USED TO PUBLISH ARTICLES / VERBOSE CONTENT RELATIVE TO DATA QUALITY, DATA CONDITINONING, ETC.
The business case for improving Data Quality is blessed by a two-sided benefit model: downside avoidance & upside realization. Data Quality professionals have a rule which they call the “Rule of 10”: “It costs 10 times as much to complete a unit of work when the input data are defective as it does when they are perfect”.
Data Quality, a two-sided Benefit Model Read More »
Data Quality is an abstract concept until we are confronted with bad data or information which is not usable, not credible, not presented correctly, not accurate, etc.. Then Data Quality becomes a very concrete experience. While, there are many definitions for Data Quality, all of them converge to the same focal point. “fitness for use” by people, applications and business processes.
Defining Data Quality in the Process Industries Read More »
In process and manufacturing, information systems should be implemented with a system of checks and balances to maximize the quality of data and the economic potential of data. Creating an environment where data can be trusted is not an afterthought. Data quality, similar to product quality, needs to be built into our business and data management activities so that we can trust data for action at all times.
Trusting Data for Action Read More »
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