Upending Ineffective Training Practices in the Age of Big Data
Effective training and development programs are central to an organization’s ability to upskill its workforce, innovate, compete successfully, and deliver tangible business growth.
However, the world of work is constantly in the throes of change. We’re seeing employee churn rates rise with every generation—the median worker tenure is 4.2 years, with Millennials only spending an average of 2.8 years in one job. And as the way we work becomes more fluid with a blend of contingent and seasonal workers, it’s no wonder that budgets for training initiatives are sky high. According to Training magazine’s 2017 Training Industry report, total training expenditures reached $90.6 billion in 2017.
The question is, how successful are these programs in leveling up the skills of employees? Particularly when it comes to training workers on using a new technology, traditional mechanisms are likely to be ineffective. In fact, research has shown that despite such significant time and money spent on training and mentoring workers, we’ll forget 70 percent of what we learn in a day—especially if the skills are not applied immediately.
Consider a person leaving a job where he or she learned how to use one HR system to update personal information and conduct annual performance appraisals. Upon moving to another job after a couple of years, this individual encounters a different software that aims to achieve the same thing. Every year as part of the onboarding and ongoing training process, organizations simply put their next round of hires through existing programs to get them up to speed on the latest tools.
The Trio of Training Challenges
I see three challenges with current training methods:
1. They are typically passive exercises. Employees congregate in a conference room, sitting through a presentation or sifting through a document that explains things step by step. It’s a time-consuming exercise that assumes everyone has the same base level of familiarity and skills in this “one-size-fits-all” method.
2. Because training is conducted in a uniform manner, it’s not tailored to meet the specific user’s needs. Once the employee actually goes to use the system, he or she probably will wind up asking either a co-worker or IT support for help on how to use it, or waste time digging through FAQ documents, e-mails, or other materials to find the answer. Often, employees will take a shortcut on how to use the tool, inadvertently omitting critical information the business needs. For example, sales reps may need to log into Salesforce to update the details of their latest prospects, but will skip filling in important fields because they didn’t realize it was important to provide the business with a full view of new leads in the pipeline. This lack of awareness can carry on as they also direct newcomers on how to use the application.
3. Training frequently lags behind the pace of technology change. If you’re training someone on how to use particular software, in the world of cloud-based solutions, technology is apt to go through constant interface and feature updates. A static approach means organizations are always playing catch-up—whether it’s updating the FAQ document, sending another e-mail, or hosting another training session.
Training Through Data
Fortunately, in the burgeoning era of big data analytics and machine learning/artificial intelligence (AI), these technologies can play a role in learning user behavior and gaining insights on how to customize training to suit the individual’s need. These insights already are being used to “layer” onto any system—whether it is a piece of software or even a Website—to understand and analyze employee use, providing data including time spent on the site, what information was omitted, and common errors to task completion.
Using this valuable data, Training and Development teams can identify how to better engage and guide users to quickly and accurately work in the application. For example, the HR director, working alongside the head of sales, may decide to invest more time in supporting junior sales reps with highly customized and active training on using Salesforce to gain better visibility into the sales pipeline.
Furthermore, AI today is providing more contextual guidance on how to use any software. By obtaining deep insights into user behavior, advancements in AI are proactively walking through the steps with employees, reducing the need for IT help desk support and improving productivity.
Focusing on More Strategic Initiatives
At WalkMe, the use of internal analytics, powered by AI, and coupled with contextual guidance has resulted in the company significantly improving the completion time for our performance reviews to virtually 100 percent by deadline. Using tools that exist today, we identified who still needed to complete the appraisals, understood the main barriers to adoption, and provided tailored step-by-step guides for each user.
It’s increasingly difficult to develop programs focused on training users around specific technologies, so providing a GPS-like solution in the online world is a more viable option for engaging and guiding today’s digitally overwhelmed employees in particular. It makes sense to focus on how the data we have can help us quantify the improvements we’re making in supporting the needs of the workforce in real time, and how this then can deliver measurable bottom-line benefits.
Rather than expecting users to learn and readily adopt a system, the better approach is to have the system learn your users. In our data-driven business world, this is a reality we can expect, giving companies the latitude to focus on more strategic initiatives.
Dan Adika cofounded WalkMe, a leading digital adoption platform, in 2011. He has more than 10 years’ experience working in Web technologies, working at HP and earlier, in the Israeli Defense Force’s elite computing unit. A frequent business and technology industry speaker, Adika was rated among the top three CEOs in Silicon Valley by Glassdoor and Battery Ventures.