Devising the Strategy, Making the Plan

Melissa Team | Address Correction, Address Quality, Address Verification, Analyzing Data, Analyzing Data Quality, Data Cleansing, Data Management, Data Quality | , , , ,

By David Loshin

The three steps that I suggested in my last post about where to begin with data quality are truly meant to help determine where to begin, but also to guide the development of a longer term strategy and plan. Let me recall the three steps, but this time put them into the long-term perspective:
  1. Solicit data quality requirements from the business users.
… Read More

Assembling a Data Quality Management Framework

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By David Loshin

There are two dominating questions that I am asked over and over again when people are in process to create a program for data quality management.

The first is “how can you develop a business justification for a data quality program?” and the second is “how do we get started?” We are currently working on a task with one customer who seems to be ready to commit to instituting a data quality management program, yet they remain somewhat resistant because of the absence of an answer to the first question and confused about planning because of the absence of an answer to the second.… Read More

Data Quality Problems are Predictable

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By Elliot King

The idea that poor data quality is costly and hurts performance is about as old as science itself. The seminal science writer Stephen Jay Gould wrote a whole book about how faulty data leads to faulty conclusions, often to the great detriment of society. And one of lasting aphorism in computing has been “garbage in, garbage out.”

Moreover, the problems and risks of poor data quality have been studied,
described and quantified for decades.… Read More

Assessment is the Critical First Step

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By Elliot King

Edward Deming taught us long ago about the virtuous cycle of continual quality improvement–plan for change; execute the change; study the results and then take action to improve the process. But Deming’s PDSA (plan, do, study, act) cycle is a generic approach. The cycle has to be modified and customized to address targeted areas for quality improvement.

The key steps in the virtuous cycle for data quality improvement are
assessment, measurement, integration, improvement and management.… Read More