With the increasing competitive world that we live and work within, the number of business merger and acquisitions continue to grow as companies strive to advance themselves in the marketplace in combination with achievement of their overall objectives. Historically, there were many situations in the not so distant past where there was less focus upon integrating and consolidating existing business and customer management systems and data. However, with the increased pressures from governmental requirements associated with Sarbanes-Oxley, Basel II, Patriot Act, and other legislation in combination with the desire to support effective business intelligence based improvements the need to consolidate existing systems and data to a single target platform has become much more critical.
Today there is an increased reliance on technology and the movement of information which in turn dictates that the underlying support systems be reliable, accurate, and becoming more efficient in order to maximize the value of the customer relationship while taking advantage of each up-sell and cross-sell opportunity. With this increased reliance, a comprehensive data quality and data integration strategy utilizing a foundation of "best practices" is integral to the overall success of meeting goals associated with business process improvements.
The process of establishing or confirming data definitions for all key corporate data assets is an important first step in order to create a map of how existing systems in each organization are utilizing the various data items as they flow throughout the enterprise. These data definitions include, but are not limited to, documentation regarding data elements and database architecture for customer, prospective customer, and systems applications extending to examining informational data flows within the enterprise itself. The time invested in the data definition process will inevitably save valuable time, identify data quality issues early in the process, and create overall cost reductions in the implementation phase for data and system integrations related to merger/acquisition initiatives.
As a component of the data and system flow architecture definitions, the overall quality of the historical data along with the current data quality process flow should be examined. Businesses consistently struggle with data-driven initiatives predominantly caused by a failure to address the inaccuracies, redundancies, and data anomalies that exist in the data warehouses that serve as the foundation for an increase in reliance upon business intelligence initiatives. Further, data quality standards must support an evolving range of quality issues that are balanced in design, implementation, and include an ongoing audit of data management processes and procedures.
Even with proper data quality assessment of the data assets represented by each company, a successful merger of customer and business management systems also requires a commitment to identify ongoing improvements which includes not only technology but extends to people and processes both in and outside the existing organizations. This commitment serves to maximize return on the acquisition investment and increase shareholder value while enabling effective business intelligence systems that are supported by consistent, reliable, and trustworthy data.
Making data quality an ongoing business practice as opposed to a "one-time" project in conjunction with a merger/acquisition data migration, ensures that not only is the initial integration successful but sets the stage for these "best practices" being adopted as an continuing priority. Inevitably, mergers and acquisitions will continue to occur as boundaries that previously existed are removed in what has become an ever-growing global e-commerce based society. Solid data quality practices in turn creates a solid foundation which in turn facilitates the greatest opportunity for success.
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