How to Create Business Value Out of Master Data Management
Discussions around master data management implementations in organizations typically raise a common refrain: What business value will the MDM program provide to the business? To provide an appropriate response, we need to examine the key functions and processes that such a program would have a positive impact on. What are the opportunities that exist to enhance capabilities of the business? What are key problem areas or pain points that the MDM program would help address? The answers to these questions can help highlight the business value that the program will provide.
The sales and marketing functions revolving around the customer are critical to the business, and any enhancement to this capability can be of great value to the organization. Any MDM program will need to focus on providing greater insight into customer behavior, enhancing the customer experience, the ability to identify patterns around the customer, and opportunities to identify new customers and grow the customer base. Most importantly, the program needs to provide a consistent and reliable source of customer information across the enterprise.
Having identified “customer” as a central theme to the MDM program, let us take a closer look at the various processes and possibilities that exist within the space. Customer, in the majority of instances, is the most widely used concept across an organization. It crosses several functional lines across most organizations. As a result, the customer function takes a very high priority because quality and integrity of the information cannot be compromised. Any deviations from this principle will result in degradation in the customer experience. Inaccurate information will quickly lead to a loss of confidence in the information. This lack of confidence could eventually lead to an abandonment of the solution or result in increased costs in problem resolution. Either scenario results in a lack of confidence in the solution.
There are a couple of choices available at this point. Several products in the marketplace offer an out-of-the-box solution for managing customer master data. These products have evolved over the last few years and provide a reliable and quick means to implement a master data solution. These products are GUI-based and are easy to use with minimal (if any) coding involved. They certainly are the recommended means of implementing a solution.
The other route that some companies take is to develop a homegrown solution. Depending on the scope and size of such an initiative, the effort, resources and time involved vary.
While MDM has become a popular and current theme, the fundamental principles around which data governance was built remains very relevant in this context. Managing a concept like customer and the data around it is as critical as efficiently managing the processes and functions around this data. Both of these functions are intertwined in the implementation of a master data solution.
Thus, the core concepts of data governance apply to the MDM solution. A data steward or stewardship team that takes ownership of the data is responsible for important governance criteria such as the data’s quality and integrity, the processes that move data around the organization, the policies around the lifecycle of the data itself, the various service level agreements with the concerned parties, and the coordination and orchestration of data feeds to ensure its timeliness.
While packaged products are a viable means of quickly implementing a solution, it is worth noting that some products are more customizable than others. This can sometimes limit the optimal implementation of an MDM solution. Most products offer a set of entities related to the customer function, but there needs to be a certain amount of extensibility to the schema. Some products allow for this. Others require numerous configuration changes and limit the ease of use, which is important. These are reasons why some organizations build homegrown solutions to suit their needs.
Examining customer attributes leads to familiar concepts like name, address, segmentation, affiliation, type, etc. Each of these concepts needs to be accommodated for in the model. From this point, the management of the solution is no different from a regular data project. All of the same functions apply to MDM as well. Quality of the information, data load-related challenges, timing, real time versus near real time, high availability, historical data needs, change data capture are all familiar requirements that apply to an MDM solution.
The Role of Metadata
The master data solution can be enriched by providing metadata around the various functions and processes, and vendors today offer metadata products. When combined with the MDM solution, this provides enhanced capabilities in understanding source-to-target information around the master data. Linkages can be created across systems that tie together information from various systems from which the data has been sourced. Additionally, metadata that tracks data being provided to downstream systems can also be generated. This provides an end-to-end understanding of data flowing across the enterprise. Reference data management capabilities take this a step further. While MDM provides solutions around key business concepts such as customer, product, location and employee, reference data management provides solutions around data that is not mission critical across an enterprise. This might include a list of codes and descriptions for software used in an organization, a list of cost center codes and descriptions, etc.