Managers understand that having employees who understand the latest tools and technologies is vital to keeping a company competitive. But training employees on those tools and technologies can be a costly endeavor (US corporations spent $87.6 billion on training expenditures in 2018) and too often training simply doesn’t achieve the objective of giving employees the skills they need.
At The Data Incubator, we work with hundreds of clients who hire PhD data scientists from our Fellowship program or enroll their employees in our big data corporate training. We’ve found in our work with these companies across industries that technical training often lacks three important things: hands-on practice, accountability, and breathing room.
Hands-on practice. Learning is not a spectator sport. A study by researchers from Bucknell University found active learners perform better than their passive peers in mechanical and electrical engineering. When learning new concepts, it’s one thing to grasp the “big picture” but it’s another to implement skills or new knowledge. Especially with technical subjects, the devil is in the details and getting those details right is what distinguishes good practitioners from charlatans. Managers should look for training courses that emphasize hands-on labs or projects.
An effective active learning curriculum for data science revolves around mini projects, which require students to complete canonical workflows (e.g., machine learning) using standard tool sets and techniques (e.g., deep learning or Spark) used by industry practitioners. When evaluating programs, ask how much time is spent in lecture versus time practicing the material. And be wary of trainers that primarily distribute glossy presentation decks and then read from that deck.
Accountability. The largest cost of training is often not the fee but the opportunity cost of employee time spent in training. To justify the investment, ask your training group what the learning objectives are and how students are measured. As an example, our mini-projects automatically grade trainee performance on tasks like data-wrangling, ETL, or natural language processing, and managers and training leads at our clients are given a real-time dashboard to monitor trainee performance.
Measuring trainee performance extends beyond data science — many technical subjects have right and wrong answers that can be used to assess student learning. Quantifying demonstrated output on practical real-world datasets is the only protection from giving trainees a vague understanding of the concepts but not the skills to implement them.
Again, be wary of technical training programs that don’t offer rigorous assessment and accountability mechanisms. Measurement does not guarantee accountability, of course, and while managers need to hold trainers accountable for learning outcomes, they should also hold their employees accountable for learning. Employees shouldn’t consider training paid vacation time. Tell trainees that you take their learning seriously and review their training performance regularly.
Breathing room. One of the most common pitfalls of corporate training is not giving employees enough breathing room to complete the training and learn the concepts. While this likely holds true for even non-technical corporate training, the problem is particularly acute for technical subjects, which often have steep learning curves and require a long uninterrupted period of study to master. For example, our module on Hadoop or MapReduce requires students to grapple with moving from familiar single-core computing to the foreign concept of distributed computing, a significant paradigm shift that requires time to absorb.
We conducted a case study of one of our training sessions and found that employees who were temporarily relieved of their duties learned over twice as fast as those who were not, more than making up for the lost productivity from training. Managers cannot demand that employees learn new skills while keeping up with a full workload. Also, relieving trainees of work duties signals a commitment to an employee’s career growth which can reap benefits in terms of long-term employee loyalty.
The field of big data changes fast. In fact, some of the most popular programs and packages see a version bump every month. Many managers know they need to keep up and the only way to do that is to commit to developing your employees’ skills. Whether you’re trying to help employees bone up on technical skills, like we do, or you’re hoping your people gain broader skills, demand training that emphasizes hands-on practice, accountability, and breathing room.
This article is written by The Data Incubator founder Michael Li. It was originally featured on Harvard Business Review.
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