What is good quality data?
Good quality data means that all master data is complete, consistent, accurate, time stamped and industry standards based.
What is the advantage of good quality data?
Good data quality will improve internal business processes for manufacturers, retailers, wholesalers, intermediaries and other third parties.
Will this add costs?
Instituting any data quality/accuracy program will involve additional resources, very much depending on the size of an organization and experience with data quality management.
But improving the quality of data from end-to-end will reduce costs, improve productivity and accelerate product speed to market. Poor data quality affects businesses negatively. It is hard to improve data quality if the relationship between data quality and business performance can be quantified. The challenge is to identify DQMs (data quality metrics) that are tightly coupled with business processes.
The following is an article written by David Loshin in Information Management Magazine, May 2005.
What makes a DQM?
More challenging, however, is that the individuals typically tasked with devising good DQMs are better trained at data analysis and less skilled in business performance monitoring. Therefore, part of this strategy is to understand the characteristics of a reasonable business performance metric and then explore how to map those characteristics to the measurable aspects of data quality. The following list of characteristics, which are by no means complete, should give some guidance as to how to jump-start the strategy:
o Clarity of definition
o Business relevance
o Drill-down capability
Clarity of definition
Because the metric is intended to convey a particular piece of information regarding an aspect of business performance in a summarized manner, it is critical that its underlying definition be stated in a way that clearly explains what is being measured. In fact, each metric should be subject to a rigorous "standardization" process in which the key stakeholders participate in its definition and agree to the definition's final wording. In addition, it is advisable to provide the metric's value range, as well as a qualitative segmentation of the value range that relates the metric's score to its performance assessment.
Any metric must be measurable and should be quantifiable within a discrete range. Note, however, that there are many things that can be measured that may not translate into useful metrics, and that implies the need for business relevance.
The metric is of no value if it cannot be related to some aspect of business operations or performance. Therefore, every desirable metric must be defined within a business context with an explanation of how the metric score correlates with a measurement of performance. More desirable is if performance measurement can be directly associated with a critical business impact; this is probably the most critical characteristic of a data quality metric.
Any measurable characteristic of information that is suitable as a metric should reflect some controllable aspect of the business. In other words, the assessment of an information quality metric's value within an undesirable range should trigger some action to improve the data being measured.
Without digressing into a discussion about the plethora of visual "widgets" that can be used to represent a metric's value, it is reasonable to note that one should associate a visual representation for each metric that logically presents the metric's value in a concise and meaningful way.
From a different point of view, each metric's definition should provide enough information that can be summarized as a line item in a comprehensive report. The difference between representation and reportability is that the representation will focus on the specific metric in isolation, while the reporting should show each metric's contribution to an aggregate assessment. In turn, this allows the manager to evaluate the priority of any issues needing resolution.
A major benefit of metrics is the ability to measure performance improvement over time. Tracking performance over time not only validates any improvement efforts, but once an information process is presumed to be stable, tracking provides insight into maintaining statistical control. In turn, these kinds of metrics can evolve from performance indicators into standard monitors, placed in the background to notify the right individuals when the data quality measurements suddenly indicate a deviation from expected control bounds.
In recognition of the summarization aspect of a representation of a data quality metric, the flip side is the ability to provide exposure to the underlying data that contributed to a particular metric score. The natural instinct, when reviewing data quality measurements, is to review the data instances that contributed to any low scores. The ability to drill down through the performance metric allows an analyst to get a better understanding of patterns (if any exist) that may have contributed to a low score, and consequently use that understanding for a more comprehensive root-cause analysis. This kind of insight allows your organization to isolate the processing stage at which any flaws are introduced and, in turn, enables you to eliminate the source of the introduction of data problems (instead of the typical, counterproductive reaction of correcting the data values themselves).
Developing key performance indicators for information quality is clearly a challenge, mostly because the hard numbers presented by data quality tools are typically out of the business context. Here we have provided some insight into how the data analyst can work with the business customer to identify ways that poor data quality impacts the achievement of business objectives and subsequently determine hard costs associated with each occurrence of a flaw. Once this has been done, providing a dashboard with tracking and drilldown capabilities establishes a value-added approach for value-directed information quality management and improvement.