tag:blogger.com,1999:blog-6895175514429514812.post2624880194598042286..comments2023-11-22T03:50:16.890-05:00Comments on Data Governance Insider: Top Ten Root Causes of Data Quality Problems: Part OneSteve Sarsfieldhttp://www.blogger.com/profile/12892788380306110697noreply@blogger.comBlogger4125tag:blogger.com,1999:blog-6895175514429514812.post-14322847564823682872014-07-14T21:17:54.326-04:002014-07-14T21:17:54.326-04:00Thank you for posting this article on the causes o...Thank you for posting this article on the causes of data quality problems. The information are really helpful. Cheers!Clarissa Lucashttp://www.process-box.com/noreply@blogger.comtag:blogger.com,1999:blog-6895175514429514812.post-14233675239745301732014-02-14T00:07:33.933-05:002014-02-14T00:07:33.933-05:00A very informative read on the causes of data qual...A very informative read on the causes of data quality. Thank you for sharing these helpful insights. Ramon Andrewshttp://www.cloudstaff.com/noreply@blogger.comtag:blogger.com,1999:blog-6895175514429514812.post-81575267232795799802011-08-25T09:38:56.143-04:002011-08-25T09:38:56.143-04:00In other words, preventing data from getting '...In other words, preventing data from getting 'bad' in the first place is preferred. I agree. We'll cover some of the areas that tools can't really help much in part two and beyond.<br />This series is mostly about changing processes and the hearts and minds of people, not tools.Steve Sarsfieldhttps://www.blogger.com/profile/12892788380306110697noreply@blogger.comtag:blogger.com,1999:blog-6895175514429514812.post-63859546605136045662011-08-25T08:36:50.179-04:002011-08-25T08:36:50.179-04:00The foundations of data quality are weak. They wer...The foundations of data quality are weak. They were derived from the manufacturing quality arena without sufficient consideration as to the distinctive nature of data. Data has no physical properties, is temporal and data quality measures are subjective. Next, estimates of the impacts of data quality were imagined and exaggerated. From statements such as data quality results in $600B a year in additional costs to claims of “death by data”. And finally, the deployment of data quality did not consider the long term implications and impacts or benefits. It was assumed data quality was the right thing to do. <br /><br />Current data quality practices are remedial in nature. Once deployed, a data quality program results in diminishing returns. Once the low hanging fruit problems are resolved, there is little more to do. The root causes of egregious data quality problems can be found in the policies of the organization. These policies are typically sacrosanct and changing them is unlikely unless a crisis occurs and even then, the changes maybe so institutionalized they will remain. There is one concept from manufacturing quality that has been ignored in data quality and that is that quality has to be designed in. For data quality to be effective, the design of applications, databases and business processes all have to be subject to quality control. These are the machinery that produces the data and if they are flawed, the result is bad data.<br /><br />Current data quality practices are like polishing a rusty car. It shines but continues to deteriorate with time. There are few preventative practices in data quality and the impacts to the business are limited.<br /><br />Those who recognized this suggest developing business cases, ROI and now data governance programs which take data quality to another level of abstraction and obfuscation.<br /><br />Data quality remains a practice of fixing data. Simplification of the problem is not a solution. Quality is a systemic problem. If an organization is interested in addressing quality it has to be addressed at the systemic level. <br /><br />Data quality is but a minor factor and is a distraction from the genuine “root” causes.Richhttps://www.blogger.com/profile/05769150989773542975noreply@blogger.com