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On Small Data, Part 2: Thinking Across Data
Information Today - Medford
Subjects: NON BIBLIOGRAPHIC DATABASES, DATA BANKS
Author: Howley, Brendan
Date: Jun 2017
Start Page: 12
Pages: 2
Section: THE RAZOR'S EDGE
Abstract (Document Summary)
[...]as we heard last month from the "father of machine learning," Michael Jordan (a University of CaliforniaBerkeley professor), the levels of quality of inference from one set of data input to the next are by no means related to the volume of input data. Big Data is growing more deterministic, and because the volumes of data points have become so large, the hits-for instance, the valuable correlates of "intending to vacation," "loves Nicaraguan coffee," and "worried about climate change" add up to an ad for ecotourism in Nicaragua's coffee highlands-don't have to be perfect, just close. If the heartbeat of your story is there, if it's relevant, authentic, and presented in a way that's commensurate with library brand equity-i.e., trust in your library to speak truth about me and my needs-then you've got something powerful. With Open Media Desk, we're teaching library marketing teams across Ontario how to a) tell better library stories to activate their communities better and b) capture the why of their successes by deriving valid inferences from the social media data their storytelling leavens. [...]participants love the weekly, 2-hour open, collaborative Zoom videoconference setting, where there's a premium on shared insights, war stories that help others, and combining media strategy and design together. A 2009 report by Boston's Aberdeen Group (acuity business.com/hubfs/Documents/ AberdeenReport_5874-RA-dash board-business-intelligence.pdf) shows that best-in-class companies win a 24% average increase in profit simply by monitoring their key performance metrics. Why should library information technologists and CEOs care about what their marketers are turning...
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