Collectively, businesses spend millions of dollars annually in their attempts to implement a variety of customer relationship management, business intelligence, customer marketing, and other learning laboratories with the goal to improve customer retention thereby increasing sales and shareholder value. It is a known fact that sustaining existing customer relationships in order to maximize the life time value of those same customers can be accomplished at a cost far less than finding new customers to replace them. In their attempts to create additional options for customers to interact with the enterprise, multiple channels or touch-points have been created which may include service call centers, customer web sites, electronic mail marketing, and sales teams charged with overseeing customer interaction and satisfaction.
In the process of creating these multi-channel touch points for customer satisfaction, data integration challenges have also been increased. In-flows of information have been expanded with new information for a single customer arriving from multiple source systems, staff, and by means of electronic, verbal, and transactional based mechanisms. This information may also be strewn across multiple data stores, formats, and even physical locations making it difficult process to ensure duplicate work effort is not created or false assumptions are not being made based upon a lack of a single customer view. As business struggles with these issues and move to implement customer data integration strategies, the key to their overall success is directly dependent upon establishing an accurate, verified, and validated foundation for customer reference data and the associated transactional histories that accompany them.
Many businesses attempt to resolve issues of unreliable and inaccurate items of information through a process of extraction, cleansing, and database reload of cleansed information. While this approach may resolve a specific issue at a specific point in time, it does not address the core problem that was the original cause of the invalid, incorrect, or inaccurate information being captured and entering the enterprise to begin with. A pro-active approach to data quality at each point that data may enter the enterprise sets the stage for minimizing the need to batch cleanse data at later points in its lifecycle while at the same time maximizing the success of subsequent data integration initiatives.
With the passing of each day, more data enters the enterprise from more source systems. New business initiatives are begun which often translates into data that was once collected to support one particular purpose must now be used to support an entirely different application. Addressing the data quality issues at the source of data entering the enterprise improves the ability for businesses to adapt quickly to the changes in economic, market, or competitive induced situations. This pro-active approach sets the stage for the greatest opportunity of success not only with being able to address a particular need at a particular point in time but extending to identification of new business opportunities that might otherwise be hidden.
Data quality problems can enter the enterprise from many different areas. Some of these may include:
- Poor data entry or other data collection procedures
- Failure to perform proper data maintenance validity processes
- Errors generated by a lack of data quality controls when migrating data from one system to another
- Data coming from outside the enterprise that may not follow the same company standards
- Misapplied or a complete lack of business rules
Poor data quality directly impacts the success of any subsequent data integration activity potentially leading to complete process failure, increased marketing costs, detriments to staff performance, and hinders business management from making effective decisions to increase shareholder value. Data is one asset that at some levels is non-consumable. That is, it gets used over and over for a variety of tasks and a multitude of different initiatives. These differing tasks are often operating in parallel with one another. Based on these realities, the costs and negative impact associated with poor data quality will be compounded. The upside to this situation is that the investments made in addressing data quality issues at their source will maximize the return made on subsequent data integration initiatives with the same compounding effect.
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