Here are my top 12 reasons to invest in data quality management: 12. Data are a business asset. 11. Business Intelligence reports are interpreted with much more confidence. 10. Data quality problems may harm your corporate image. 9. Good corporate management requires trustworthy data. 8. Data quality problems cause unnecessary rework during business process execution.
Calculating the business case for data quality management is a complex task. However, it is not impossible to do so. Classifications of traditional quality management, e.g. from Feigenbaum, are an excellent means to identify the costs of data quality. Referring to the „Cost of Conformance“ and „Cost of Non-Conformance“ we can classify data quality costs
Die Messung von Datenqualitätskosten ist nicht ganz einfach, aber auch nicht unmöglich. Analog zu den „Cost of Conformance“ und „Cost of Non-Conformance“ von Feigenbaum kann man Datenqualitätskosten in
Unfortunately, many companies still make the mistake to define Data Governance as responsibility of IT-Departments. There are two major reasons why this is a big mistake:
Leider machen viele Unternehmen den Fehler, Data Governance als IT-Aufgabe und nicht als Aufgabe des Fachbereiches anzusehen. Dort ist es aber aus zwei wesentlichen Gründen vollkommen falsch angesiedelt:
Everybody is talking about Big Data. Although there is no common definition of the term, most people relate Big Data to processing tremendous amounts of data. With this in mind, people often intuitively assume that high performance analytics is the biggest problem of Big Data. The big software vendors utilize this intuition to sell technologies