Continuing Education, Training, and Professional Development

Continuing Education 101

The terms “Certificate”, “Industry Certification”, “Digital Badge”, and “Credential” are frequently used interchangeably. While there are some commonalities, there are some not-so-subtle differences that managers of analytical organizations should understand before sending their team off for training - either physically or virtually. The most significant commonality is that these continuing education offerings are part of a large and very profitable sector of the economy. According to the 2019 Training Industry annual report1, total U.S. company expenditures on training and education was $83 billion - which was a 5-3% decrease from 2018. On average, companies spent $1,286 per “in-house learner” (employees enrolled in company-provided training and education). These figures do not include tuition reimbursement for degrees pursued at universities. U.S. companies have offered university tuition reimbursement to their employees as a benefit for decades - up to the annual tax-deductible limit ($5,250 in 2020). However, tuition reimbursement is typically only applied to “for credit” courses that can be used towards a degree. And educational products like certificates may or may not be “for credit”. And are different from certifications. Which are different from badges. And mayor may not be offered by universities. We know. It’s complicated.

In 1989, Stephen Covey published The 7 Habits of Highly Successful PeoplePowerful Lessons in Personal Change1. The seventh “habit” is “Sharpen the Saw - Seek Continuous Improvement and Renewal Professionally and Personally”. This habit is regularly cited as the rationale for an entire industry around continuing education and the (justified) mantra that individuals should regularly update their skills and become “lifelong learners”. The New York Times3 referenced the concept of “The 60-Year Curriculum”, as an alternative way to think about learning; rather than consider higher education as four years of classroom learning, people should consider stretching their education over the six decades they are expected to work over their lifetime (this would certainly change college football).

Tlte approach of continuous and lifelong course enrollment contributes to ensuring relevant skills and exposure to “the latest thinking” in a particular industry (e.g., how our ability to capture and analyze new forms of data is impacting consumer lending decisions or how sensor-based data is improving customer service in big box retailers). It also facilitates more seamless career changes. In a survey completed by Northeastern University, 64% of employers agreed that the need for continuous lifelong learning will demand higher levels of education and more credentials — and a majority (61%) of hiring leaders view credentials earned online as equal to or better than those completed in person (although not equal to a university degree).

One example of a company collaborating with a university to encourage and facilitate lifelong learning is the rideshare platform Uber.

Uber allows drivers who have completed more than 3,000 rides and have high customer ratings to take free classes through Arizona State University's (ASU's) online programs.

They ask drivers to fill out financial aid forms and apply for federal grants, and ASU will provide scholarships. Uber covers the remaining costs. Drivers - who are considered contractors rather than employees - are responsible for taxes on the benefit. The program extends to drivers’ family members, such as spouses and siblings. Starbucks has had a similar partnership with ASU since 2014, through which about 2,000 employees have received degrees.

Analytics and Data Science – Revisited

As discussed in the previous chapters, analytics and data science are fairly nascent academic disciplines; chances are if you graduated from college before about 2015, you had very little formal exposure to the concepts, much less the programming languages, which facilitate the translation of data into information. As we discussed in Chapter 1, the definition of what data even is has changed significantly just over the last decade. Less than a decade ago, few people would have considered text or images (unstructured data) to even be “data”, much less developed the skills necessary to effectively translate it into information.

So where does all of this leave existing employees who have strong domain knowledge, highly relevant industry experience, and are valuable organizational contributors, but their skills and even their approach to thinking about data are completely outdated?

Rather than pursue a strategy of hiring (exclusively) new data science talent, managers of analytical organizations should consider opportunities related to "upskilling" existing employees. In the words of songwriter Stephen Stills, "If you can't be with the one you love, honey love the one you're with".

Illustrations have been created especially for this book by Charles Larson.

There is almost an infinite number of training options for working professionals. A search for “Analytics Training” generated over a billion hits. See Figure 6.1.

Google search on "Analytics Training"

Figure 6.1 Google search on "Analytics Training".

[hat is a big number. These results included a majority of university options4, but there were also results for-profit training centers, not-for-profit training outlets, industry-specific training, and outlets for professional credentialing. There was a lot of paid advertising and some options that were potentially predatory. Those billion+ results can be confusing for both individuals looking to “sharpen” their analytical saws, as well as for managers of employees looking to upskill their teams.

Below, we provide some definition and distinction to those billion+ results and explain where these options may or may not fit within a larger university collaboration.

 
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