Data Strategy & Transformation
Master Data Management
Master your data, enable data-driven strategic success.
How well do you understand the structure and relationships within your master data? Does your current data management practices ensure data accuracy and completeness, supporting key business processes and data-driven decisions? Where is your master data stored, and how well is it integrated with other data sources to provide a unified view? Do you use any compliance-driven measures to ensure security and privacy for your master data?
Effective master data management (MDM) relies on a holistic data-driven approach addressing the key questions. At Dataiso, we help you deploy a resilient MDM strategy that ensures data quality, improves operational efficiency, and enables informed decision-making.
As a master data management (MDM) consulting firm, Dataiso partners with organisations to design, implement, and optimise effective MDM solutions. We leverage our data management, integration, quality management, and compliance expertise to create tailored MDM strategies that empower your organisation to maximise the value of its master data. Explore how Dataiso can help you in your MDM initiatives.
Your challenges
Your challenges
Master data management (MDM) is a strategic discipline focused on creating a single source of truth for unified data management (UDM). By building robust data repositories and knowledge bases, this empowers data consistency, accuracy, and quality within an organisation.
Historically, organisations attempted to link heterogeneous applications through centralised interfaces. However, this often resulted in complex, expensive IT solutions that failed to achieve data harmony, leading to diverse challenges.
At Dataiso, we have a deep understanding of the unique challenges organisations face when integrating MDM into their operations to achieve success.
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
Organisations have siloed data scattered across disparate systems like ERPs, CRMs, and legacy databases, making MDM integration complex. Consequently, this impedes data consolidation and standardisation.
Data security and privacy concerns
A centralised 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, organisations 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
Winning organisations make informed decisions by understanding the strategic value of managed data. At Dataiso, effective MDM initiatives are most often built through crucial key factors for success.
Organisations 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 centralising and organising their master data, organisations can get a unified view of their business information.
MDM is all about ensuring consistent and accurate master data. Building data cleansing and standardisation processes, data validation rules and ongoing data quality monitoring is crucial throughout the MDM lifecycle.
A MDM platform isn’t a database, an ERP, or a CRM. Selecting the right MDM solution with the right features, scalability, and integration capabilities to meet business and technical needs is critical. Organisations should consider factors like vendor lock-in, open standards support, and ease of use during the selection process.
By developing a holistic data map to understand data sources and their relationship, organisations 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, organisations must break down departmental barriers. Standardising 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 approaches to suit specific needs: registry, consolidation, coexistence, and centralised.
This is the fastest and cheapest MDM approach to deploy, as it allows us to minimise the amount of data transferred by consolidating, instead, the globally unique identifiers of it into a repository of work. This enables us to offer a fast and inexpensive way to integrate and analyse data with the advantage of minimal intrusion into application systems.
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 visualisation.
With the coexistence model, we take consolidated MDM to the next level by adding the critical step of synchronising 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 organisations wishing synchronisation and real-time access to data concerning their quality.
With the centralised approach, we make the master data repository the main source of optimised 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.
Whether a registry, consolidation, coexistence, or centralised model is implemented, we use a People-Process-Technology (PPT) methodology to ensure its effectiveness. 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 organisations 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
- Maximise 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.
- Develop 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.
- Showcase 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 organisation’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.
- Maximise master data investments through efficient optimisation plans.
Master data management solution deployment
- Design tailored master data architectures to meet requirements, such as registry, consolidation, coexistence, and centralised 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.
- Optimise 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
- Accurately translate complex business and technical requirements into well-defined, comprehensive master data specifications.
- Identify relevant data entities, attributes, relationships, constraints, hierarchies, and taxonomies for efficient master data model design.
- Design comprehensive conceptual (CDM), logical (LDM), and physical (master) data models (PDM) using methods like Entity-Relationship (ER) modeling.
- Enable ISO 11179-compliant master data lineage with frameworks like DAMA-DMBOK through by developing metadata models.
- Ensure master data consistency, integrity, and quality by leveraging strong master data modeling standards and 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 standardisation
- Eliminate data inconsistencies with robust consolidation methods like data matching, reference data management, and data deduplication.
- Ensure data accuracy and reliability through flexible standardisation 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 standardisation processes.
- Maintain high master data standards with strong master data governance baselines and regular reviews.
- Handle multi-domain master data effectively through proactive data consolidation and standardisation 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 8000 and ISO 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 synchronisation
- Foster seamless master data integration through flexible data mapping and transformation processes.
- Enable real-time and batch synchronisation for accurate, up-to-date master data.
- Leverage data virtualisation for a unified data view.
- Handle data conflicts and versioning for data consistency and accuracy using advanced techniques lile 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 synchronisation.
- 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.
- Easily update master data repositories 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-premise deployments through detailed assessments like gap analysis, risk assessment, and more.
- Evaluate compatibility, scalability, and performance of existing master data environments using efficient benchmarking and stress-testing methods.
- Build comprehensive 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 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 8000 and ISO 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.
Our guarantees
Our guarantees
Reduced data management time and cost
Increased data confidence level
Secure and compliant data
Ready to equip yourself with powerful master data management (MDM)?
Deploying a master data management (MDM) solution delivers a unified, reliable data view for improved business and operational outcomes. However, it also requires careful planning, expert execution, and ongoing support from a skilled team to maximise its value.
At Dataiso, we believe every organisation has the potential to thrive with effective MDM. Are you ready to unlock yours and gain a competitive advantage? Get in touch to explore how we can help you achieve long-lasting success.
Deep dive
Deep dive
AI in the enterprise
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Ut elit tellus, luctus nec ullamcorper mattis, pulvinar dapibus leo.
Big Data and AI
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Ut elit tellus, luctus nec ullamcorper mattis, pulvinar dapibus leo.
Increase your sales
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Ut elit tellus, luctus nec ullamcorper mattis, pulvinar dapibus leo.