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30 Jun 2008

dn:Director for Customer Data Migration

Optimize the Data, Minimize Your Customer Data Migration Risk

When migrating customer data to a new or existing application, it’s hard to see beyond the technical issues at hand - moving the data and preserving basic data integrity.

But the true success of a data migration project can only be measured against business goals. This means that data must not just simply ‘fit’ in a technical sense, it must also be fit for all its new purposes.

In order to ensure it’s fit for purpose , source data must be fully understood, validated, improved and re-validated before migration, using transparent rules that check it against the business needs of the data in the new system. Why? It’s all about reducing risk. Failure to understand your data will result in budget and time overruns when the migration is based on assumption rather than fact.

The only way to avoid data issues from derailing your migration project is to gain a full understanding of the data to be migrated, and to create the right set of validation and transformation rules for each unit of work. Logical or theoretical mapping rules are never sufficient for real customer data.

The Datanomic Data Quality Approach

dn:Director helps reduce the risk of customer data migration projects failing or overrunning due to core data quality issues.

Specifically, dn:Director allows you to:

  • Understand the content of your source customer data
  • Minimize the data being migrated - only move what you need
  • Work out how and when to migrate records based on business needs
  • Consistently validate data using business rules before and after transformation
  • Protect new systems from bad data - no more ‘Load and Explode’
  • Eliminate hard-to-find errors caused by bespoke code
  • Establish rules to control the effectiveness of the target system on an ongoing basis

dn:Director is best used in tandem with an initial Datanomic Customer Information Quality Assessment (C-IQA) to gain an understanding of your source data set(s).

The results of the C-IQA are used to set the functionality goals of your data migration in terms of data selection, validation, matching, transformation and mapping rules. Users can then call upon starter templates for common complex semantic analysis and transformation requirements, such as:

  • Resolving name and address data to a new structure
  • Standardizing and verifying address data
  • Validating and transforming email addresses
  • Standardizing and validating telephone numbers
  • Deduplicating addresses, individual and company names
  • Maintaining complete and accurate contact histories

Any data analysis and transformation rules you require can be quickly and easily configured in dn:Director. This ensures that your migration logic is transparent, and demonstrable because it has beendeveloped using a complete understanding of the actual data.

Find out more about the market’s leading Data Quality solution today…