What is master data management?
Master Data Management (MDM) is the technology, processes and governance enabling an organisation to link and share critical master data.
Master data are the objects of a business - typically people, places, things and concepts. For example, a supplier (person) may provide a service (thing) under the terms of a contract (concept). Or a customer (person) may buy a product (thing) from a store (place).
Until recently, technology to MDM modelled these logical groups seperately - as so-called domains such as Customer, Employee, Product and Supplier. As our examples show, real business processes typically use multiple domains, meaning that organisations have been forced to implement multiple MDM solutions. In fact, Gartner Research shows that most organisations will have deployed two or more MDM solutions to try to address enterprise MDM challenges.
This approach is counterproductive.
It defeats the goal of creating a linked view of enterprise master data. It makes it difficult to support and improve key business processes, which are dependent on multiple domains, to extend master data management solutions beyond the initial use case, or to answer real business questions such as “How many of my suppliers are also employees?”
The problems created by domain centric MDM solutions are driving the emergence of a new paradigm – multidomain MDM solutions.
What is multidomain MDM?
Multidomain MDM is a software solution that manages multiple types of master data in one repository. Rather than delivering a predefined data model, that restricts use of master data to a single “artificial” domain, multidomain solutions focus on supporting key business processes and master data across all necessary domains.
Common sense tells us that if you are trying to create an accurate master data model for your company then a multidomain MDM approach will get you there faster. This is because a multidomain solution can store and share all master data – customers, employees, products, places and more – in one unified system.
Linked master data, across multiple domains, makes it easy to answer questions like “Which customers are buying which products?” and “Which suppliers are also employees?”
Multidomain MDM models your business, allows you to improve process efficiencies, and supports key analytics goals, such as the implementation of big data.
Benefits of multidomain MDM
A multidomain MDM approach delivers several benefits:
Forrester Research shows that the average corporate has at least three master data domains requiring management. Implementing each of these domains using separate solutions creates new data siloes that cannot easily interact without additional integration.
Supports growth from a departmental to an enterprise view of master data:
Domain centric approaches may be seen as departmental solutions. The HR department benefit from an employee master, Procurement benefits from a Vendor master, Sales benefits from the Customer master, and so on. These tactical solutions are not easily extensible to serve additional purposes.
Multidomain MDM solutions, on the other hand, can start as tactical solutions for a specific problem or department. They can be easily extended to add additional data elements and to support new business processes and departments. This saves time and money as existing rules and infrastructure are reused.
Reduced implementation and maintenance costs
The reduced complexity and reuse inherent in a multidomain solution reduce the redundant cost, training and maintenance that result from managing multiple master data siloes. The costs of implementing two separate systems (even from the same vendor) can be close to double the costs associated with implementing two domains in a true multidomain platform.
Improved data governance and data quality.
It is commonly accepted that data governance and data quality consume between 30% and 50% of any MDM budget. Siloed MDM approaches may , in fact, create new data quality problems as different standards may be applied to similar attributes.
A unified technology platform helps to ensure that standards are applied consistently, saving money and improving data governance and data quality. More importantly, a unified approach changes thinking – requirements are planned for and delivered based on an enterprise approach, rather than delivering siloed domains for different business stakeholders without considering the enterprise need.
A unified solution also reduces the complexity for data stewards, and other stakeholders, by reducing the number of applications and interfaces they need to work with to perform standard MDM task, such as merging duplicate records.
Improved analytics and process support
Your business processes and analytics require an integrated view of all key master data elements supporting them. For example, a corporate real estate environment must manage leases, properties, rental units and suppliers in order to ensure optimal use of each rental property. Critical KPIs, such as Lease Renewal dates and Total Running Cost per building, cannot be accurately measured without an integrated, accurate view of all these master data elements.
Trying to get this kind of view from a MDM hub designed for a single domain is next to impossible.
A multidomain MDM solution, on the other hand, breaks down siloes within the company, often for the first time. This improves performance, and accuracy, because everyone in the business can now work from the same underlying data rather than wasting time every month reconciling multiple sets of redundant master data.
Where single domain solutions are restricted to specific data models and purposes, multidomain MDM solutions can be easily adapted to support new business processes or departments.
The ability to adapt the technology to evolve with rapidly changing business needs is critical.
Reduced risk of MDM failure
Single domain solutions were once the only option available.
Rather than creating a single, unified view of enterprise master data, these solutions may have simply created new siloes of data within the organisation, with competing standards and a lack of enterprise data governance. Given that integrated and shared master data are the desired result of MDM this can lead to the failure of the entire initiative.
What to look for in multidomain MDM
Tools must provide support for common MDM governance functions, such as merging duplicate records. A unified view should support governance across all domains.
Master Data Management without data quality is just data integration. Tools must support data standardisation, enrichment, matching and the building of a common record without relying on the intervention of data stewards. Address validation and geocoding could be useful features for Customer, Supplier and Location data.
Multiple deployment options:
Tools should support both pure hub and pure bus based integration approaches, along with variations, in order to cope with the complexities of existing corporate architectures.
Integration should support both batch and real time APIs
Platforms must preserve the raw data, identify how matches are achieved and provide an audit trail for changes to golden records as they are modified.
Choosing between single and multidomain MDM platforms
The advantages of a single domain solution – ease of deployment for a specific domain – are now delivered by leading multidomain solutions.
The benefits of multidomain MDM are, however, impossible to replicate using single domain solutions. Organisation should think very carefully before investing in a limited solution that cannot easily adapt to future needs.
Approach for implementing multidomain MDM
Identify key business processes to be supported
Not all data is equally important. Business priorities should be the focus. Create a focussed scope by supporting key business processes first.
Identify key business stakeholder relying on these processes – these include producers, consumers and owners of data
Finding data owners and other stakeholders can be a key challenge. Process owners can often be more easily identified and would probably have accountability for the data supporting their processes
Implement simple data governance structures and workflows for data standards, priorities, definitions, match rules, etc.
Data governance is a critical success factor to ensure business alignment for the MDM project.
Governance should enable collaboration between all stakeholders, identify key data requirements and standards, and create the context and priorities for successful MDM. Without data governance, MDM projects frequently unravel due to conflicting needs and political agendas.
Identify master data supporting these processes and the producing and consuming systems
What master data is required, where is it currently stored, how is it changed?
Conduct an MDM Readiness Assessment to on the key master data.
Are there any unpleasant surprises lurking in the data? Data Quality efforts typically take much longer and cost much more than planned.
An MDM Readiness Assessment is vital to understanding and managing the data risk inherent in MDM, to define a data model that will minimise data migration failures, and to keep implementation time scales within scope and budget.
The MDM Readiness Assessment will provide you with critical metadata for planning the most cost effective data models, migration strategy and implementation plan.
Develop the master data model
What attributes must be supported to support key processes?
The metadata generated during the MDM Readiness Assessment is a key feed into this model, as are the business requirements defined by your Data Governance team.
The goal is to define an extensible model that can support your existing requirements and that allows you to map all source system attributes to a “best fit” physical model. However, the model should not be more complex than required - the more data that is added the more difficult to manage
Generate and test the master data
Enterprise data quality capabilities must be used to standardise and consolidate various representations of each record into the single best “golden” record.
You need to be able to trust your match process – no data stewards have the time to manually validate matches.
Implement Data Quality metrics
Is your master data fit for purpose? Master data that is of poor quality can quickly spread to consuming systems, degrading the overall quality of data within your business.
Data stewards, and other interested stakeholders, should monitor data quality dashboards to ensure that master data remains fit for purpose, and to allow corrective action if it reaches unacceptable levels.
Modify the producing and consuming systems
Existing systems may need to be modified to use the new master data. At the very least, systems should not add new master records without first doing a check to see whether the record already exists in the master system. If possible, leverage the same data quality solution as used in the MDM application for this.
Enhance and maintain
Multidomain master data is not static.
As business processes improve, additional data elements may need to be added, or data quality standards and policies may change to support new business requirements.
Your data governance process should drive ongoing enhancements and changes to the MDM solution to keep it relevant.