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Poor Data Governance: Why Customer Data Integration (CDI) Projects Fail Part III
(Continued from Page 1)
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Corporate dashboards are becoming the “must have” business intelligence technology for executives and business users across corporate America. Dashboard solutions have been around for over a decade, but have recently seen a resurgence in popularity due to the advance of enabling business intelligence and integration technologies. This paper discusses how to create an effective operational dashboard and some of the associated design best practices.
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Today, many CDI solutions overlook the real-time configurable rules needed to trigger alerts about the exceptions that are created during data flow. Often managing such exceptions requires full user interfaces for complex data stewardship tasks such as investigating the history and lineage of certain master records in question. This may be the only way to ensure that the user acceptance and data hub reliability remains high. In other circumstances, an enterprise may choose to build a specific user interface against the programming interfaces of the master hub in order to suit its needs. In either case, an adaptive solution must deliver rules-based configurability with best-in-class data stewardship consoles as well as programming interfaces to handle all data exception and reliability needs.
Data Governance Regime: Central, Distributed or Both?
Most CDI solutions are designed to create a single silo, an operational hub to serve a handful of applications, as opposed to one that can scale to the needs of the enterprise. While a focused deployment may be the desired option for central data governance, it usually does not address all the business needs for governance and compliance across an enterprise. Often, certain data attributes (such as privacy preferences) need central control and exception handling whereas other attributes are best left under local management. In addition, security and access to data attributes in the hub will vary by individual roles within each organization and by organization at large. In fact, to support the broad range of business requirements across business lines, there may be multiple data governance regimes required for different data attributes, all within a single enterprise.
An adaptive approach must be based on a distributed architecture whereby multiple hubs can be deployed to integrate different data sources and support different processes, yet be able to share data across one another based on any number of hub-and-spoke or peer-to-peer governance regimes. This offers a line of business yet another dimension of flexibility to share some but not all data – each based on its own data reliability and governance policies. With full rules-based configurability and data stewardship interfaces, a broad range of data governance regimes can be supported.
Data Governance Requirements: Coded or Configurable?
As the pace of change in business continues to increase, so does the complexity of maintaining data quality and reliability across the enterprise. In master data hubs that are custom-built or based on fixed models, it is very difficult to implement changes in either business logic or process because the rules reflecting the business conditions and requirements are usually custom-coded or tightly bound to the underlying fixed data model. In order to automate the consolidation of customer data in an intelligent and ongoing manner and maintain the highest level of data quality and reliability in the face of business changes, it is critical for an enterprise to be able to manage the repeatable business rules for its data cleanse, match and merge processes effectively.
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