End the Conflict: Resolve Customer Data Inconsistency - InformationWeek

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Software // Information Management

End the Conflict: Resolve Customer Data Inconsistency

The conflict must end: To provide strategically important customer sales, marketing, and service, your organization must get past the turf battles and establish solid, timely, and high-quality customer data.

If your company is one of the many that have collectively invested billions of dollars in customer relationship management systems, you know that it's still difficult for a large enterprise with CRM to get a consistent view of any given customer across the entire enterprise. Without an accurate picture of your customers, however, you are losing opportunities to pursue additional revenue and increase customer profitability.

Customer data in large enterprises is usually located in multiple applications: Front-office systems such as sales force automation systems, order management, and call center management systems; back-office systems such as ERP; and homegrown operational and shipping systems. Therefore, some of the customer data ends up duplicated in multiple applications.

Each application has a proprietary data model, and therefore may represent the same customer in different ways — with dissimilar customer codes and diverse attributes. For example, the ZIP code in one application may be represented in a 5+4 numeric data format; while in another application it may be represented in 9 characters to accommodate foreign postal codes.

To address such diversity, IT organizations write large amounts of manual code to integrate the data from multiple apps, including semantics rules and validation techniques, to ensure that duplicated customer data and dissimilar customer attributes remain consistent across these applications. For example, there may be semantics rules to ensure that before the customer reference records are updated in the target application, the birth date of a customer is never later than the death date or the customer address is in the right format. Many of these rules are deeply embedded inside applications.

However, as applications are upgraded or new applications are added into the ecosystem, these embedded rules sometimes miss being upgraded or fail to trigger. As a result, changes to customer data in one application either do not propagate or propagate erratically to other applications. Consequently, critical information about the customer, duplicated across these IT systems, begins to fall out of sync over time and becomes inconsistent and inaccurate. The unfortunate outcome is that the business loses opportunities to pursue new revenue and increase profitability when plans are based on inconsistent data or lack of a common view of the customers.

Some companies have tried to resolve this inconsistency by treating a certain application as the source of truth for certain types of customer data. For example, a mobile phone company may create a business rule designating the call center application as the source of truth for the customer home phone number.

However, these rules are difficult to deploy in a world where customers have multiple channels of interaction with the company. For instance, if a customer walks into the mobile phone company's retail outlet to sign up for a promotional offer and happens to provide her new home telephone number, the business rule I just mentioned will prevent the propagation of the correct phone number to the company's other applications.

Other companies have attacked the problem by standardizing on a single business application across the entire enterprise. However, this approach is not very practical: Most business applications make it difficult to model every customer-related process across all product lines within a large company.

Two new approaches are emerging that solve the problem of customer data inconsistency across applications. I'll now describe them and their successes to date.

One Approach: Centralized Semantics Store

The first approach, that of the centralized semantics store, comes from this assumption: If the data rules that are applied during application integration always keep pace with any upgrades or changes to the applications, then the customer data across those applications will always stay consistent. Therefore, the centralized semantics store technique focuses on centralizing all the rules (validation, transformation, aggregation, and business rules) in one common repository and making them available to various applications. As applications are upgraded, the centralization of these rules makes it easier for IT organizations to ensure that these rules are constantly updated to support the changes to the applications environment. As a result, the customer data within the enterprise can be kept consistent and accurate across various applications.

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