Last month, we talked about the life cycle to transform data into action: data, information, insight, strategy and action. This month we focus on the information aspect. Disparate systems are built out of necessity, time constraints and the continual move to packaged applications. The ability to extract, integrate, clean, consolidate, household and audit your organization's information is critical and forms the foundation for your successful customer intelligence (CI) initiative. As mentioned in my August column, ETL has been the major challenge for organizations building their business intelligence capabilities since the advent of data warehousing. However, new age data warehouse professionals turn ETL, data integration and data quality challenges into a business enabler, instead of an impediment.
ETL, and the integration of your information, should not be a constraint or bottleneck to your CI initiative. This is a mind-set and philosophical change. With the maturity of ETL technology and methodology from market leaders such as Informatica and Ascential, organizations have substantially decreased the time to market for their CI initiatives. New channels, new applications and new customer preferences can be integrated into customer information repositories faster than ever. Data integration issues are no longer the anchor weighing down the scope of your project.
ETL is a means to an end. That end result is the insight derived from your CI initiative and ultimately the strategies and actions driven by insight. Historically, CI initiatives are most likely to achieve their ROI when built backwards from the business objectives, analytical requirements and decision-making processes. Customer data warehouses that are built by source system or prioritized by type of data do not meet business objectives. For instance, calculation of the ROI of marketing programs requires information from several different source systems. Those daunted by the data integration task may look at ways to simplify the scope. These days, we are not advising organizations to reprioritize requirements due to ETL complexity.
Maintainable and restartable processes are essential for the long-term viability of the data warehouse. The excitement of data warehousing is that the future is not known and the business issues that need to be solved are unknown. The data warehouse must be able to morph in order to meet those challenges. Many data warehouses are constricted by their ETL processes, creating a rigid structure that cannot meet future challenges.
Proactive, effective monitoring, evaluation and correction of data quality issues will provide users with the confidence in your CI systems that increases adoption, use and ROI. Automated auditing, quality control and quality reporting, combined with the organizational processes (stewardship, ownership, incentives, etc.) needed to consistently maintain the level of data quality, will drive advanced insight, excitement in the initiative and confidence that someone is watching the quality of information on a daily basis.
Wells Fargo's goal is to increase the size of the relationship with their customers, measured by the number of services and products a customer has enlisted. To do this they need to combine information from an incredible number of systems (channels, bank systems, mortgage, teller systems, etc.) in order to produce the most critical metrics that support their strategy. Wells Fargo has reaped the financial benefits of their analytical systems by putting their strategy at the forefront of their priorities, regardless of the ETL complexity.
Retail continues to have difficulty integrating their channels. You can sign up for a wedding or baby registry online, but your friends and family cannot shop at the stores for the gifts. You can buy products online, but cannot return them at the store. In these cases, tremendous amounts of data exist, but due to a lack of integration, there is no true information to drive new customer relationship strategies.
If you are looking for a single view of the customer or the corporation, or simply trying to create marketing effectiveness reports, data integration will sooner or later be deemed a significant issue. However, confident developers will steamroll these issues as they focus on the end requirements and their business value, not the technical data integration task.
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