Big data rituals for the end of the beginning

Graphic by Alex Zezza

A student arrives on campus as a freshman, an empty shell containing the sum of their life experiences, hopes, dreams and expectations. Their knowledge is unfinished and unformed. It will be refined by years of study, debate and exploration. Ideally, it is tested against competing ideas and measured for truth and usefulness. It will be verified by experience and constantly modified by new input.

This is analogous to the concept of big data, a phrase used to describe the relationship between humans and the large volumes of useful information generated by our phones, tablets, computers and other networked devices. Users generate data when they use search engines, shop online or employ navigation apps. Advertisers study the data generated by shoppers as they move from initial interest to sale. Government agencies and private businesses use data analysis tools to identify trends in the economy, from the impact of student debt to maps depicting wide-scale migrations of people, animals, infectious diseases or the spread of political ideas.

“There is this incredible amount of data. It’s searchable, it’s parsable,” said MSU Denver Department of Journalism and Technical Communication Chair, Shaun Schafer.

One of the more common data analysis methods is to create a spreadsheet and query the data to identify trends, patterns and connections. Cutting edge data analysis tools use rudimentary artificial intelligence to manage data in real time and can identify unusual activity in credit or bank accounts, protecting both the customer and company from fraud. Other uses of real time data analytics are high speed trading, network monitoring, retail sales and commuter traffic routing.

Large data sets are both a problem in search of solutions and a tool for uncovering solutions to existing problems. Experts in every field are carving out their own niche through careful analysis of the growing data pile. In a widely networked world where our tools, appliances and applications keep score, a baseline appreciation of our own data – how it’s generated and how it might be used by others – is now mandatory.

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