and Data Monitoring

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

Scope and Depth of Analysis

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

Proactive vs. Reactive Approaches

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

Data Insights and Decision-Making

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

How Data Observability and Monitoring Work Together

Despite their differences, data observability and monitoring are most powerful when used in tandem. Together, they create a comprehensive view of system health and data reliability.

Complementary Roles in Data Management

Monitoring handles alerting and immediate issue recognition, while observability offers deep diagnostics and context. This combination ensures that teams are not only alerted to issues but are also equipped to resolve them effectively.

For example, a data monitoring system might alert a team to a failed ETL job. A data observability platform would then provide lineage and metadata context to show how the failure impacts downstream dashboards and provide insight into what caused the failure in the first place.

Enhancing System Reliability and Performance

When integrated, observability and monitoring ensure:

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

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

Choosing the Right Strategy for An Organization

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

Assessing Organizational Needs

Start by answering the following questions.

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

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

Evaluating Tools and Technologies

Tools for data monitoring include:

  • Prometheus
  • Grafana
  • Datadog

Popular data observability platforms include:

  • Monte Carlo
  • Actian Data Intelligence Platform
  • Bigeye

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

Implementing a Balanced Approach

A phased strategy often works best:

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

Use Actian to Enhance Data Observability and Data Monitoring

Data observability and data monitoring are both essential to ensuring data reliability, but they serve distinct functions. Monitoring offers immediate alerts and performance tracking, while observability provides in-depth insight into data systems’ behavior. Using both concepts together with the tools and solutions provided by Actian, organizations can create a resilient, trustworthy, and efficient data ecosystem that supports both operational excellence and strategic growth.

Actian offers a suite of solutions that help businesses modernize their data infrastructure while gaining full visibility and control over their data systems.

With the Actian Data Intelligence Platform, organizations can:

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

Organizations using Actian benefit from increased system reliability, reduced downtime, and greater trust in their analytics. Whether through building data lakes, powering real-time analytics, or managing compliance, Actian empowers data teams with the tools they need to succeed.

The post Data Observability vs. Data Monitoring appeared first on Actian.


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Author: Actian Corporation

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Jul 17 2025
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:

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. 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.


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Author: Actian Corporation

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Jul 17 2025
Beyond Pilots: Reinventing Enterprise Operating Models with AI


The enterprise AI landscape has reached an inflection point. After years of pilots and proof-of-concepts, organizations are now committing unprecedented resources to AI, with double-digit budget increases expected across industries in 2025. This isn’t merely about technological adoption. It reflects a deep rethinking of how businesses operate at scale. The urgency is clear: 70% of the software used […]

The post Beyond Pilots: Reinventing Enterprise Operating Models with AI appeared first on DATAVERSITY.


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Author: Gautam Singh

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Jul 16 2025
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 […]


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Author: Ramalakshmi Murugan

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Jul 16 2025
A Leadership Blueprint for Driving Trusted, AI-Ready Data Ecosystems
As AI adoption accelerates across industries, the competitive edge no longer lies in building better models; it lies in governing data more effectively.  Enterprises are realizing that the success of their AI and analytics ambitions hinges not on tools or algorithms, but on the quality, trustworthiness, and accountability of the data that fuels them.  Yet, […]


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Author: Gopi Maren

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Jul 16 2025
All in the Data: Where Good Data Comes From
Let’s start with a truth that too many people still overlook — not all data is good data. Just because something is sitting in a database or spreadsheet doesn’t mean it’s accurate, trustworthy, or useful. In the age of AI and advanced analytics, we’ve somehow convinced ourselves that data — any data — can be […]


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Author: Robert S. Seiner

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Jul 16 2025
The Book Look: Rewiring Your Mind for AI
I collect baseball and non-sport cards. I started collecting when I was a kid, stopped for about 40 years, and returned to collecting again, maybe as part of a mid-life crisis. I don’t have the patience today though, that I had when I was 12. For example, yesterday I wanted to find out the most […]


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Author: Steve Hoberman

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Jul 1 2025
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.

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Author: Liz Brown

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