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In data processing, customer data integration (CDI) combines the technology, processes and services needed to set up and maintain an accurate, timely, complete and comprehensive representation of a customer across multiple channels, business-lines, and enterprises — typically from multiple sources of associated data in multiple application systems and databases. It applies data-integration techniques in this specific area.


Techniques for identifying customers

Customer data has many attributes. Common attributes include customer name, address, phone number and perhaps a social-security number (or equivalent). But given two sets of data that differ slightly, perhaps with a nickname ("Barb" instead of "Barbara") or with a cell-phone number, how can a computer distinguish between different aspects of the same customer and two distinct individuals?

In the larger sense, customer data can become very complex; for example, it may require anywhere from six to twelve or more fields to represent just a personal name. For example, some typical fields associated with a customer "name" may include:

  • Name prefix (Mr., Mrs., Dr., Captain)
  • Given name (a.k.a. "first name" or "Christian name" in some cultures)
  • Family name
  • Middle name(s)
  • Name Suffix (Jr., Sr., II, III)
  • Initials
  • Nickname
  • Maiden name
  • Married name
  • Professional title
  • Academic title

Address entries have their own complexities (for example: primary address number, pre-directional (N, S, E, W,) street name, street suffix, post-directional, secondary identifier (building, suite, apt,) secondary number, city, state, ZIP, and ZIP+4(R))

Add in phone numbers, social security numbers, email addresses, tracking (customer number, account number), relationship, risk-level, purchase history, service history, demographic, socio-economic, lifestyle, consumer-behavior segmentation and privacy preferences, just to name a few.

This information varies constantly, whether due to changes, entry-errors, corrections, or fraud. And many medium-to-large organizations store this sort of data in several different places: different departments, different locations, in different formats, etc.

Techniques for managing complexity

Attributes and their values can become extremely complex and dynamic due to the many changes individuals go through. Multiply all these fields by the millions of records a business or organization may have in its data sources, then factor in how quickly and how often this information changes. The results can intimidate. The Data Warehousing Institute (TDWI) says: “The problem with data is that its quality quickly degenerates over time. Experts say 2% of records in a customer file become obsolete in one month because customers die, divorce, marry and move.”[1]

To put this statistic into perspective, assume that a company or charity has 500,000 customers, donors or prospects in its databases. Cumulatively, if 2% of these records become obsolete in one month, 10,000 records go stale per month; or 120,000 records every year. Within two years about half of all the records may become obsolete if left unchecked.

Peppers and Rogers call the problem, "an ocean of data". Jill Dyche and Evan Levy, gurus in this field, have boiled the challenges down to five primary categories:

  1. completeness – organizations lack all the data required to make sound business or organizational decisions
  2. latency – it takes too long to make the data valuable: by the time of use, too much has become obsolete or outdated (slowed by operational systems or extraction methods)
  3. accuracy
  4. management – data integration, governance, stewardship, operations and distribution all combine to make-or-break data-value
  5. ownership – the more disparate the owners of the data-source owners, the more silos of data exist, and the more difficult it becomes to solve problems

History of customer data integration

Customer data existed and required integration even prior to the 21st century.

In the late 1990s Acxiom, a company based in central Arkansas, worked with GartnerGroup analysts in the field of data management to crystallize the concepts of and coin the term "customer data integration" (CDI). Acxiom (founded in 1969) had worked in the field of data management and integration for over 20 years and had developed several best practices from the experience. GartnerGroup wanted to standardize the concepts and share the best practices.

The process of CDI, as Acxiom and Gartner described it, should calm the ocean of data. It includes:

  1. cleansing, updating, completing contact-data
  2. consolidating the appropriate records, purging duplicates and linking records from disparate sources to enable customer or donor recognition at any touch-point
  3. enriching internal and transactional data with external knowledge and segmentation
  4. ensuring compliance with contact suppression to protect the individual and the organization

As of 2009, service-providers deliver CDI as:

  • a hosted solution in batch volumes
  • on demand using a software as a service (SaaS) model
  • on-site as licensed software in companies and organizations with the resources to drive their own data integration processing

The results appear promising. CDI enables the full integration of online and offline marketing to reach perfect prospects at the perfect time, please them and still protect their privacy. CDI enables companies to optimize merchandizing (assortment, promotion, pricing and rotation) based on demographics, lifestyle and life-stage, to ensure inventory turn and to reduce waste. CDI aids companies and organizations to choose the best location for new branch offices or outlets.

CDI commonly supports both customer relationship management and master data management, and enables access from these enterprise applications to information confidently describing everything known about a customer, donor, or prospect, including all attributes and cross references, along with the critical definition and identification necessary to uniquely differentiate one customer from another and their individual needs.

See also


  1. ^ Eckerson, Wayne W. (2002). "Data Quality and the Bottom Line: Achieving Business Success through a Commitment to High Quality Data" (Portable Document Format). TDWI Report Series. The Data Warehousing Institute. p. 3.  

External links




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