#SLA2017: Big Data, Big Role for Info Pros


Sponsored by Information Today, Inc.

Big data—is it still “a thing”? If career surveys are an accurate measure, it is. Glassdoor ranked “data scientist” number one on its list of the “25 Best Jobs in America for 2016.” “Librarian” didn’t make the list, but it can be argued that librarians and information professionals have been doing the rudimentary work of data scientists for years. We are experts at finding relevant data; determining its integrity, value, and relevance to research; and organizing it in an accessible way. Our application of value-added analysis to data to create critical knowledge products help to answer key questions and shape and inform the mission of organizations. The challenge lies in demonstrating to the powers that be that info pros and data scientists have overlapping skill sets. How do we do that? This session will explore new roles for info pros in big data. Attendees will leave with actionable steps on how to transfer existing skills to work in big data and to acquire new competencies required for the future.



  • Amy Affelt

    Director of Database Research Worldwide, Compass Lexecon



Author of The Accidental Data Scientist.

Big Data: you know it when you see it.

Gartner’s  Five V Characteristics of Big Data

  • Volume
  • Velocity
  • Variety
  • Verification
  • Value

Why did you sample my data?

Formula vs Algorithm (“Where formula stops being a formula”)

Consumer Reports Digital Consumer-Protection Standard

IoT Considerations:

  • safety and reliability of devices
  • security ramifications
  • preservation of data
  • privacy of issues
  • manufacturer obligation to export data?

Your device can and will be used against you.

Problematic Data: 2016 US Presidential Election

Who’s Afraid of the Big Bad Data?

  • Same day voter registration
  • Caller ID

Clean Data Determination Checklist:

  • Pull the last 100 data-points as a sample
  • High light 10-15 attributes
  • Analyze each record
  • document errors
  • calculate accuracy percentage of total perfect vs. error