By Elliot King
If you can't measure a process, you can't improve upon it. That's one of the ironclad quality initiatives. Edward Deming's revolutionary insight was that if you can measure a process or outcome continually, you have created the opportunity to improve it continually.
But measurement is only a starting point to quality improvement. Those measurements have to be assessed according to specific standards and then their impact on business operations has to be assessed. Only at that point can managers determine whether investing in remediation is appropriate or cost effective.
In many data quality improvement programs, data scorecards are the tools of choice for data managers to place raw statistics into meaningful contexts. From that point, different constituencies can identify problem areas and determine what actions should be taken.
To understand the idea of a data scorecard, a baseball analogy actually works here. Broken down into its granular bits, a baseball game consists of strikes and balls, hits and outs.
But in most contexts (although not all), to know that there were 210 pitches, 13 hits and 8 runs scored has little value. That data has be to combined and grouped correctly to have meaning. And different stakeholders--the players, the manager, the front office, the opposing teams--may want that data in different formats.
For data scorecards, the process of measuring and aggregating raw statistics into useful combinations is called a hierarchical rollup. It could look like this. At the most granular level, data is measured against the metadata associated with the database. Are fields the right length? Is there missing data? These statistics are of interest to database managers.
The next step is to assess whether the data meets data quality standards such as accuracy and completeness. This is a concern to data quality managers. Does the data conform to business rules? Business analysts need to know that. And what is the impact of data quality on process outcomes? As that point, management can determine what actions are prudent.
In baseball, a glance at a scorecard reveals the outcome of the game and who played poorly as well as offering the chance to drill down deeper to the underlying raw data if need be. A data scorecard plays the same role in data quality improvement.
If you can't measure a process, you can't improve upon it. That's one of the ironclad quality initiatives. Edward Deming's revolutionary insight was that if you can measure a process or outcome continually, you have created the opportunity to improve it continually.
If you can't measure a process, you can't improve upon it. That's one of the ironclad quality initiatives. Edward Deming's revolutionary insight was that if you can measure a process or outcome continually, you have created the opportunity to improve it continually.
But measurement is only a starting point to quality improvement. Those measurements have to be assessed according to specific standards and then their impact on business operations has to be assessed. Only at that point can managers determine whether investing in remediation is appropriate or cost effective.
In many data quality improvement programs, data scorecards are the tools of choice for data managers to place raw statistics into meaningful contexts. From that point, different constituencies can identify problem areas and determine what actions should be taken.
To understand the idea of a data scorecard, a baseball analogy actually works here. Broken down into its granular bits, a baseball game consists of strikes and balls, hits and outs.
But in most contexts (although not all), to know that there were 210 pitches, 13 hits and 8 runs scored has little value. That data has be to combined and grouped correctly to have meaning. And different stakeholders--the players, the manager, the front office, the opposing teams--may want that data in different formats.
For data scorecards, the process of measuring and aggregating raw statistics into useful combinations is called a hierarchical rollup. It could look like this. At the most granular level, data is measured against the metadata associated with the database. Are fields the right length? Is there missing data? These statistics are of interest to database managers.
The next step is to assess whether the data meets data quality standards such as accuracy and completeness. This is a concern to data quality managers. Does the data conform to business rules? Business analysts need to know that. And what is the impact of data quality on process outcomes? As that point, management can determine what actions are prudent.
In baseball, a glance at a scorecard reveals the outcome of the game and who played poorly as well as offering the chance to drill down deeper to the underlying raw data if need be. A data scorecard plays the same role in data quality improvement.
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