In the ongoing worldwide series of CDI-MDM summits, I have had the opportunity to review and collect customer data integration (CDI) and master data management (MDM) best practices based on discussions with more than 500 CDI-MDM project leads and members - primarily enterprise and data architects, CTOs and data stewards. This column provides a synopsis of the top 10 best practices applied by IT organizations during their CDI-MDM solution evaluations.

Executive-level sponsorship. Most CDI-MDM projects are brought about by critical business priorities - e.g., inability to cross-sell across lines of business, sullying of the brand due to poor service, inability to execute marketing and service levels based on total lifetime value of the customer, etc. Typically, a high-ranking business executive is essential to communicate the critical success factors and the ongoing metrics of success as the initiative rolls out.

Business partnerships. Without participation of the major business units in the requirements capture, CDI-MDM projects naturally take the IT "white elephant" path and become out of sync with the businesses' needs. Moreover, after the initial development of a CDI-MDM system, IT and business must continue to partner in development of business rules and resolution of master data match/merge issues. There must be an ongoing commitment to update both applications and business processes to leverage the core data stored in the new master data hub.

Data governance. Too many IT organizations are simply revamping their well-worn system development lifecycle or data warehouse methodologies. Data governance is vital to the success of projects, and the lack of such formal political processes can doom a project to failure before it gets off the ground. It is true that for complex systems implementations such as CDI-MDM, many IT professionals will turn to the tools and methods they are experienced in. But CDI-MDM requires much more stamina and discipline to successfully engage in "data politics."

Evolution. Very few enterprises can tolerate the risk associated with rip-and-replace conversions of legacy master file systems in one iteration. More palatable to the risk-averse is the organic growth scenario wherein a persisted data hub grows by proving its value as a trusted source to the various business units over time. Consequently, the business units elect to use the hub as their trusted source rather than suffering the expense of maintaining their own master customer data. Such evolutionary approaches also allow for a fair amount of flexibility as the various business units are weaned from data ownership to data stewardship.

Policy/process hub. Mega vendors are promoting the notion of business information registries and business policy hubs as the next generation of application architecture. It is clear that without attention to the notion of process hubs, many enterprises will simply be recreating the master files systems they spent the last five to 10 years creating as they rolled out their enterprise customer relationship management (CRM) and enterprise resource planning (ERP) projects.

Data quality. We all know the extremely high costs associated with poor customer data quality. CDI-MDM provides for instantiated data quality processes upstream at point of contact or capture that require attention and resources. What trust will users put in a system that does not provide the best sources of master customer data when that is the raison d'etre for building such master customer data systems? The loosely coupled implementation architectures of the latest generation of data quality tools further eliminate the status quo of batch-only cyclical data quality processes. There is no excuse these days for lack of inline data quality processes at the point of capture.

Communications. A formalized approach to communications infrastructure includes technology approaches such as shared reference master data and common processes such as data governance committees and councils. This will ensure the ongoing community consensus regarding data definitions, business rules for survivorship of the various trusted master data sources (in case of collisions) and the automated formalization of heuristics, etc. in reference to master data maintenance and access.

Metrics. Defining the basic metrics for application monitoring (stakeholdership and holding individuals accountable) is vital to any project. For CDI-MDM initiatives, metrics include KPIs from marketing, sales and service as well as those related to reduction of costs associated with the previous master customer data maintenance systems.

Managing the systems integration partners. Given that in the first year of deployment the typical Global 5000 enterprise will spend three to four times as much for systems integration (SI) services than for the CDI-MDM software, it is imperative that SI partners are chosen wisely and managed well.

Staffing. It is very common that the CDI-MDM development team will be composed of a mixture of newly trained developers with skills using relatively new software architectures and tools. A recent twist in the staffing market is the arrival of both rent-an-architect and rent-a-steward businesses (available via your friendly local SI or other consultancy). Make sure your team has the full attention of IT and HR management to avert the probability of the team being lured by your competitors (or other service firms). For example, provide bonuses tied to key project milestones but also provide nonmonetary corporate recognition through very visible company communication channels. Also make sure that there are both formal career tracks for these newly minted CDI-MDM experts as well as noncompete clauses with your consulting partners.

As both custom-built and off-the-shelf CDI-MDM solutions become the dominant means to package the concept of a single view of the customer/product/supplier/et al, then best practices will increasingly be captured and packaged by both SIs and software vendors as competitive differentiators (accelerators) for their respective solutions.

This column served only to touch on the very highest level of such best practices. Clearly, there are many more such accelerators that are industry- and solution-specific. These accelerators are the best way to kick-start a savvy, successful CDI-MDM program, which provides proof points that are collectively furthering the data governance profession.

Register for the  CDI-MDM Summit 2006 in New York City, October 15 to 17 at Hope to also see you at  CDI-MDM Summit Iberia in Madrid, November 9 and 10! 

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