A Stand-Alone Methodology for Data Exploration
In Support of Data Mining and Analytics
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With the emergence of Big Data, data high in volume, variety, and velocity, new analysis techniques need to be developed to effectively use the data that is being collected. Knowledge discovery from databases is a larger methodology encompassing a process for gathering knowledge from that data. Analytics pair the knowledge with decision making to improve overall outcomes. Organizations have conclusive evidence that analytics provide competitive advantages and improve overall performance. This paper proposes a stand-alone methodology for data exploration. Data exploration is one part of the dat...
With the emergence of Big Data, data high in volume, variety, and velocity, new analysis techniques need to be developed to effectively use the data that is being collected. Knowledge discovery from databases is a larger methodology encompassing a process for gathering knowledge from that data. Analytics pair the knowledge with decision making to improve overall outcomes. Organizations have conclusive evidence that analytics provide competitive advantages and improve overall performance. This paper proposes a stand-alone methodology for data exploration. Data exploration is one part of the data mining process, used in knowledge discovery from databases and analytics. The goal of the methodology is to reduce the amount of time to gain meaningful information about a previously unanalyzed data set using tabular summaries and visualizations. The reduced time will enable faster implementation of analytics in an organization. Two case studies using a prototype implementation are presented showing the benefits of the methodology.
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