The key to success is learning to walk before you run

However, these multi-channel touch points have created a major customer data integration challenge at most companies. Now, information of a single customer is strewn across scores of database silos in different lines of business or product divisions. In a large enterprise, for instance, it is not unusual to find data on a single customer, stored in over 30 different data sources. Worse, the same customer information is duplicated by each application, recreated differently for each business process, or stored repeatedly in various data warehouses. In the face of such data dispersion, duplication and conflict, an enterprise’s vision of maintaining a 1:1 relationship with every customer has remained unfulfilled.

As companies rush to implement customer data integration initiatives, they over-look a key component to its success: an accurate and reliable foundation of customer reference data. Customer reference data - data that uniquely identifies a customer across different applications and sources - is commonly duplicated and is often in conflict across disparate systems. Reference data conflicts are usually the root cause of pervasive data quality and reliability problems, leaving companies to backpedal on the very initiatives they hoped would drive value. But all is not lost. While an unquestionable master reference data source remains a key challenge, it is never too late to enter into the race.

Current approaches to building reliable data foundation force-fit technologies, such as data cleansing and ETL (extract-transform-load) tools that are not designed to address the core problem: poor lifecycle management of customer reference data. Data quality tools are a necessary step to standardize and cleanse dirty data but are inadequate in maintaining data reliability in a business context. For instance, a customer address may be cleansed and verified as a valid postal address, but still may be obsolete or inappropriate (e.g., the address might be the right shipping address but not the correct billing address). In contrast, data reliability requires the capture of business meta-data and the creation of business rules that determine the validity of customer reference information in business context. Further, building a data warehouse using data cleansing and ETL tools can be difficult because such data reliability rules have to be custom coded. The result is an inflexible solution that is laborious to build, hard to maintain and difficult to extend to new data sources over time.