Slideshow 5 Steps to Data Quality

  • January 12 2012, 11:02am EST

1. Specify Value in the Eyes of the Customer

Using a Six Sigma DMAIC (define, measure, analyze, improve, control) approach and a focus on Six Sigma Lean tools, the plan began to take shape. As DMAIC specifies, before you do anything, define the problem. Begin with a voice of the customer. Understand what the customer needs. Remember that it's not about good data, but what the business can do with trusted data from across the corporation.

2. Identify the Process Chain

Develop a process to deliver continued improvement. First, establish confidence levels with the quality of data in your databases. Then, recruit a team consisting of members from across the enterprise to outline a data project scope, criteria, governance and data ownership.

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3. Make the Process Flow

Engage the business side to foment an enterprise-wide approach to data, knowing that this process can take some time to have everyone fully involved. Data stewards are key to streamlining this process and improving the data. Make sure stewards are in touch with your data governance council to keep consistency with your project.

4. Address Only What the Customer Values

Seek out tools and solutions that both keep data in a consistent format and address that value earlier identified by the customer. Then, document and obtain agreements that reflect your problem statement, quantifying risk factors and requirements to drive consensus.

5. Continually Remove Wasteful Data

For quality data across an enterprise, always go to the source. Data quality is not owned by IT but by the data consumer in the business. Set up weekly meetings to keep all the teams on track; this can prevent multiple reviews and training at the department level. A philosophy of getting it right the first time drives cost containment and points to the need for mechanized standards. Rework wastes the cost of employee time and the lost opportunity to work on other projects that add to growth.

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5 Steps to Data Quality

These steps stem from a project taken on by data management expert Nancy Northrup. Read the full article here. For more on data quality from Information Management, click here. All images were used with permission from ThinkStock.