Master Data Management
Master your data, enable data-driven strategic success.
A vast library is of little use without a proper cataloging system. Similarly, effective data management requires master data management (MDM). MDM provides a single source of truth (SSOT) across the organization—a true guarantor of (meta)data accuracy, consistency, and accessibility. However, it requires expertise to be implemented effectively.
As your master data management consulting partner, Dataiso helps you establish this SSOT for your critical data to reduce critical errors and missed opportunities. We work with you to identify, cleanse, and manage your master data, achieving data quality and reliability across all your systems.
Your challenges
Your challenges
Master data management (MDM) is a major enabler of unified data management (UDM)—which drives data harmony, streamlined access, and improved decision-making. For all these advantages, MDM initiatives still face barriers.
Dataiso has identified key challenges compromising the effectiveness of MDM.
Unsuitable MDM platform
MDM platforms vary widely in features and scalability. Selecting one lacking critical functionalities or growth capacity can hinder implementation and performance, leading to failure.
Siloed and inconsistent data
Organizations have siloed data scattered across disparate systems like ERPs, CRMs, and legacy databases, making MDM integration complex. Consequently, this impedes data consolidation and standardization.
Data security and privacy concerns
A centralized MDM raises security risks. Compliance with privacy regulations, like GDPR (General Data Protection Regulation) and CCPA (California Consumer Privacy Act), increases MDM complexities.
High implementation costs
Building an MDM system can be expensive, including software licenses, hardware infrastructure, and ongoing maintenance costs. As a result, organizations often delay MDM implementation.
Resistance to change
Users accustomed to working with existing systems might resist adopting new processes and workflows associated with MDM. Unfortunately, effective change management strategies are often underestimated.
Our key factors of success
Our key factors of success
Successful implementation of a single source of truth for your master data relies on several key factors. At Dataiso, we’ve identified seven to ensure effective MDM and maximize its long-term value.
Organizations must implement effective data governance for successful MDM. It includes defining data ownership, enforcing data access controls, and establishing transparent policies to ensure security, retention, and proper usage.
A well-structured data repository is essential to unlock the business value of MDM. By centralizing and organizing their master data, organizations can get a unified view of their business information.
MDM is all about ensuring consistent and accurate master data. Building data cleansing and standardization processes, data validation rules and ongoing data quality monitoring is crucial throughout the MDM lifecycle.
An MDM platform is not a database, ERP, or CRM. Choosing the right MDM solution with appropriate features is vital. Organizations should consider factors like vendor lock-in, support for open standards, and ease of use to better meet their needs.
By developing a holistic data map to understand data sources and their relationship, organizations can master data domains with ease. Additionally, the use of data lineage and data profiling enables a comprehensive overview of the data landscape.
To empower efficient MDM initiatives, organizations must break down departmental barriers. Standardizing data definitions and processes, and promoting a culture of open data sharing, improve data accessibility and decision-making.
Successful MDM implementation requires complete stakeholder buy-in. This includes a wide range of initiatives: user training and education, user-friendly MDM interfaces, ongoing support, incentives, and more.
Our approach
Our approach
We offer four main MDM implementation styles to suit your specific needs, whether operational MDM or analytical MDM: registry, consolidation, coexistence, and centralized.
This MDM approach offers rapid, cost-effective deployment by consolidating globally unique identifiers, minimizing data transfer. This enables fast, inexpensive data integration and analysis with minimal application disruption.
Through this approach, we consolidate data from multiple sources in a master data hub to create a single source of truth (SSOT) or golden record. Consolidated hubs are inexpensive and easy to set up, providing a quick and efficient way to facilitate enterprise-wide reporting for better data analysis and visualization.
With the coexistence model, we take consolidated MDM to the next level by adding the critical step of synchronizing master data to sources, thus creating a master record that “coexists” both in the MDM repository and at the original application system. We adopt it for organizations wishing synchronization and real-time access to data concerning their quality.
With the centralized approach, we make the master data repository the main source of optimized application data. We store and manage basic data attributes using techniques such as linking, cleansing, matching, and enrichment algorithms to enhance data. The enhanced data can then be republished back to their respective source system.
Whatever the model may be, we apply a People-Process-Technology (PPT) framework to ensure optimal outcomes. We leverage this collaborative model to help you drive successful MDM initiatives.
Our services
Our services
Dataiso provides cutting-edge master data management services to help organizations achieve real-world results. We go beyond theoretical methods, delivering bespoke solutions that address your specific challenges and unlock new opportunities.
Master data management strategy and roadmap
- Maximize return on investment (ROI) by aligning MDM objectives with the overall strategy.
- Drive growth by identifying high-impact opportunities where MDM can make a significant difference.
- Create a comprehensive MDM roadmap for successful implementation strategies.
- Define the appropriate MDM technologies and tools to meet unique business needs and drive innovation goals.
- Strengthen master data scaling strategies by implementing MDM operations (MDMOps) principles.
- Demonstrate MDM value through compelling proofs of concept (PoCs) and proofs of value (PoVs).
Master data audit and diagnosis
- Assess all existing master data practices, policies, and technologies.
- Identify gaps between the organization’s current state and master data best practices, including both technical and functional discrepancies.
- Assess master data health and observability, including master data models, quality, consistency, and accessibility.
- Evaluate master data systems’ strengths and weaknesses using methods like performance testing and user feedback.
- Review master data ethics, sustainability, security, privacy, and compliance.
- Benchmark master data maturity against industry standards with proven maturity models.
- Maximize master data investments through efficient optimization plans.
Master data management solution deployment
- Implement tailored master data architectures for registry, consolidation, coexistence, or centralized styles.
- Integrate best-in-class master data components, workflows, data quality processes, and tools.
- Ensure seamless master data deployment on cloud platforms, on-premises infrastructure, or hybrid environments.
- Optimize master data infrastructure through smarter performance tuning techniques and efficient resource allocation.
- Strengthen master data security and governance through proactive measures leveraging data protection and privacy best practices.
- Streamline and scale deployments with robust MDM operations (MDMOps) practices.
Master data model design
- Design custom conceptual, logical, and physical master data models using proven approaches like Entity-Relationship (ER) modeling.
- Enable traceable master data lineage with ISO/IEC 11179-compliant metadata structures based on frameworks such as DAMA-DMBOK.
- Ensure master data consistency, integrity, and quality through strong MDM design practices.
- Design tailored conceptual, logical, and physical master data models using proven approaches like Entity-Relationship (ER) modeling.
- Enable traceable master data lineage with ISO 11179-compliant metadata structures based on frameworks such as DAMA-DMBOK.
- Ensure master data consistency, integrity, and quality through strong MDM best practices.
- Take control of master data model versioning and change management through effective version control systems.
- Enable ongoing master data model refinement and validation with regular reviews and updates.
Master data consolidation and standardization
- Eliminate data inconsistencies with robust consolidation methods like data matching, reference data management, and data deduplication.
- Ensure data accuracy and reliability through flexible standardization rules and processes.
- Unify disparate data sources into a single, trusted source of truth for authoritative golden records.
- Streamline ongoing data cleansing and enrichment with efficient standardization processes.
- Elevate high master data standards with strong master data governance baselines and regular reviews.
- Handle multi-domain master data effectively through proactive data consolidation and standardization strategies.
Master data quality management (MDQM)
- Identify and resolve data quality issues through comprehensive root cause analysis (RCA) procedures.
- Ensure accuracy, consistency, and reliability with robust MDQM frameworks based on industry standards like ISO/IEC 8000 and ISO/IEC 25012.
- Measure the effectiveness of MDQM initiatives using relevant data quality metrics and key performance indicators (KPI).
- Fix data quality issues through efficient data profiling and cleansing strategies.
- Enhance master data with valuable information by leveraging tailored data enrichment methods.
- Enable efficient data deduplication with advanced data quality techniques like entity resolution, data matching and merging processes.
Master data integration and synchronization
- Facilitate seamless master data integration through flexible data mapping and transformation processes.
- Enable real-time and batch synchronization for accurate, up-to-date master data.
- Leverage data virtualization for a unified data view.
- Handle data conflicts and versioning for data consistency and accuracy using advanced techniques like data reconciliation.
- Ensure traceability and accountability through efficient data lineage and audit trail processes.
- Allow better control of record and data ownership for improved master data integration and synchronization.
- Enable master data scalability and adaptability with robust change management processes.
Master data lifecycle management (MDLM)
- Define clean, robust data capture methods through tailored data acquisition processes.
- Streamline master data repository updates with valuable insights by employing efficient data enrichment techniques.
- Enforce data consistency and integrity by leveraging robust data classification and validation methods.
- Prevent data breaches and maintain trust with proactive data loss prevention (DLP) measures.
- Track changes and maintain regulatory compliance with effective data lineage and auditing methods.
- Securely handle obsolete data by implementing advanced data archiving and deletion protocols.
Master data migration
- Assess master data migration requirements for cloud or on-premises, including gap and risk analysis.
- Validate existing master data compatibility, scalability, and performance through rigorous benchmarking and stress-testing methods.
- Build robust master data migration plans addressing technical, operational, and business specifications.
- Implement comprehensive cutover and rollback plans, leveraging robust testing and validation methods.
- Seamlessly migrate master data assets to the target platform, with minimal disruption and risks.
- Leverage enhanced features and patches, by ensuring security and reliability with upgraded platform versions.
- Validate (master) data integrity and quality post-migration for better accuracy, completeness, and consistency of business-critical information.
Master data security and governance
- Safeguard master data landscape with efficient security measures (e.g., data classification, access controls) based on industry standards like ISO/IEC 27001.
- Maintain transparency, accountability, and compliance with regulations like Data Act, GDPR, and CCPA through future-proof master data governance.
- Strengthen master data confidentiality, integrity, and availability using a comprehensive CIA triad model aligned with industry standards like ISO/IEC 8000 and ISO/IEC 25012.
- Uphold fairness, explainability, and privacy by addressing master data ethics and bias throughout the master data lifecycle.
- Enhance master data monitoring and preventive methods through proactive data observability.
- Integrate master data governance with overall data governance frameworks and best practices, including comprehensive policies and procedures like DAMA-DMBOK (Data Management Body of Knowledge) and ISO/IEC 38500.
Your benefits
Your benefits
- Improved data quality and consistency.
- Increased operational efficiency through streamlined data management.
- Enhanced decision-making with a single, trusted data source.
With a well-executed MDM strategy, your master data stops being a constraint—and becomes a sustainable driver of enterprise performance.