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Internet Librarian 2013 – Big Data: Fitting the Framework

Amy Affelt 

Let’s begin by taking the “big” out of big data. It’s just data.  We’ve always worked with data.  Our role as librarians isn’t to get big data, but to get bigger data.  We’re very good at finding valuable, credible information and that’s what we should be doing in the context of business problems.

The big data communication framework: Understand the business problem. Determine impact measurements.  Discover available data. Decide which data is most valuable (where did the data come from, which data can be merged). Formulate hypothesis(ses) (prove and disprove—could a change in conditions affect assumptions).  Communicate the business impact of the results.

Example: Hurricane Sandy.  The challenge – sea gate costs would be $50 billion vs. the aftermath costs of the storm.  Data to consider include sea gates costs and maintenance costs.  Storm surge aftermath costs include infrastructure rebuilding, lost revenue, insurance payouts, tourism loss, etc.  Sources of data—government websites, commercial building firms, chambers of commerce, building associations, etc.  Hypothesis—the data might show that sea gates are cheaper than disaster aftermath costs.  What do the results show or not show?

Big Data Analysis, Brought to You by Librarians.  Storytelling with data is a skill we bring as librarians.  The story of a storm: damage caused by surge not storm, list of damages, remedies and costs, alternative of sea gates and costs.

Amy then had us work on specific “big data” scenarios at our tables.  A good learning process!

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