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Reliable BI data requires collaborative approach
This article is part of the BI Trends + Strategies issue of Issue 3 March 2012
Business competitiveness and agility are increasingly dependent on decisions that are informed and fueled by business intelligence (BI), reporting and analytics. For example, in an emerging “age of the algorithm,” operational applications and processes are often enhanced as a result of business analytics. Meanwhile, power-user analysts explore various business scenarios by combining multiple large data sets, in many cases containing both structured and unstructured information. David Loshin As this dependence on BI grows, it should not be a surprise that business analytics users must have an implicit trust in their decisionmaking processes, which implies a reliance on having trustworthy data available to them. Data quality is especially critical as the size of data volumes and the number of data sources grow, but what is meant by “high-quality data”? Data management professionals typically define data quality in terms of “fitness for use,” but that concept rapidly becomes obsolete as we consider the numerous ways that the same...
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Features in this issue
Big data and operational BI place new demands on information architectures. What worked in the past may not be the best choice for the advanced analytics that are poised to provide substantial business value.
What constitutes data quality when analyzing millions of daily transactions? Does trustworthy data mean “perfect” data? You might be surprised at the answers.
News in this issue
Various software vendors have begun offering connectors designed to help users bridge the gap between Hadoop clusters and relational databases.