Information Quality in the Era of Big Data 4 The business world is flooded with data. Companies collect vast amounts of data about products and projects, opportunities, process outputs, product and service quality, sales and receivables, customers and their usage patterns, vendors, competitors, etc. They source data from third parties to link with internal data to make fundamental business decisions. And, across the organization people are gathering publicly available data. All companies must deal with the expanding variety, velocity and volume of data. There are different types of data in different formats with different storage requirements. We have moved from batches of data to streaming data. And, some have estimated that the volume of data in 2020 will be more than 40 times greater than the volume in 2011. The question is whether this increase in variety, velocity and volume will actually drive better analysis and decisions. There is an important distinction between raw (or source) data and decision-ready information. Raw data is uncertain. How reliable is the source? How can you confirm accuracy relative to an external point of reference? When was the data collected? Is it the most recent data? Will it be updated if it changes with time? Is the data complete or are there other attributes that could affect your interpretation of the data elements? Does the source use consistent methods/tools to collect the data? All these questions and more reflect the uncertainty inherent in raw data. Raw data simply cannot be used for making significant business decisions. No company wants to make investment decisions, strategic choices and competitive assessments with questionable data. Businesses need analysis and insight based on reliable information. Information is created by reducing the uncertainty inherent in raw data. Information, but not raw data, can be used to create the analysis and insight required for significant business decisions.
IHS Global Energy Information Review
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