Recently, many a large enterprise has embarked on a Customer Data Integration (CDI) initiative to gain unified views of its customers and their relationships across products, locations and business lines. With the demand for data integration hubs increasing in the past few years, several CDI vendors now vie for a leadership position in the emerging market. As these vendors offer an array of packaged solutions with rich features and services, it is easy to lose sight of the most fundamental requirement of a CDI hub – sound customer data management. Creating unified customer views across conflicting, disparate data sources is the raison de ętre for all CDI implementations; therefore, ensuring that the data in the hub is reliably consolidated, its exceptions properly stewarded and data policies properly implemented – in short, the data is well governed – has to be one of the most critical requirements of the solution. Yet, companies often evaluate these data governance capabilities inadequately in their vendor selection process only to regret their decision later in the implementation phase.
Therefore, it is critical for enterprises to review the architecture of a CDI solution closely to determine whether it meets all their data governance needs in the long run. So, as you embark on a solution, consider these three factors that underlie data governance capabilities: data reliability, data stewardship and governance regimes, and ensure that these are all part of a flexible architecture platform.
Data Reliability: Built-in or Bolted-on?
Most application-centric CDI solutions that focus primarily on the operational use of data often underestimate the harder challenges of building a reliable high-volume customer hub in the first place or of managing different data governance regimes across multiple lines of business. While external tools can be loosely integrated to such solutions to cleanse and match data, the more intractable problem is that of merging matched records to create the “best of breed” master record for each customer. Essentially, a CDI solution that promises a scalable, operational hub but has bolted-on data quality tools is going to incur ever increasing costs for data management over time.
In order to deliver a “golden” or master record for each customer and its various affiliations, a system must dynamically assess reliability across all data sources – based on user-defined parameters and intrinsic data properties – and ensure that only the most reliable content survives at the cell-level of the master record. For instance, if the call center begins collecting email addresses when confirming orders, this data attribute may be more reliable than the email addresses submitted by customers at the website. The ability to rapidly adjust the system to survive the call center email address over the website email address is a critical architectural component of any CDI system. Moreover, such cell-level survivorship for data reliability must be built into the core product architecture and should not be sacrificed as the customer hub scales to millions of customers. Ultimately, how well the end-users accept a customer data hub depends on sustaining high level of data reliability, even as the hub grows in volume or as new data sources are added.
Data Stewardship: Out of the Box or Custom?
A business cannot implement an operational customer hub in the absence of data stewardship ? as soon as data begins to flow through the supported business processes, exceptions and errors begin to flow as well. Therefore, any customer hub acting as a data integration platform must offer business capabilities to monitor and handle such exceptions either by business analysts and end-users or by anyone designated as a data steward.
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