Analyzing Analytics

What is Big Data Analytics?

Big Data - Term that describes the large volume of data – both structured and unstructured – that inundates a business on a day-to-day basis. But it’s not the amount of data that’s important. It’s what organizations do with the data that matters. Big data can be analyzed for insights that lead to better decisions and strategic business moves.

Three Components of Big Data:

  • Volume - Enormous size of data sets. Specifically, this component of big data has to do with the amount of information or data that is collected.
  • Variety- Information collected is now not just numeric but includes information such as text, video, and pictures posts, Facebook likes, Twitter re-tweets, and LinkedIn endorsements.
  • Velocity- Information is continually being collected, and continuous collection has important implications for both the technology needed to store the data and the quantitative methods used to make sense of the data

Analytics - A collection of computer automated algorithms and methods that follow a set of parameters defined and monitored by the user that are used to interpret, describe, and identify patterns within and between structured and unstructured data.

Four Types of Analytics: 

                                                         
  • Descriptive - The goal of this type of analytics is to understand what happened in the past (Cech et al., 2015). This is the simplest form of analytics and is the most commonly used by organizations,
  • Diagnostic - Look at patterns between variables to determine the strength of the relationship between various concepts.
  • Predictive - Builds upon the ability of diagnostic analytics to identify causes, and attempts to use that information to predict future outcomes.
  • Prescriptive - Combines business rules with the results from the other three analytic types to compare the outcomes of various situations being encountered and proactively recommends or takes a course of action

 

Guilfoyle, S., Bergman, S. M., Hartwell, C., & Powers, J. (2016). Mo' Data, Mo' Problems? The potential of big data and analytics in employment decisions. In. R.N. Landers & G.B. Schmidt (Eds.), Using Social Media in Employee Selection: Theory, Practice, and Future Research. New York: Springer.