How Can We Build a Foundation for Successful Customer Data Integration?
The core problem is poor lifecycle management of customer reference data.
Posted Mar 15, 2004
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To stay competitive enterprises have invested in building multiple touch points to let their customers reach them. Most companies now have multiple customer Web sites, service call centers, targeted email campaigns, and sales teams armed with detailed customer contact information. However, these multichannel touch points have created a major customer data integration challenge at most companies. Now information on 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 more than 30 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. As companies rush to implement customer data integration initiatives, they overlook 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. However, all is not lost. An unquestionable master reference data source remains a key challenge, but 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, which is 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 metadata and the creation of business rules that determine the validity of customer reference information in a 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. What is needed is a centralized repository that consolidates all the customer reference data, along with the cross-references to source systems, into a master reference store. This store then becomes the best source of truth of customer profile information for all operational and analytical applications. More important, any solution that builds such a repository needs to have the ability to manage customer reference data through out its life cycle. Building a reliable foundation of master reference data: lifecycle approach The four life-cycle stages of customer master reference data require these critical capabilities: Consolidate: An ability to create or easily import a customer reference data model, map it to numerous data sources, and then cleanse, match, and merge source records based on sophisticated rules, without having to write any custom code. Manage: An ability to manage the data administration and data content management tasks separately. For instance, IT team manages rules, system configuration, and other administrative tasks; a data steward who understands data content handles the data merge exceptions based on business and data context. Share: An ability to provide views from the customer reference data model to all applications that consume the reference data and automatically communicate any changes in the master reference data to all affected applications, as needed. Extend: A scaleable solution with a low cost of ownership for enterprises requires the ability to efficiently extend the solution to other data sources or eliminate existing data sources (as applications retire or relevant capabilities are phased out) without writing code. Walk before you run With this foundation of reliable master reference data, the companies can identify customers across systems correctly and combine their transaction data accurately for use by customer-facing employees. By restoring trustworthiness to customer reference data across all applications, enterprises can maximize the return on their CRM investment and be on path to realize the vision of 360 degree view of every customer. Companies need to first learn to walk before they run when it comes to implementing successful customer data integration efforts.
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