Using mentorships to help avert an analytics talent crisis
Three of the top five positions in Glassdoor's annual "50 Best Jobs in America" survey involve working with data analytics. And for the second consecutive year, "Data Scientist" ranks number one on the list.
Why do data analytics jobs rank so high?
Three factors evident from the Glassdoor study:
· Plenty of positions available - and not just technical -- as drivers such as big data propel us toward an information economy.
· Relatively high pay, as median base salaries exceed $100K in the top three data-related categories – i.e., Data Scientist, Data Engineer and Analytics Manager.
· High job satisfaction, as average satisfaction rating for survey respondents holding analytics-intensive jobs exceed "4" on a 5-point scale
Yet, in the face of these attractive attributes, the U.S. may soon confront an analytics talent crisis. As discussed in my past Information Management columns, management firm McKinsey predicts a 50 percent to 60 percent gap between the supply and demand of analytics talent as early as next year.
Signs are this situation will worsen. Per a new study by Forrester Research, “As Tech Management Structures Change, Roles Become More Strategic and Externally Focused,” the future workforce will comprise fewer IT workers and more data analysts. But, according to the report’s author, Forrester’s Mark Cecere, many companies believe they lack sufficient skills on staff in the areas of analytics and data management.
So, if McKinsey doesn’t expect data science talent to come from outside organizations, and Forrester doesn’t see talent developing inside existing enterprises, then how will we cultivate analytics talent for tomorrow’s business needs? By working to attract more teens to data science careers today.
Mentorship during the formative years of middle and high school is one key to success in this mission, per IT Futures Labs research.
Here are three reasons why:
Role models are highly persuasive
Their ability to inspire and influence future career choices cannot be overstated. So, if we want more kids to grow up to be data scientists, during their middle-school and high-school years tweenagers and teenagers should meet more data scientists and learn what data scientists do. Furthermore, a mentor’s influence most likely will multiply when parents gain an understanding of analytics career paths, too. Students listen to their parents two to one over any other adult in their lives, as we discovered in our “Teen Views on Tech Careers” study.)
Mentors model mentality more than method
What makes a good data scientist is more mindset than best practices. Traits such as thinking “strategy first,” showing a “passion for solving problems” and believing that technology is about “humans, not hardware” play out more in professional performance than technical manuals. By interacting with tweenagers and teenagers, analytics mentors can provide an early look at how a data scientist thinks and acts, enabling students to visualize a future beyond the next level of education.
Kids have bought into the “follow your passion” message
They want careers that allow them to do something they love. That’s why meaningful contact with professionals who love their work, too, is vital. Extending mentorship out of the workplace and down to middle- and high-school years is about inspiration, not training.
What method of mentorship works best for tweenagers and teens? There are several essential elements we incorporate into mentorship models we use with NextUp, our organization’s initiative to orient young people toward tech careers, that apply to the analytics fields:
Timing is key
It’s important to work with kids in middle school or early high school, the time in their lives when we classify them as "Dreamers," meaning they have yet to consider the practicalities of a career, money, or security. But during this period in their education, they already are working with data in a pragmatic way, applying information to support conclusions in multiple contexts. Not only are kids plugging series of data into equations in math classes, they are collecting data during experiments in science classes and expressing data in words for English and History essays. These activities are like the day-to-day work of an analytics manager.
Guest speakers only go so far
Don’t stand up and lecture to students. To make a connection with young people, work on a data science project together. The more sustained the connection, the more likely it is that growth will happen. Show kids how numbers in a database translate into infographics on websites. In addition to showing charts, tell stories with data. Help them visualize how the information captured in analytics influences day-to-day business decisions and operations.
Guide, don’t control
A student will never become passionate about something if they don’t have some amount of autonomy. Give them the tools they need to get started—then get out of their way. Let kids take over your laptop or tablet and run the apps that process the information. In short, let them put their hands on the data and turn it into analytics. You’ll be amazed at what they are able to do—and they will be, too. That just makes them hungry for more. That’s when mentors step in to guide to the next stage.
Mentors are a true catalyst for engaging with kids, validating their interest in working with data, expanding their views of themselves and the world of information around them – and making the career path to analytics seem not only attractive, but attainable.