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  • Data Science, Unicorns, and Some Things That Never Change

    This post is a follow-up to Mind the Gap where I wrote about Δ, the difference between our perception of ourselves and other’s perception of us. In Mind the Gap I focused on personal and community ramifications of minimizing your Δ. In this post I write about applying these same concepts to data science, analytics, and IT. Before we proceed, ...
    Posted to Andy Leonard (Weblog) by andyleonard on May 5, 2017
  • Data Wrangling Concepts: Latency and Staleness

    Latency is the amount of time between data creation and load. Staleness is the amount of time since data in a target was refreshed. In many data integration use cases, a latency or staleness of one day, one week, or even one month is acceptable. Enterprise Data & Analytics helps enterprise data integration teams reduce latency and staleness. ...
    Posted to Andy Leonard (Weblog) by andyleonard on April 13, 2017
  • Why Automate?

    Because, as Jen Underwood (jenunderwood.com | LinkedIn | @idigdata) states in an upcoming podcast: The future of data science is automation. If automation is the future, how do we decide what to automate? We look for the long pole. What’s the long pole in data science? Data Integration is the Long Pole According to Lars Nielsen in his book ...
    Posted to Andy Leonard (Weblog) by andyleonard on March 12, 2017
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