Generative AI (GenAI) continues to provide significant business value across many use cases and industries. But despite the many successful customer experiences, GenAI is also proving to be challenging for some businesses to get right and deploy across their organizations in full production. As a result, plenty of projects are getting stuck in planning, experimentation, […]
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 […]
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 […]
What is Model Context Protocol (MCP) and why is it suddenly being talked about everywhere? How does it support the future of agentic AI? And what happens to businesses that don’t implement it?
The short answer is MCP is the new universal standard connecting AI to trusted business context, fueling the rise of agentic AI. Organizations that ignore it risk being stuck with slow, unreliable insights while competitors gain a decisive edge.
What is Model Context Protocol?
From boardrooms to shop floors, AI is rewriting how businesses uncover insights, solve problems, and chart their futures. Yet even the most advanced AI models face a critical challenge. Without access to precise, contextualized information, their answers can fall short by being generic and lacking critical insights.
That’s where MCP comes in. MCP is a rapidly emerging standard that gives AI-powered applications, like large language models (LLM) assistants, the ability to connect to structured, real-time business context through a knowledge graph.
Think of MCP as a GPS for AI. It guides models directly to the most relevant and reliable information. Instead of building custom integrations for every tool or dataset, businesses can use MCP to give AI applications secure, standardized access to the information they need.
The result? AI systems that move beyond generic responses to deliver answers rooted in a company’s unique and current reality.
Why MCP Matters for Businesses
The rise of AI data analysts, which are LLM-powered assistants that translate natural-language questions into structured data queries, makes MCP mission-critical. Unlike traditional analytics tools that require SQL skills or dashboard expertise, an AI data analyst allows anyone to simply ask questions and get results.
These questions can be business focused, such as:
What’s driving our increase in customer churn?
How did supply chain delays impact last quarter’s revenue?
Are seasonal promotions improving profitability?
Answering these questions requires more than statistics. It demands contextual intelligence pulled from multiple, current data sources.
MCP ensures AI data analysts can:
Converse naturally. Users ask questions in plain language.
Ground answers in context. MCP optimizes knowledge graphs for context.
Be accessible to all users. No coding or data science expertise is needed.
Provide action-oriented insights. Deliver answers that leaders can trust.
In short, MCP is the bridge between decision-makers and the technical complexity of enterprise data.
The Business Advantages of MCP
The value of AI isn’t in generating an answer. It’s in generating the right answer. MCP makes that possible by standardizing how AI connects to business context, turning data into precise, actionable, and trusted insights.
Key benefits of MCP include:
Improved accuracy. AI reflects current, trusted business data.
Scalability across domains. Each business function, such as finance, operations, and marketing, maintains its own tailored context.
Reduced integration complexity. A standard framework replaces costly, custom builds.
Future-proof flexibility. MCP ensures continuity as new AI models and platforms emerge.
Greater decision confidence. Leaders act on insights that reflect real business conditions.
With MCP, organizations move from AI that’s impressive to AI that’s indispensable.
Knowledge Graphs: The Heart of MCP
At the core of MCP are knowledge graphs, which are structured maps of business entities and their relationships. They don’t just store data. They provide context.
For example:
A customer isn’t simply a record. They are linked to orders, support tickets, and loyalty status.
A product isn’t only an SKU. It’s tied to suppliers, sales channels, and performance metrics.
By tapping into these connections, AI can answer not only what happened but also why it happened and what’s likely to happen next.
Powering Ongoing Success With MCP
Organizations that put MCP into practice and support it with a knowledge graph can create, manage, and export domain-specific knowledge graphs directly to MCP servers.
With the right approach to MCP, organizations gain:
Domain-specific context. Each business unit builds its own tailored graph.
Instant AI access. MCP provides secure, standardized entry points to data.
Dynamic updates. Continuous refreshes keep insights accurate as conditions shift.
Enterprise-wide intelligence. Organizations scale not just data, but contextual intelligence across the business.
MCP doesn’t just enhance AI. It transforms AI from a useful tool into a business-critical advantage.
Supporting Real-World Use Cases Using AI-Ready Data
AI-ready data plays an essential role in delivering fast, trusted results. With this data and MCP powered by a knowledge graph, organizations can deliver measurable outcomes to domains such as:
Finance.Quickly explain revenue discrepancies by connecting accounting, sales, and market data.
Supply chain. Answer questions such as, “Which suppliers pose the highest risk to production goals?” with context-rich insights on performance, timelines, and quality.
Customer service. Recommend personalized strategies using data from purchase history, service records, and sentiment analysis.
Executive leadership. Provide faster, more reliable insights to act decisively in dynamic markets.
In an era where the right answer at the right time can define market leadership, MCP ensure AI delivers insights that are accurate, actionable, and aligned with the current business reality. From the boardroom to the shop floor, MCP helps organizations optimize AI for decision-making and use cases.
Find out more by watching a short video about MCP for AI applications.
Not long ago, manipulating large datasets, training machine learning models, or visualizing results required advanced programming skills and specialized statistical knowledge. Today, intuitive AI tools and natural language interfaces are allowing nearly everyone – not just data scientists, engineers, and technical experts – to analyze and act on data. In fact, nearly 8 in 10 organizations now […]
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 […]
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 […]
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 […]
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.
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.
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
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.
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.
In this short demonstration of Pretectum CMDM we show Pretectum Cognito supports you in creating or editing your customer master data classification tag metadata #customerdata #customer #AI #MDM #loyaltyisupforgrabs
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/
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 […]
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 […]
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 […]
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 […]
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 […]
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 […]
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.
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:Â Essential for personalized communication.
FullName:Â A consolidated field often derived or assembled for display purposes.
Email:Â A primary digital contact point. Pretectum supports email as a content data type.
Phone:Â Another crucial contact method.
DateOfBirth:Â Important for age-based segmentation and compliance.
Gender:Â A demographic identifier that can be validated against business area data in Pretectum.
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:Â 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:Â 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:Â Cumulative spending over time, indicating customer value.
AverageOrderValue:Â Insights into spending habits per transaction.
NumberOfOrders:Â Frequency of purchases.
LastOrderDate & FirstOrderDate:Â Markers for customer engagement and longevity.
HighestOrderValue & LowestOrderValue:Â Indicators of purchasing range.
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:Â Frequency of website or app engagement.
AverageTimeOnSite:Â Indicates engagement depth.
MostViewedPages:Â Highlights product or content interest.
SearchHistory:Â Reveals specific customer needs or desires.
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:Â Opt-in/out status for various communication channels.
SubscriptionStatus & SubscriptionStartDate/EndDate:Â Relevant for recurring service models.
FeedbackScore:Â Direct sentiment from customer surveys or reviews.
LoyaltyPoints:Â Track participation in loyalty programs.
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):Â A projection of the total revenue a customer is expected to generate over their relationship with your business.
ChurnProbability:Â The likelihood of a customer discontinuing their relationship.
NextBestAction:Â Suggested actions for sales, marketing, or service based on customer profiles.
SegmentMembership:Â Assigning customers to specific segments for targeted strategies.
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
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.
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.
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.
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:Â The RBAC matrix allows for precise control over who can view, edit, or unmask data, ensuring adherence to privacy regulations.
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:Â 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:Â 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
