Kimball University: Eight Recommendations for International Data Quality
Language, culture, and country-by-country compliance and privacy requirements are just a few of the tough data quality problems global organizations must solve. Start by addressing data accuracy at the source and adopting an MDM strategy, then follow these six other best-practice approaches.
One might think that at least with simple numbers, nothing could go wrong. But in India and other parts of central Asia the number "12,12,12,123" is perfectly legitimate and corresponds to "121,212,123" in the United States. Also, in many European and South American countries, the role of the period and the comma for designating the decimal point is reversed from the United States. You better get that one right!
Architectures for International Data Quality
Here, in condensed form, are my recommendations for addressing international data quality:
1. 90 percent of data quality issues can be addressed at the source, and only 10 percent further downstream. Addressing data quality at the source requires an enterprise data quality culture, executive support, financial investment in tools and training, and business process re-engineering.
2. The master data management (MDM) movement is hugely beneficial for establishing data quality. Build MDM capabilities for all your major entities including customers, employees, suppliers, and locations. Make sure that MDM creates the members of these entities upon demand, rather than cleaning up the entities downstream. Use MDM to establish master data structures for all your important entities. Make sure the deployment lets you correctly parse these entities at all stages of the DW/BI pipeline, carrying the detailed parsing all the way to the BI tools.
The Agile ArchiveWhen it comes to managing data, donít look at backup and archiving systems as burdens and cost centers. A well-designed archive can enhance data protection and restores, ease search and e-discovery efforts, and save money by intelligently moving data from expensive primary storage systems.
2014 Analytics, BI, and Information Management SurveyITís tried for years to simplify data analytics and business intelligence efforts. Have visual analysis tools and Hadoop and NoSQL databases helped? Respondents to our 2014 InformationWeek Analytics, Business Intelligence, and Information Management Survey have a mixed outlook.
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