Part IV –Good Data Quality drives better Identity Management

Most organizations spend many months defining requirements and determining which Identity Management (IDM) solution will help them improve their business, while few pay attention to the data that are critical to running their IDM systems. If your organization is going to invest large sums of money and time building an IDM solution, why is data quality so often an afterthought? The end result of implementing IDM with poor data quality is delivering bad results, faster!

Instead, take a step back and create a Data Quality program as part of your IDM strategy and overall governance structure. Establishing a Data Quality program requires carefully designed processes that borrow key elements of change management, including support from senior leadership in the organization.

Key goals of a Data Quality Program:

  • Put a stake in the ground and define an enterprise vision for a quality-centric culture
  • Define a systemic approach to improving data collection processes and validation techniques
  • Make a long-term commitment to the process; data quality and management principles will evolve over time
  • Validate Data when it is Collected – During data collection, if data is not protected with validation functions, then bad data will be captured, created, and propagated to all connected systems in your organization
  • Executive Sponsorship – Have leadership pave the way for any difficult organizational process changes to reduce barriers to change

We recommend evaluating and managing data quality as you develop solution requirements to support an easier implementation and avoid the challenges of cleaning up user data after your Go Live.

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