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 HRB 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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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? […]
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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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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.
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When I was deciding where to go with my husband, I experienced a bit of work-related déjà vu. Every option I 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 were all reviewing the same data.
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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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