Data quality

Data Management Best Practices - infographic

The following collection of quality control slogans are compiled and created by others that emphasize the importance of investing in quality control measures for overall business sustainability. 100% Quality. A Company Of Quality. A good

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Data cleansing includes various methods for removing and correcting data errors in databases or other information systems. For example, the errors may consist of incorrect (originally incorrect or outdated), redundant, inconsistent, or incorrectly formatted data. Key steps for data cleansing are duplicate detection (detecting and merging the same data sets) and data fusion (merging and …

In an era where data is king, and big data analytics and related tools are the king of kings, enterprise big data analytics is no longer a differentiator, but a password at the door for some industries. Enterprises in industries like banking, energy, transportation and others rely on big data to not just keep a

This is a 4th version of the Big Data Decision Tree (Mind Map), which reflects the last changes in Microsoft products. As usual, a disclaimer: The process of solution selection for Big Data project…

Hits: 5969Just preparing for the next BI project, I thought that maybe it would be useful for Power BI users

The Stanford maturity model considers six components subidivded by three dimensions, all across 5 maturity levels. Here's how you can use it.

Companies now realize that ongoing competitiveness depends on the ability to free critical business processes from the confines of individual applications and execute them smoothly and consistently across system boundaries. Master Data Management (MDM) helps companies achieve this challenging goal in a way that leverages the existing system environment and maximizes overall IT investment. Now there is a way to eliminate the data redundancies and inconsistencies that diminish business…