Originally published on Hub Designs Magazine March 2012
Like data integration processes, MDM solutions can be seen at times as 'plumbing', but in both cases the work to be done has strategic, business-critical purpose. Master Data is meant to be at the heart of most business transactions, applications and decisions that occur in an enterprise. In today's volatile business world, it is important to focus MDM on what matters most for the goals of the business to ensure reliable, unified and timely data for key systems and processes. Such data must conform to how information is used by the business. So while it is tempting for enterprises to think of MDM as a technical or IT initiative, MDM is far more about business purpose and business problems to be solved.
To solve the problem of ensuring high quality 'cleansed' and unified master data, interest in MDM practices and solutions is increasing. But in many ways MDM implementations are still immature and the solution space is still evolving. William McKnight notes that often MDM solutions are focused on "simple" issues, rather than on larger, more strategic value, but that he is "starting to see some pioneer MDM implementations explore the full range of benefits". Among the benefits are data governance practices that elevate data to strategic positioning in the enterprise in support of: operational and productivity improvements, reliability of BI and analytics results, timely customer insights, and ultimately, better decision-making.
Understand Business Processes to Understand Data Priorities
First it is imperative to understand how the business operates: how the business uses key data and processes, how processes interact with data, and which business processes matter to strategic focal points. From an understanding of business processes, the best approach or approaches can be determined for implementing a master data solution that not only creates trustworthy, consistent master data but is responsive to changes in the business and to new data that continues to be collected / generated by business processes.
Data quality plays such an important part in the MDM solution. Analysis must be done to determine overall quality, where the weak links are, and the impacts on business processes and operations from inadequate data quality. But data quality is a subjective measurement – part of the process and data assessment will be the setting of requirements for acceptable data quality for individual data elements to provide the level of quality needed by the selected business processes. Some thought should also be given to future needs, whenever possible. The potential "trap" is that data that is "good enough" for one business process may be inadequate for another, particularly when considering future direction.
Data derives its real value from its usability in significant business processes, including those that drive and benefit from analytics, intelligence and decision-making processes. And value attaches to data when it positively impacts business outcomes. Unified, quality data attained through MDM solutions must also be integrated into business processes, which is not an easy task. Such integration is not only at the technology and systems levels, but involves the synchronization of people, practices and 'super' processes across the enterprise to reflect how work is accomplished.
All data management solutions are now challenged by the proliferation of data, content and information in sites that are not owned by the enterprise, such as cloud and mobile services, social sites and partner repositories. It has become clear that such data can be valuable to enterprises, particularly for insights into customers, future trends and markets, and directions for innovation. MDM solutions are also challenged to now pull in and harmonize some very unwieldy data with existing master data repositories.
In particular, integrating customer master data with customer-related content in social media will likely lead MDM solutions into new territory. The difficulty of content from social media is capturing the data 'clues' to be passed to analytics for accurately determining sentiment and context. So-called 'big data' needs to be aligned with reliable enterprise master data to properly correlate social content in decision-making processes. How the MDM solution maintains an overall master customer 'profile' will include continuous integration processes for targeted social media sites.
Enterprises continue to experience massive increases in data assets. Beyond social content, enterprises encounter data explosions from business activities such as constant M&A, outsourcing initiatives, and highly diversified partner and supply chain channels. Industries such as Telecom have enormous stores of machine-generated data related to customer activities as well as operational performance and 'product' pricing. GPS data relates more and more to customer and market insights for certain industries.
Achieving consistent master data with such disparate, sometimes unwieldy and complex, data sources will continue to be difficult, but will likely lead MDM practices and solutions to new levels of value to the enterprise and vital business process. Again the focus will have to be on the key business processes that need these new data sources integrated with existing master data.
About the author: Julie Hunt is a software industry solution strategist and analyst, providing market and competitive insights. Her 25+ years as a software professional range from the very technical side to customer-centric work in solutions consulting, sales and marketing. Julie shares her takes on the software industry via her blog Highly Competitive and on Twitter: @juliebhunt For more information: Julie Hunt Consulting – Strategic Product & Market Intelligence Services