New data management technologies and disciplines emerge to solve existing problems, address new demands, bridge gaps between existing technologies, or to drive innovation and change in the marketplace. Whether that is CRM attempting to better address and manage the notion of customer relationships, or CDI practices driving better integration of customer data, or Customer MDM initiatives bringing together more front-end and back-end synchronization of the customer master data, the overall concept and capability of data management becomes more rounded out as these technologies and disciplines bring more technique and data management practices into play. Regardless of the initial data domain or functional context, good technologies and practices usually find their way into more generalized usage over time.
For example, although CRM initially raised the focus and technique for distinguishing and managing customer relationships, this is not a practice limited to sales and opportunity management functions, nor just CRM technology. Identifying and managing customer relationships now also plays heavily into ERP, customer service, marketing, partner management, or a business intelligence model, and has become a broad underlying factor in Customer MDM in order to drive a customer 360° view. Similarly, CDI technology and practices have evolved from being more BI-centric to use in broader data migration efforts such as in transitioning from legacy applications to a new integrated platform or to assist in M&A-driven data integration efforts.
As MDM brings more technology and discipline to the table and as these mature and become more imbedded in the business model, we can expect that MDM's underlying techniques and disciplines such as data governance, data quality management, data stewardship, and data access management become further exploited beyond just the master data management context. These type disciplines can and already are being applied to other types of data.
To better understand where MDM is going and what it will influence, we need to look at the current drivers and consider what are likely to be the future drivers. Currently, MDM initiatives are focused primarily on improving data quality and operational efficiencies that, in turn, are expected to help yield better business intelligence and sales opportunity. Just how much yield can be expected is an ongoing debate and a constant sticking point when trying to project a hard ROI. The ability for MDM to improve operational efficiency is the more predictable part of the ROI equation because there are usually many tangible opportunities to improve data and the associated processes, which can immediately start improving operational efficiency and provide more accurate and consistent data to the BI processes. But how much this can translate to new sales opportunity is the intangible part because this is influenced by many other analytical, business, and market factors beyond MDM's scope.
We believe that MDM's ability to drive more operational efficiency through continued maturity of its core disciplines and with the influence of new technology will certainly continue and contribute to improvement in a company's bottom-line performance. But how much MDM can contribute to BI improvement and a company's top-line growth is still a very open question and an indirect proposition. As MDM practices continue to mature, this will go a long way toward improving the foundation and data integrity issues from which the analytical models and business intelligence processes have long suffered. But even as the underlying data gets better and becomes more trusted, the emphasis will be primarily on the shoulders of the BI teams to clearly demonstrate how better data transforms into more dynamic and real-time information, insight, and services that can drive strategic decisions and better business opportunity.
Therefore, we believe that more technology and business focus will shift toward understanding, improving, and optimizing the value chain between the operational and the analytical processes. As Figure 12.2 shows, this shift will expose where more improvement and synergy is needed across a value chain of data integration, MDM, BI, and business process areas to create a more integrated and optimized relationship between the operational and analytical functions.
Figure 12.2 Value Chain of Improvement
In general, there is still enormous opportunity to create more closed loop value streams between the operational and the analytical processes. As we discussed in Chapter 1, operational-based or analytical-based MDM approaches can each bear fruit in their own right, but eventually either approach is likely to reveal the need to address both operational and analytical data management under a common enterprise MDM model. Enterprise MDM focused solutions and the shift toward SOA-based technologies will greatly help solidify the foundation needed to create a more beneficial relationship and more end-to-end synchronization between the operational and analytical dynamics in order to help drive more top-line growth.