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Tag: analytics

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Analytics is a Function, Not a Job Title

  • I recently read an HBR article that reinforced much of what we’ve been seeing internally at Management Concepts for the last two years: Analytics is a function, not a job title, and regardless of job title, analytics should be a part of every team’s profile. As noted by the HBR authors, you don’t have to be a Data Scientist to work in analytics. Success with analytics requires a “big tent” approach: Everybody in, and everybody all in.

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Adaptability: The Underestimated Skill

  • We all know the definition of insanity – doing the exact same thing over and over again, expecting different results each time.  But what happens when you’re trained to always run the same report, or write the same code, and something fundamentally changes?  Do you try to force-feed the existing process into the new system?  Or do you thoughtfully and methodically work within the new environment to solve the problem?  Adaptability isn’t always a skill associated with analysts, but I’d argue that, for everyone’s sanity, it’s one we all need.

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Data, Data Everywhere…Albatross or Opportunity?

  • Samuel Taylor Coleridge’s Rime of the Ancient Mariner tells the experience of a sailor returning from a long voyage. In the early parts of the poem, the mariner describes how an albatross leads the crew out an ice jam in the Antarctic, but the mariner then kills the bird. The crew vacillates between viewing the killing of the albatross as a good or a bad thing. However, they soon become surrounded by “water, water, everywhere, nor any drop to drink.” Eventually, the mariner encounters death, but survives after accepting his guilt – however as a penance for shooting the bird, the sailor must wander the earth telling his story to passersby.

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What Is Data Science and Why Do We Need More Data Scientists?

  • If you take a quick look at Glassdoor.com and their “50 Best Jobs in America” for 2017, you will notice that Data Scientist ranks at #1, among several other “data/analytics” titles in the list. Although this is evidently a highly desirable job, there appears to be a large talent gap in the field. Recent research by McKinsey Global Institute has projected that by next year, the US could face a shortage of 140,000 to 190,000 professionals with deep analytical skills, i.e., advanced statistical training and knowledge of machine learning. What exactly is data science? While not an entirely new concept, the terms “data science” and “data scientist” have exploded in popularity in the past 5-10 years. At its core, data science is about using scientific methods along with new technology systems to gather insights from massive amounts of heterogeneous data.

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Same Data, Different Conclusions: It’s a Good Thing

  • In just a couple weeks, I will be on my way to Italy for a two-week vacation! Che bella! When I was deciding where to go with my husband, I experienced a bit of work-related decision-making déjà vu. Every option we considered was very different from the next, but all were possibilities chosen based on the same exact set of data (number of vacation days available, budget, etc.).

    I felt like I was back at the office working with a team, arguing about what decision to make, even though we’re usually reviewing the same data. The difficulty can be that different does not necessarily mean wrong in these situations.

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Defining the Problem – How Savvy Data Pros Get It Right

  • Ever hear the phrase, “The first step is the hardest”?

    While it may be a common phrase for motivational posters, it also applies to the first step in making data-driven decisions: defining the problem you’re trying to solve.

    Without a clearly or accurately defined problem, time and other resources will be wasted, and you’ll be left unable to make an effective decision. Often, nailing down exactly what you’re trying to solve is more difficult than the analysis itself.

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