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    April 2008 : THE Journal. As each district progresses, it will face new challenges discerning what data is relevant, addressing tolerance for change among users, and figuring out how to respond now that data is driving its decision-making.
    \nStage 1: Define the Outcomes
    \nStage 2: Define the Questions
    \nStage 3: Collect and Sort
    \nStage 4: Extract Meaning
    \nStage 5: Take Action
    \nStage 6: Evaluate Outcomes, Modify as Needed

Anne Bubnic

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    Here is a suggestion for the use of this article with colleagues. You will need a list of the 10 understandings and copies of the article written by Carol Ann Tomlinson.
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  • In this post I hope to share the essence of some of the main ideas communicated in the speech. The format is: words from the slide followed by a short narrative on the core message of the slide. Hope you find it useful.
  • The biggest challenge in our current environment is that it is trivial to implement a tool, it takes five minutes. But tools are limiting and can just give us data. What compounds the challenge is that we all have this deep tendency to make decisions that come from who we are influenced from our life experiences. Based on my humble experience of the last few years here are seven common sense recommendations for creating a data driven company culture……
  • # 6 Reporting is not Analysis
  • ...6 more annotations...
  • # 7 Go for the bottom-line (outcomes)
  • # 5 Depersonalize decision making
  • # 4 Proactive insights rather than reactive
  • # 3 Empower your analysts
  • # 2 Solve for the Trinity
  • # 1: Got Process?
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    The title of this presentation at the Washington DC Emetrics summit was: Creating a Data Driven Web Decision Making Culture - Lessons, Tips, Insights from a Practitioner. Although meant for corporations, the advice applies just as well to academic institutions. The goal here was to share tips and insights that might help companies move from just having lots and lots of data to creating cultures where decisions are made not on gut-feel, or the proverbial seat of the pants, but rather based on data.
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