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Customer Data Integration: Creating One True View of the Customer

In recent years, businesses have frequently ignored - or at least paid only cursory attention to - one of the most fundamental keys to success: their relationship with their customers. Besides being able to extend or cancel their relationship with a vendor, consumers had little influence on how businesses responded to their needs. At the same time, businesses could derive little reward by distinguishing themselves through customer relationships and superior service. In effect, businesses dictated the relationships with their customers, and customers often accepted that standard.

In today's competitive environment, the nature of customer relationships has changed. Consumers have many choices to meet their needs, and aggressive advertising or access to the Internet increasingly broadens a consumer's horizons for competing products. Companies are competing for the same customer, and successful businesses must provide a superior relationship with customers to stand out.

In fact, the rise of customer relationship management (CRM) systems and methodologies that exploded in the late 1990s was merely a desire to return to "traditional" customer relationships. Like the mom-and-pop shops before them, successful corporations win and keep customers and prospects by establishing direct, sustainable and manageable relationships.

The Need for a Single, Unified View of the Customer

Before you can establish meaningful relationships, however, companies must be able to answer - with precision and confidence - one easy question. Who are my customers? As organizations increasingly standardized on different data collection methods - CRM, enterprise resource planning (ERP), data warehouses, etc. - customer data often was replicated in different systems. And each business unit or division may have their own systems. This viral spread in applications led to a confused and untenable view of the customer.

To maintain, manage and track these critically important relationships and the associated customer activity, corporations are investing valuable time and resources into managing customer data with customer data integration (CDI) systems. CDI is a combination of technologies and processes that manage the integration held within customer information systems so that interactions can be managed for the mutual benefit of both the customer and the business.

As any successful company knows, the ultimate success of a relationship between a business and a customer is determined by the quality of the interaction. Successful CDI results in:

  • Significantly enhanced customer service by understanding what the customer needs
  • Increased customer satisfaction by providing timely, informed options
  • Higher customer retention as consumers view the company not as a vendor but more as a trusted provider of goods or services
  • Lower cost of acquiring customers by using aggregated data sources to refine sales and marketing messages
  • Better understanding of your customers, leading to better decisions in product offerings, enhancements and packaging
  • Reduction in duplicate critical customer information, leading to improved marketing campaign results and sound forecasting practices
  • Improved business intelligence reporting by providing more accurate data to reporting applications, leading to more timely, accurate reports to decision-makers

Accomplishing a CDI project, however, is not just about simply integrating data into one final source. Data integration projects frequently fail, and a primary reason for that failure is a lack of data quality within the data integration technology.  Without being able to intelligently represent customers from different data sources - and match and link existing customers to create a cumulative record from disparate data points - any data integration project is doomed for failure.

The Role of Data Quality within Integration Projects

The disconnect between CDI and data quality has typically flowed from the lack of understanding about data quality within the enterprise. Rarely do technology professionals engage in random discussions about data quality; it is just not an exciting topic at the forefront of business technology agendas. Nonetheless, companies do not identify data quality problems until there is a direct, negative impact, stemming from their poor data management practices.

Before the advent of new, easy-to-use technologies, data quality was difficult and expensive to address. The issue of data quality straddled the line between an IT problem (since IT "owned" the systems housing the data) and a business problem (since business users felt the aftereffects of bad data). To make things worse, quantifying return on investment when implementing a data quality solution has been very difficult since few companies understand the impact of bad data on finances, employee efficiency or even morale.

The data quality challenge posed by CDI is no different. The CDI process requires different data quality steps and rules for different data sources. However, the basic process is consistent. Just as every puzzle begins with the first piece and builds from there, the CDI puzzle begins the same way.

Taking a Quality-First Approach to CDI

The first piece of the CDI puzzle is to take stock of your systems. Only then can you begin to uncover the integration and quality issues within these systems. Understanding the problem is a large part of the solution. Data profiling technology (a component of most data quality systems) provides analysis and data discovery techniques that allow you to investigate your current customer systems. Begin with a simple discovery:

  • What points of data collection might have customer information?
  • How is customer information kept, validated, and audited in each of these data sources?
  • What sources contain the best customer data?
  • How can we integrate data across the data sources?
  • What information about my customer do we need? Where can we get it?

After discovery, it is time to roll up your sleeves and find out what you are up against. With data quality and data integration technology, you can determine:

  • How do we know if our data is bad?
  • What, specifically, are the costs of customer data imperfections?
  • How can we provide consistent and uniform standards?
  • Are our systems producing data that is within business parameters?
  • Can we consolidate information across different business units?
  • What kind of consolidation do we need?

Armed with this information, a CDI solution can determine what business and integration rules are required to bring the best data from the various sources together. The customer data hub stores high-quality information from the various systems and stores linking information to the data sources. The CDI system makes the most accurate and recent customer information available to any enterprise-wide system. As dictated by the enterprise, the best customer information can be qualified and then synchronized with operational customer data systems.

CDI is about having correct and complete information about your customers in one place. Like Aristotle's metaphysics, CDI is about knowing everything about your customers. With this information, you can understand a total view of your customer base - and have a valuable relationship with each customer.


Daniel Teachey is the director of corporate communications at DataFlux, a wholly owned subsidiary of SAS, that enables companies to analyze, improve and control their data through an integrated technology platform. Teachey can be reached at daniel.teachey@dataflux.com.

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