How do you handle changes and updates to master data domains and what are the best tools and methods?

Master data domains are like the VIP sections of a nightclub - exclusive, organized, and essential for a smooth operation. As a Senior Manager in Business Intelligence, I can't stress enough the importance of defining these domains. It's like setting the foundation for a house - get it right, and everything else falls into place. With a clear domain model, you'll have a roadmap for managing changes and updates, ensuring your data stays as fresh as the latest dance moves. ???? #MasterDataManagement #DataIsKing #StayOrganized

Master data domains are logical groupings of related entities with shared characteristics, rules, and policies. ?? For instance, a customer domain encompasses entities like name, address, contact details, preferences, and transactions. Similarly, a product domain includes SKU, description, price, category, and inventory. ??? Defining these domains is crucial for effective management, aiding in identifying scope, ownership, and governance. Utilizing tools like domain models and data dictionaries facilitates documentation of definitions, attributes, and relationships within each domain. ?? This foundational step streamlines change and update management, enhancing overall data governance and ensuring consistency.

I'd manage updates in master data domains as following: - Governance: Set policies for change management. - MDM Tools: Use tools like Informatica MDM, SAP MDG, or IBM InfoSphere MDM for centralized management. - Version Control: Keep a history of changes. - Workflow Automation: Automate approval processes to ensure quality. - Audits: Regularly review data for accuracy. - Training: Educate stakeholders on MDM processes. Employing MDM tools and processes ensures data accuracy and consistency, facilitating effective change management within master data domains.

Master data domains typically include Customer, Product, Supplier, Location, and Reference Data, among others. I often emphasize the importance of including aggregated transaction values within the master data model to provide greater business context. For example, rather than presenting only demographic information in a single customer view, incorporating metrics such as Total Purchased Amount from transactions makes the data more valuable and appealing to the business. However, it is important to strike a balance and avoid turning the MDM into a data warehouse.

A great tool here to identify which domains are truly 'master' is to work backwards from all of the most often used reports / dashboards within your organization, and determine data is used most often across various business functions. A given 'domain' may be used widely within a single function, but if its not used consistently across multiple functions, it would not typically be considered master data.

To establish master data governance, create a robust framework encompassing roles, responsibilities, processes, standards, and metrics for ensuring master data quality, consistency, and security. Form a governance committee or board to oversee strategy, policies, and procedures. Leverage a governance tool or platform for data stewardship, quality management, lineage, and cataloging. Ensure authority, accountability, and auditability in each domain. Implement regular updates and changes with a focus on clear communication and documentation. Prioritize data integrity, security, and compliance. Utilize key performance indicators (KPIs) to measure governance effectiveness and drive continuous improvement.

Governance frameworks, like that provided by DAMA (via the DMBOK) can be a helpful tool, but I would strongly recommend that instead of having a scope of 'implement a framework', you should instead take more of a top-down, outcome-driven approach to early stage MDM efforts. In other words, implement MDM to solve a specific business problem, and then implement the minimum amount of governance needed to solve the problem. Taking more of a bottoms up approach is problematic, because you'll never ever truly be 'finished' implementing a governance framework. Instead, focus on solving a business problem with MDM, and implement only what's necessary from a governance perspective to solve that problem. Then do it again, and again, and again...

This entire suggested flow is taking a very technology and data-management centric approach - which is fine - so long as you also ensure that you have a deep understanding of how end users are creating, and consuming, master data. You cannot implement a master data workflow without also understanding the full lifecycle of a given domain in your organization, from cradle to grave. Who creates a customer record? In what system? Under what conditions? Who can edit it? etc. etc. etc. So while I agree that you need to define governance policies and business rules that manage a master data lifecycle, lets not also forget that doing so requires a deep understanding of how the business is interacting with that data.

A workflow mechanism or tool is highly recommended to support the Master Data lifecycle, and mostly MDM tools provide that functionality. If a duplicate customer name is identified from two systems, they need to get staged through matching for validation by Stewards and custodians, then those records need to get merged to form single source of truth. In order to have seamless processing of these steps, workflows are required.

Implementing a master data lifecycle starts with a defined roadmap: 1. Assessment and Planning: Assess current practices, identify gaps, set clear objectives with stakeholder input. 2. Design and Development: Define stages—data capture, validation, enrichment, distribution, archiving—with detailed plans. 3. Technology and Tools: Choose tools to automate processes like ingestion, transformation, synchronization, deletion. 4. Governance and Standards: Establish frameworks for consistency and compliance. 5. Monitoring and Performance: Develop metrics to monitor performance and ensure improvement. 6. Communication and Training: Ensure stakeholder communication and provide adoption training.

Adopting master data standards is essential for maintaining high-quality, consistent, and accurate data across diverse systems and applications. Utilizing a standardized model or schema helps define the format, structure, and semantics of each master data entity and attribute. This prevents issues like data duplication, inconsistency, and errors. Employing tools and services such as data profiling, cleansing, matching, and deduplication ensures the enforcement and validation of these standards. Consistent implementation of master data standards enhances data integrity, facilitates efficient updates, and supports a seamless flow of information across the organization.

Another way to describe these 'standards' is by using the phrase 'data governance policies', because that's exactly what they are. The rules you define for things like the expected quality of a record, the structure of a record, or the definition of a domain, are all an outcome of a governance process. Governance policies define the rules, people and software systems (including MDM) help to enforce the rules.

Can you do MDM without MDM software? The correct answer here is technically 'yes', but for the overwhelming majority of companies serious about MDM, it's not something I would usually advise. This is because manual approaches lack the scale, flexibility, configurability, and auditability needed to deliver any meaningful and prolonged value from the implementation of an MDM program.

Leverage master data technologies for efficient master data management! Employ software and hardware solutions for creating, maintaining, and governing master data across systems. Optimize with a robust technology model or architecture, defining components, layers, and interfaces. Implement using tools and platforms for seamless integration - think data integration, modeling, storage, and access. Ensure scalability, performance, and adaptability to handle changes. Embrace ?? updates and changes with enhanced functionality. Design a resilient system that aligns with your organizational needs. ?? Maximize efficiency, accuracy, and compliance through strategic master data technology utilization!

I have been in the position where a company (actually just the "Chief Architect" who didn't like COTS) wanted to build their own system. I said, "You're not a software development company! You don't have the personnel or the skills. The companies that make MDM software have been doing it for years with thousands of customers around the world - and there's a reason for that." The response was, "Our engineers say they can do it." I tried to tell them Why what they wanted to do could not be done, but they insisted the could. Several months later, they finally admitted they couldn't do what they said they could. (There's even more to the story as well, that you wouldn't believe ??)

Implementing a Master Data Management (MDM) program with enterprise-level, multi-domain MDM software significantly eases and accelerates the process. While the tool may sometimes be used as a black box, successful go-live MDM projects I've encountered empower business users to maintain the master data. Therefore, the tool's flexibility to be tailored to business needs is crucial. An intuitive and user-friendly interface is always the key to success.

Sadly, through data provided by Gartner, we know that 90% of companies actually don't do this step - but they absolutely need to. The key here is to focus on *business* performance, and not necessarily the performance of the data itself - as typically measured through data quality KPIs. Examples of business KPIs include things like 'revenue per customer' or 'total cost of inventory' or 'sales conversion rate' - and its these metrics that you need to link to specific DQ metrics, where you attempt to model the business impact that comes as a result of improvements in master data quality.

In my opinion, performance of the Master data should be measured by its use. Many times, there is dormant data sitting in the system which is not used for many years holding space. It is essential to run health checks on the master data, simple reporting on when was the last time the data was used or consumed would help identify unused data and make right decisions to keep your systems healthy. Building thoughtful KPIs of the core master data and the extended components has helped me maintain a good quality and data compliance. Very specific example could include KPIs built on last modified date.

Ultimately, MDM is about delivering value to the business. The #1 killer of MDM initiatives is a failure to quantify and deliver value in a timely manner, so above all else, keep your scope small, and be as agile as possible in how you implement. I strongly recommend starting with an analytical style of MDM that produces a '360 degree view' of something (like a customer or supplier), which can then be used by business leaders to optimize a critical business process.

Master Data Management (MDM) involves various aspects, as it requires producing and consuming properly governed golden records. Different MDM implementation styles and integration technologies must be considered. Golden records can significantly improve downstream analytics. Enhancing business users' understanding of golden records with a business glossary is also beneficial. My advice is that there is no absolute right or wrong in choosing the combination of these elements. Be pragmatic and agile, starting small and scaling gradually.

The problem is that existing MDM solutions focus primarily on metadata cataloguing and governance from a business perspective, providing a business context with no direct link to the technical data. Data Piloting offers a unified, real-time view of enterprise data and metadata, directly linking the business context to the technical context. This real-time contextualisation enables more informed and reliable decision-making, ensuring that data issues are identified and resolved before they impact the business.