What are the Core Principles of Data Governance and How to Achieve Them?

 

In today's data-centric world, organizations are overwhelmed with vast volumes of data flowing from systems, apps, devices, and external partners. Yet, the real challenge isn't having data—it's governing it effectively.

As an Enterprise Architect, I’ve seen organizations succeed and fail based on how seriously they treat Data Governance (DG). It’s not a compliance checkbox; it’s a business enabler, a risk mitigator, and a competitive differentiator.

In this post, I’ll break down the core principles of Data Governance and show you how to implement them practically across your enterprise.

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What Is Data Governance?

Data Governance is the discipline of managing data to ensure it is accurate, available, secure, and used responsibly. It spans people, processes, and technology.

Think of it as the framework that answers:

• Who owns the data?

• What rules apply to it?

• How is data quality ensured?

• How is it secured?

• Who can access it, and for what purpose?

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🔑 Core Principles of Data Governance

Let’s explore the foundational principles that govern every strong DG program:

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1. Data Ownership and Stewardship

Principle: Every piece of critical data must have a clearly defined owner and steward.

How to Implement:

• Define Data Domains (e.g., Customer, Product, Financial).

• Assign Data Owners (business leaders) responsible for the data’s use, policies, and quality.

• Appoint Data Stewards (operational roles) who ensure metadata, lineage, and quality are maintained.

Example:
In a retail firm, the Head of Marketing could be the Data Owner of customer profile data, while a CRM Analyst acts as Data Steward, managing changes and monitoring duplicates.

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2. Data Quality Management

Principle: Ensure data is accurate, complete, timely, and consistent across systems.

How to Implement:

• Use data profiling tools (e.g., Informatica DQ, Talend, IBM IGC) to assess data quality.

• Define data quality rules (e.g., “Email must have @domain.com”, “Age must be > 0”).

• Implement automated data validation pipelines.

• Monitor data quality KPIs: completeness, uniqueness, validity, timeliness.

Example:
In a banking environment, if “Customer KYC Documents” are missing or expired, automated workflows can alert relationship managers or suspend onboarding.

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3. Data Accessibility and Usability

Principle: Data must be accessible to authorized users in a secure, standardized, and user-friendly manner.

How to Implement:

• Create a data catalog (e.g., Collibra, Alation, Informatica EDC) so users can search and discover data assets.

• Standardize APIs for consistent access.

• Build role-based access models via IAM systems (Azure AD, Okta).

Example:
A data scientist searching for “Product Sales by Region” can use the enterprise catalog to discover a trusted dataset from the BI team, with clear usage instructions and lineage info.

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4. Security and Privacy Compliance

Principle: Data must be protected against unauthorized access and comply with legal regulations like GDPR, HIPAA, and CCPA.

How to Implement:

• Classify data into sensitivity levels (Public, Internal, Confidential, Restricted).

• Implement data masking or tokenization for PII (Personally Identifiable Information).

• Maintain audit trails of who accessed what data, and when.

• Use data retention policies to purge obsolete data.

Example:
In healthcare, patient records must be encrypted and only accessible to authorized clinicians. Masking the SSN and storing logs of access helps achieve HIPAA compliance.

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5. Metadata and Lineage Management

Principle: Every data asset should have metadata and traceability across systems.

How to Implement:

• Use metadata harvesting tools to extract schema, definitions, and relationships.

• Build end-to-end data lineage diagrams to track flow from source to report.

• Encourage semantic consistency—use standardized terms and business glossaries.

Example:
In a global logistics company, tracking how “Shipment Delivery Time” is calculated from raw GPS logs through ETL pipelines to dashboard KPIs ensures trust and transparency.

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6. Policy Management and Compliance Enforcement

Principle: Clearly defined policies must govern how data is handled across the lifecycle.

How to Implement:

• Draft data usage policies and acceptable use standards.

• Embed rules into data platforms and enforce via data access governance tools.

• Conduct regular audits and generate compliance reports.

Example:
A financial institution may define that “Credit Score data can only be used for risk modeling” and restrict its usage in marketing campaigns through access rules.

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7. Culture and Accountability

Principle: Data Governance is as much about culture as it is about controls.

How to Implement:

• Launch data literacy programs for all business users.

• Gamify good governance—recognize teams with the highest data quality.

• Set up a Data Governance Council involving stakeholders from IT, business, legal, and compliance.

Example:
An insurance company’s “Data Day” event involves demos, awards, and workshops, creating engagement across departments about why data quality and security matter.

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✅ Steps to Implement Data Governance in Your Organization

Let’s put theory into action. Here’s a proven roadmap I’ve used with several Fortune 500 clients:

🔷 1. Assess Current Maturity

Run a Data Governance Maturity Assessment to identify gaps in people, process, technology.

🔷 2. Define Vision and Strategy

Establish a mission like: “To make high-quality, secure data accessible for all business-critical decisions.”

🔷 3. Build Governance Framework

Develop roles (Owner, Steward, Custodian), committees (Data Council), and artifacts (policies, glossaries).

🔷 4. Start with a Pilot Domain

Choose one data domain (e.g., Customer) and implement governance end-to-end.

🔷 5. Deploy Tooling

Use modern tools:

• Data Quality: Informatica DQ, Talend

• Catalogs & Lineage: Collibra, Alation, Azure Purview

• Access & Masking: Immuta, Protegrity

🔷 6. Roll Out Organization-Wide

Scale to other domains (Finance, Product, Operations) and embed governance into data lifecycle processes.

🔷 7. Monitor & Improve

Track metrics like:

• % of data assets cataloged

• % of users trained

• Reduction in duplicate records

• Audit policy violations

Data Governance is a marathon, not a sprint. It demands alignment between business goals, technical capabilities, and cultural mindset.

When implemented well, it not only reduces regulatory risk but unlocks real business value—fueling trusted analytics, AI initiatives, and confident decision-making.

As an Enterprise Architect, my advice is simple: Start small, think big, act fast. The cost of not governing data is far greater than the effort to govern it well.

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Let’s treat data like we treat money—asset-class, secured, managed, and wisely invested.

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Learn more about Informatica MDM SaaS here,

Things to Consider While Matching Records in Informatica IDMC MDM SaaS

 Master Data Management (MDM) plays a vital role in ensuring that an organization has a single, trusted view of its data entities—customers, products, suppliers, etc. One of the most critical steps in achieving this golden record is the matching process. In Informatica Intelligent Data Management Cloud (IDMC) MDM SaaS, record matching is highly configurable, but requires thoughtful design and tuning to ensure optimal results.

Here are some key things to consider while matching records in Informatica IDMC MDM SaaS:

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1. Understand the Matching Purpose

Before configuring match rules, determine the business goal:

• Are you eliminating duplicates (deduplication)?

• Are you linking records across sources (survivorship)?

• Are you identifying household or corporate relationships?

The type of matching (e.g., exact, fuzzy, survivorship) depends heavily on the use case.

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2. Choose the Right Match Rule Strategy

In Customer 360 SaaS, matching is driven by Match Rules, which can be:

• Exact Match: Fast and reliable for fields like national ID or email.

• Fuzzy Match: Useful for names, addresses, and other text where variation is expected.

• Hybrid Match: Combination of fuzzy and exact match fields.

Match rules should align with your data characteristics and accuracy needs.

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3. Prioritize Attributes Based on Trust

Every attribute used for matching should be evaluated on:

• Completeness (how often is it populated?)

• Accuracy (is the data trustworthy?)

• Volatility (does the field change often?)

For instance, email addresses might be unique but may change over time. Names may be stable but less unique.

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4. Handle Nulls and Blanks Smartly

Informatica IDMC MDM allows configuring how null or blank values are treated in match rules:

• Should blank be treated as a non-match?

• Should nulls be ignored?
  Misconfiguration can lead to false matches or missed duplicates.

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5. Leverage Match Confidence Scores

Each match pair gets a match score based on field-level confidence and rule weights. Use this to:

• Set thresholds for auto-merge vs. manual review.

• Fine-tune rules after testing.

This scoring helps balance precision (avoiding false matches) and recall (not missing valid matches).

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6. Test Match Rules with Real Data

Always test match rules with a representative data sample:

• Identify over-matching or under-matching cases.

• Review false positives/negatives.

• Refine rules iteratively using match logs and dashboards.

Testing is essential for building trust with data stewards and business users.

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7. Cluster vs. Pairwise Matching

IDMC MDM supports cluster matching (grouping similar records) instead of just comparing pairs. This improves:

• Performance

• Group-level insights

• Automated survivorship

Design match rules that take clustering behavior into account.

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8. Versioning and Auditability

Each change in a match rule or strategy should be:

• Version-controlled

• Audited

• Approved by data governance team

Ensure business and compliance teams are in the loop, especially for sensitive data like healthcare or finance.

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9. Consider Match Exceptions and Overrides

Not all matches should be auto-resolved. IDMC MDM allows:

• Manual override

• Stewardship workflows

• Match review dashboards

Design governance processes around match exceptions.

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10. Performance Optimization

Matching is compute-intensive. Consider:

• Reducing unnecessary fields in match rules

• Using appropriate match keys (blocking strategy)

• Scheduling match jobs during off-peak hours

Efficient match rule design reduces cost and improves user experience.

Matching is the heart of MDM, and in Informatica IDMC MDM SaaS, it offers powerful configurability. But with great power comes the need for careful planning, governance, and testing. By considering the above factors, you’ll ensure better match quality, data confidence, and business value from your MDM solution.

Learn more about Informatica IDMC MDM SaaS here,

The Latest Trends in Technology: What’s Shaping the Future in 2025

 

Technology continues to evolve at a breakneck pace, reshaping industries, economies, and daily life. As we move through 2025, several transformative trends are emerging that promise to redefine how we interact with the digital and physical world. Here’s a look at the most significant technological trends that are shaping the future right now.

1. Generative AI Goes Mainstream

Generative AI has moved beyond niche use cases to become a cornerstone of modern productivity and creativity. Tools like OpenAI's Sora and GPT-4.5 have democratized video creation, code generation, and content design. Enterprises are increasingly integrating generative AI into workflows, automating customer support, marketing content, legal document review, and even scientific research.

Trend Insight: AI agents are now capable of managing complex, multi-step tasks autonomously, moving closer to truly intelligent digital assistants.

2. Quantum Computing Breakthroughs

While still in its infancy, 2025 has seen several breakthroughs in quantum hardware and error correction. Companies like IBM, Google, and startups such as PsiQuantum are scaling up qubit counts and reliability, bringing us closer to practical quantum advantage. Financial modeling, materials science, and pharmaceutical research stand to benefit first.

Trend Insight: Governments and corporations are investing billions in quantum research to secure future dominance in this game-changing field.

3. AI + Robotics Integration

Robotics, supercharged by AI, is making significant strides in sectors like healthcare, agriculture, and logistics. Humanoid robots are now assisting in eldercare and hospitality, while autonomous delivery drones and warehouse bots are becoming commonplace.

Trend Insight: With AI-driven perception and decision-making, robots are becoming more adaptable and collaborative in human environments.

4. Spatial Computing & Mixed Reality

Apple Vision Pro and similar devices have brought spatial computing into the consumer space. Mixed reality (MR) is redefining digital interaction, blending physical and virtual environments for gaming, education, design, and remote work.

Trend Insight: The metaverse is maturing from a hype-driven concept into practical MR applications with real utility.

5. Personalized Health Tech

Wearable devices and AI-powered diagnostics are creating a revolution in personalized healthcare. Real-time biometric tracking, genetic analysis, and digital twins allow for predictive and preventive medicine tailored to individuals.

Trend Insight: The fusion of AI, genomics, and wearable tech is empowering consumers to manage their health proactively, while reducing healthcare costs.

6. Sustainable Tech Innovations

Environmental sustainability is driving innovation in green technologies. From carbon capture and battery recycling to AI-optimized energy grids and climate modeling, technology is playing a key role in tackling climate change.

Trend Insight: Startups and governments are embracing “climate tech” as both a necessity and an economic opportunity.

7. Cybersecurity in the AI Era

With the rise of AI-generated content and deepfakes, cybersecurity challenges are escalating. In 2025, there’s a major focus on AI-driven defense systems, zero-trust architectures, and quantum-safe encryption.

Trend Insight: The arms race between attackers and defenders is being reshaped by AI, making cybersecurity a priority across sectors.

8. Decentralized and Edge Computing

The limitations of centralized cloud models are giving way to decentralized infrastructure and edge computing. Data is increasingly processed at the source — from factories to self-driving cars — enabling faster, more private, and more reliable applications.

Trend Insight: Decentralized AI (like federated learning) is improving privacy while maintaining performance in sensitive environments.

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Final Thoughts

As 2025 progresses, it’s clear that the convergence of AI, quantum computing, spatial computing, and sustainability efforts will be the defining force of the next decade. The key for individuals, businesses, and governments is to adapt swiftly, embrace responsible innovation, and anticipate how these technological advances will reshape society and the global economy.

Configuring Survivorship in Informatica IDMC MDM SaaS

 This article provides a comprehensive guide to configuring survivorship rules within Informatica IDMC MDM SaaS, using the example of a person business entity. Survivorship is a critical process for establishing the "best version of truth" for data records by defining how conflicting information from various source systems should be resolved.

Understanding the Basics

The process begins by accessing the "person" business entity, which encompasses various components such as the data model, data flow, data quality rules, match survivorship, and event configurations. Before diving into the configuration, it's essential to identify several key prerequisites:

• Source Systems: Determine all the source systems that contribute data and will be part of the survivorship rules.

• Target Fields: Pinpoint the specific fields that require survivorship application. For instance, fields like first name, middle name, last name, and date of birth are typically subject to survivorship, while others like place of birth might not be.

• Survivorship Rules: Define the specific rules, including any maximum and minimum percentage scores, that will govern how data conflicts are resolved.

Navigating Survivorship Settings

The survivorship configuration options are conveniently located within the left-hand side panel of the business entity interface. Here, you'll find a clear display of fields like first name, middle name, and last name, along with their associated rules. It's important to note that some fields, such as "prefix" and "suffix," may inherit their rules from the parent level (in this case, the person business entity). Conversely, fields like "first name," "middle name," and "last name," often have specific ranks applied to them, indicating their priority in the survivorship process.

Step-by-Step Configuration

Configuring survivorship involves several key steps:

1. Source Ranking: Select a field (e.g., "prefix name") and configure its source ranking. All source systems previously set up in the Business 360 console will be available for selection and ranking.

2. Creating New Ranks: You have the flexibility to create new ranks by choosing specific source systems and adding them to the rank. For example, you might create "rank five" that includes "Informatica customer 360" and "default" source systems.

3. Applying Ranks: Once a new rank is created, it must be explicitly applied to the relevant field to take effect.

4. Rule Configuration (Decay Rules): A powerful feature is the ability to apply "Decay minimum and maximum trust score" rules. This involves selecting a rule type (Decay, minimum, or maximum), choosing a source system, and setting parameters such as the maximum trust score (e.g., 80%), minimum trust score (e.g., 40%), Decay unit (e.g., years), and Decay period (e.g., 10 years). After applying Decay rules, a visual graph is displayed, allowing you to further refine the Decay pattern (linear, rapid initial/slow later, or slow initial/rapid later).

5. Dependent Fields: Survivorship for one field can be made dependent on another. For instance, the "prefix name" might be evaluated based on the "full name" field.

6. Applying Rules to Multiple Fields: For efficiency, you can select multiple fields simultaneously (e.g., suffix name, title, description) and apply the same survivorship rules and source ranking to all of them in one go.

7. Saving Configuration: After making any changes to the survivorship configuration, it is crucial to save your work to ensure the new rules are applied.

By following these steps, users can effectively configure survivorship in Informatica IDMC MDM SaaS, leading to more accurate and reliable master data.

Learn more about Informatica IDMC here

Understanding the Consolidation Indicator in Informatica IDMC Customer 360 MDM SaaS

 The Consolidation Indicator (CI) field is a crucial element in Informatica IDMC Customer 360 MDM SaaS, playing a vital role in managing and consolidating data. This indicator helps track the state of data records as they move through the matching and merging process, ultimately leading to the creation of a "best version of truth" for each unique entity.

The CI field can take on four distinct values, each signifying a different stage in the data consolidation lifecycle:

• Match Dirty: This is the initial state for data records when they are first loaded or updated within MDM SaaS. It indicates that the record is new or has been modified and needs to be processed for potential matches.

• Match Index: After the indexing job runs, the CI value transitions from "Match Dirty" to "Match Index." In this state, records are prepared to participate in the matching process. If re-indexing is required for any reason, the CI value can be reset back to "Match Dirty."

• Matched: A record receives the "Matched" CI value once it has gone through the match and merge process. This applies whether a matching candidate was found or not.

• Consolidated: This is the final and most desirable state for a merged record. "Consolidated" signifies that a unique and accurate "best version of truth" record has been successfully created.

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Beyond these core values, there are several important aspects related to the CI field:

• Accept as Unique: If the "accept as unique" option is enabled for a record that undergoes the match and merge process but doesn't find any matching rules, its state will change from "Match Index" to "Matched." This allows the record to be treated as unique even without a direct match.

• XREF Record Updates: When an XREF (cross-reference) record is updated, its CI value automatically reverts to "Match Index." This ensures that the updated XREF record is re-evaluated through the match and merge process.

• CI Field Location: A key distinction in MDM SaaS compared to on-premise MDM is the placement of the CI field. In MDM SaaS, the CI field is located at the XREF level, not the business entity level. This granular placement provides more precise control over data consolidation.

• Extracting Consolidated Records: To extract only the consolidated records, a two-step extraction process is necessary. This involves creating one extract for business entity records and another for XREF records. These two extracts can then be joined to filter and retrieve only the consolidated values.

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In summary, the Consolidation Indicator is a fundamental component of Informatica IDMC Customer 360 MDM SaaS, providing clear visibility into the data consolidation journey and enabling robust data management practices.

Introduction to Reltio Master Data Management

 In today’s digital economy, data is the foundation of successful business operations. However, with data pouring in from countless sources — sales platforms, marketing systems, customer service channels, and more — many organizations struggle with fragmented, inconsistent, or outdated information. This is where Master Data Management (MDM) comes in, and Reltio is a leading player in this space.

Reltio Master Data Management is a modern, cloud-native MDM platform designed to help enterprises consolidate, cleanse, and unify their critical data assets. By creating a single, trusted source of truth, Reltio enables businesses to drive better decisions, improve customer experiences, and enhance compliance.

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A Brief History of Reltio

Reltio was founded in 2011 by Manish Sood, a data industry veteran who saw the limitations of legacy MDM systems firsthand. With its headquarters in Redwood City, California, Reltio set out to build a next-generation MDM platform, designed for the cloud and for the demands of modern data-driven enterprises.

Since then, Reltio has grown rapidly, attracting investment from major venture capital firms and building a customer base across Fortune 500 organizations in healthcare, life sciences, financial services, retail, and other sectors. Its platform is recognized by industry analysts (such as Gartner and Forrester) for its innovation, scalability, and business value.

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Why Reltio?

Unlike traditional on-premise MDM solutions, Reltio offers a cloud-first, API-driven architecture that supports real-time data processing and integration. Here are some standout features that make Reltio a compelling choice:

• Multi-Domain MDM: Manage customer, product, supplier, and location data in one place
• Cloud-native & scalable: Handles high-volume, high-velocity data seamlessly
• Data quality & governance: Cleansing, validation, survivorship, lineage tracking
• Graph technology: Discover and leverage entity relationships with connected graph models
• API-first & real-time: Modern integration to power digital ecosystems

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Detailed Business Use Cases with Attributes

Let’s look at some practical business use cases where Reltio is especially valuable, with examples of typical attributes managed in each:

1️⃣ Customer 360 for Financial Services

Use Case: A bank needs to create a unified customer profile to improve onboarding, risk assessment, and personalized product offerings.

Typical attributes managed:

• Name

• Address

• Social Security Number / National ID

• Date of Birth

• Contact numbers

• Email addresses

• Account numbers

• KYC documents

• Risk rating

• Credit score

• Relationships to other customers or accounts (beneficiaries, joint account holders)

Business value:

• Improved compliance (KYC/AML)

• Better fraud detection

• Personalized cross-selling opportunities

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2️⃣ Product 360 for Retail & E-commerce

Use Case: A global retailer needs a single view of its products across all sales channels, to drive consistency in pricing, promotions, and supply chain.

Typical attributes managed:

• SKU

• Product name

• Description

• Brand

• Price

• Categories

• Product images

• Inventory levels

• Supplier details

• Related products / bundles

Business value:

• Faster time-to-market for new products

• Accurate inventory planning

• Seamless omnichannel experience

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3️⃣ Healthcare Provider 360

Use Case: A healthcare network needs to manage consistent information about its providers (doctors, specialists, clinics) to streamline referrals and claims processing.

Typical attributes managed:

• Provider name

• NPI (National Provider Identifier)

• Specialty

• License details

• Affiliated hospitals

• Availability

• Contact information

• Insurance acceptance

• Certifications

Business value:

• Reduced claim rejections

• Improved care coordination

• Enhanced provider search tools for patients

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4️⃣ Supplier 360 for Manufacturing

Use Case: A manufacturer wants to manage supplier information globally to optimize procurement, quality, and compliance.

Typical attributes managed:

• Supplier name

• Tax ID

• Supplier location

• Product categories supplied

• Pricing agreements

• Contracts

• Quality certifications

• Risk assessments

• Relationship hierarchy (parent/subsidiary)

Business value:

• Reduced supplier risk

• Consolidated spend

• Better contract compliance

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Typical Industries Benefiting from Reltio

• Retail & E-commerce — better product and customer data for omnichannel
• Financial Services — single customer view for compliance and fraud
• Healthcare — provider and patient data management
• Life Sciences — compliance and product data governance
• Manufacturing — supplier and product data optimization

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Conclusion

Master Data Management is no longer a “nice-to-have” — it’s a business imperative. Reltio’s modern, flexible, and scalable approach helps enterprises build a trustworthy data foundation to thrive in the digital era.

With its rich history of innovation, strong multi-domain capabilities, and focus on real-time, API-driven architecture, Reltio is well positioned to support modern businesses as they navigate increasingly complex data challenges.

If you’re exploring a future-ready MDM solution to unify and unleash the power of your data, Reltio is absolutely worth a closer look.

Understanding NULL Handling in Informatica MDM: Allow NULL Update vs. Apply NULL Values

 NULL handling in Informatica MDM plays a crucial role in data consolidation and survivorship. Two key properties that determine how NULL values are managed are Allow NULL Update and Apply NULL Values. Let’s break them down:

1. Allow NULL Update on the Staging Table

This property controls whether a NULL value can overwrite a non-NULL value during a load job.

• Enabled: A non-NULL value in a column can be updated to NULL.

• Disabled: Prevents NULL updates, retaining existing non-NULL values.

• Behavior in Cross-Referenced (XREF) Records:

  • If a Base Object has a single XREF, a NULL can overwrite a non-NULL value.

  • For multiple XREFs, NULL updates are managed based on the Allow NULL Update setting.

  • To maintain consistency across single and multi-XREF records, a user exit can be implemented.

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2. Apply NULL Values on the Base Object

This property determines how NULL values are treated during the consolidation process.

• By Default (Disabled):

  • NULL values are automatically downgraded, ensuring non-NULL values survive.

• When Enabled:

  • NULL values are treated normally with trust scores.

  • NULLs may overwrite non-NULL values during put-operations or consolidations.

  • Higher trust scores allow NULL values to survive in the Base Object.

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3. Comparison: Allow NULL Update vs. Apply NULL Values

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4. How MDM Determines NULL Survivorship?

For each XREF column, MDM follows these steps:

1. Identify the source stage table:

   • If the XREF record has a non-null STG_ROWID_TABLE, use it.

   • If not, use ROWID_SYSTEM to find the source stage table.

2. If only one source stage table exists:

   • Use the Allow NULL Update setting of that table.

3. If multiple source stage tables exist:

   • If all have the same setting, use it.

   • If inconsistent, refer to Apply NULL Value setting in the Base Object.

4. If no stage table is found, use Apply NULL Value setting in the Base Object.

5. If Allow NULL Update is false, the trust score of NULL values is significantly downgraded, reducing the likelihood of NULLs surviving.

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5. Operations Affected by NULL Handling

All operations involving Best Version of Truth (BVT) calculation follow these rules, including:

• Load/Put/CleansePut

• Merge/Unmerge

• Recalculate BVT

• Revalidate

By understanding these settings, you can better manage data integrity and ensure accurate MDM processing!