Building your data academy: Assessing candidate skills
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Analytics is a rapidly evolving field of high business value, so your organization needs a robust up-skilling strategy. I call the type of institutionalized data education platform organizations need a “data academy.” Enrollment in the academy is a good time to take stock of candidate skills: after all, the nature of the education depends on the current skill levels of its students. So, what are some ways to assess the data skills of your candidates in relation to what your organization needs? Take the time to assess your candidate data skills and motivations and you’ll be surprised at the talent you’ve got in house. The data academy will cultivate that talent.
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Building your data academy: Assessing candidate skills
- 1. Building your data academy: Assessing candidate skills
- 3. Self-rating • “Rate your Excel skills on a scale of 1 to 5.” • Easy to collect • Fun to analyze • Hard to design in a way that tells much!
- 4. Time estimates • “Approximately how many hours a week do you spend cleaning and preparing data for analysis and reporting?” • Time is money! • Do your analysts even have time to analyze? • Good baseline for ROI
- 5. Skill assessments • “Which tab on the home ribbon allows you to create a PivotTable?” A. Data B. Insert C. View D. Home • More specific than a 1-5 self-rating • Stripped of context, these knowledge checks can become meaningless
- 6. Skill assessments • A promising alternative assessment: • “Which of these best describe how easily you could perform a left outer join in Power Query? A. I wouldn’t know where to start B. I could struggle through with trial and error and lots of web searching C. I could do it quickly with little to no use of external help”
- 8. Experiences • “What systems do you pull your data from and to what extent do they interface?” • What are the pain points? • What do your candidates know and not know about how to fix them?
- 9. Emotions • “What excites you with working about data? What frustrates you?” • What are the pain points? • What do your candidates know and not know about how to fix them?
- 10. Goals • “What excites you with working about data? What frustrates you?” • Don’t discount intrinsic motivation! • What specifically do your candidates want to learn more about?