Knowledge Base
Beginner's Guide to MDM
What is MDM?
A set of disciplines, processes and technologies, for ensuring the accuracy, completeness, timeliness and consistency of multiple domains of enterprise data - across applications, systems and databases, and across multiple business processes, functional areas, organizations, geographies and channels.
Common MDM Themes
Despite the confusion over a clear definition of MDM, by researching various thought-leaders and publications focused on MDM there are some constant themes that come through:
- MDM is focused on Master or Reference Data (yes an obvious point but important to make the distinction with other information such as transactional data)
- Certain dimensions of data quality are critical to enabling effective MDM (eg. timeliness, accuracy, completeness, meaning)
- Continuous data improvement and a well-managed data quality strategy are essential
- Technology is a key component in providing an MDM platform but MDM requires a whole lot more than just a technical solution, in particular data governance, which will be the subject of a separate article
- Harmonizing and synchronising multiple data items is extremely important in creating a "single version of the truth" for your business objects
- MDM typically delivers a "hub" infrastructure to source and distribute master data
- Creating a single, shared reference point for a business entity is pivotal
- Fostering cross-organisational commitment and change management through a data governance framework is also essential
- MDM is in its infancy as a discipline, there are relatively few experienced practitioners, it can be tough to implement and it can take significant effort to achieve buy-in at a senior level
What is meant by Master Data and Reference Data?
Every organisation typically has data on customers, products, employees and physical assets but these data items are seldom held in one location.
They are often physically scattered around the business in various applications, spreadsheets and even physical media such as paper and reports. What makes matters worse is that different parts of the business will have different concepts and definitions for the same business entity and relationship. For example, an employee may be recorded in a payroll, HR, training and expense management system of an employer but back in the real world they are the same person. Typical examples of master data include (sourced from Master Data Management by David Loshin): Customers, Employees, Vendors, Suppliers, Parts, Products, Locations, Contact Mechanisms, Profiles, Accounting Items, Contracts, Policies.
What is the difference between Master Data and Reference Data?
External data is a typical form of reference data whereas standard business objects such as customer, employee, parts and so on are classed as master data. When building MDM strategies, external data becomes incredibly important for creating a surrogate source of "truth". Some people consider reference data (such as standardized lists of values) as one type (or domain) of master data.
What Technology is required for MDM?
3 components are required to implement MDM:
- A hub: There are 3 flavours. A persistent hub takes all of the business critical data into the hub from the source system. In a registry hub only the identifying information and key record identifiers are copied to the hub. In a hybrid hub an element of both options is used allowing more fine-grained control of what goes into the hub.
- Data integration or middleware: There is a need to synchronise any data quality improvements that take place so that the benefits are maintained and quality is continuously improved. There are also various other interfacing and workflow type technologies that are incorporated in a typical MDM "stack" structure.
- Data Quality Tools: There are 5 categories for these: auditing, cleansing, parsing/standardisation, hybrids. The hybrid tool contains elements of the other data quality functions and may also incorporate ETL (extract/transform/load) capabilities. The other functions are typical of most data quality initiatives.
Why is MDM so popular now?
- MDM issues impact the business: What is a business without its customers, its products and its employees? Master data is some of the most important data that an organisation holds and there is no choice but to fix the issues of the past; even minor issues with master data cause viral problems when propagated across a federated environment. A recognition that enterprise MDM defines competitive advantage has grown significantly in the last decade.
- Increasing complexity and globalization: Master Data Management really hits right to the point of the drivers for an Information Development approach. Organisations are becoming increasingly federated, with more information and integration globally that ever before. Reducing the complexity is essential to a successful approach. Globalization led to a variety of additional problems and complications from the data management perspective. This includes multi-lingual and multi-character set issues, and 24x7 data availability needs driven by global operations. The number of channels enterprises receive and provide information has also grown significantly with the recent evolution of the Internet and voice recognition technologies.
- All sides see a major opportunity: MDM is a big, complex problem and is therefore an opportunity for product vendors and systems integrators. New MDM technologies referred to as MDM data hubs have been developed. Even though the data hubs may look like their predecessors Operational Data Stores (ODS), modern data hub technologies are SOA enabled and leverage a number of other modern technologies not commonly used by the old traditional ODS. As the problem is an information management problem, every information management vendor has a "solution". Application-centric vendors (which started the MDM trend) also see this as a major opportunity to expand their integration and application scope. Organisations with MDM issues are doing a variant of the same approach: they face a variety of challenges in the information management space and this provides them with a collective way to frame the problem. This situation is similar to that which arose with compliance initiatives a few years ago.
- Compliance initiatives: These add corporate pressure. Driven by the War on Terror and corporate scandals in the US, compliance initiatives have put additional pressures on the enterprises. Without a sound MDM solution enterprises are facing increasingly difficult problems to support evolving regulatory requirements.
What are the Challenges presented by MDM?
- Organisations typically have complex data quality issues with master data, especially with customer and address data from legacy systems
- There is often a high degree of overlap in master data, e.g. large organisations storing customer data across many systems in the enterprise
- Organisations typically lack a Data Mastering Model which defines primary masters, secondary masters and slaves of master data and therefore makes integration of master data complex
- It is often difficult to come to a common agreement on domain values that are stored across a number of systems, especially product data
- Poor information governance (stewardship, ownership, policies) around master data leads to complexity across the organisation
Research from other experts in the field also leads to other more practical concerns:
- Finding skilled practitioners/implementation partners to help deliver projects
- Technology selection, MDM is an emerging market, lots of vendor movement, difficult to select one outright "winner"
- Creating a compelling business case and getting senior sponsorship is always tough
- Deciding where to start, prioritisation and focus are often a challenge
- Educating the business on why MDM is so important
Source: http://www.dataqualitypro.com/data-quality-home/a-beginners-guide-to-mdm-master-data-management.html