I get it. 

Workforce training is tough. You and your colleagues are busy with your day jobs. You probably have a full life outside of work. Despite the fact that the internet has a lot of free data training courses, and most companies have access to at least one learning platform, carving out the time and dedicating it to learning a new skill is a big ask. Even bigger is understanding how to apply your team’s data science lessons in the appropriate context on the job.

Yet the outcomes most organizations can realize from a strong data program are seductive. Here are a few examples we’ve seen at Data Society:

  • An organization that automated grant application sorting and processing based on topic and research type. By achieving a 90 percent accuracy rate, they saved hundreds of hours per month, allowed the grants team to develop their program more effectively, and saved more than $1 million over the course of one year.
  • A company that used data about parking activity (and even weather) to optimize where to place 20 electric car charging stations in each lot they owned. When the stations are installed, they will generate $2 million a year, an entirely new revenue stream they hadn’t even considered before. 
  • A medical device company that used a data algorithm to understand defect rates and pinpoint where they were coming from, saving countless lives and dollars. 

All of which begs the question: if we have access to all of these resources and the returns are so promising, why isn’t behavior around data culture and data literacy changing at the pace that’s required for breakthroughs?

I see five ways that data leaders can create the conditions that drive lasting change and significant business impact.

  1. Give employees time. Data leaders must commit to letting colleagues and employees invest the time to complete data training courses that may run six to eight weeks on a part-time basis. A lot of organizations have the money, but don't want the workforce to take time away from other things. In the case of data, this can be self-defeating. At an organizational level, at a cultural level, if employees are not given the time that they need to actually do this, then they’re not going to feel motivated. 
  2. Create incentives. What’s in it for me? This question is likely on the minds of every employee when it comes to data training, so think through how building these skills could relate to their job performance metrics, their goals, and even their compensation. Then make those connections clear. Another way to incentivize data-driven decision-making is to ensure that they have access to the tools they need to apply their skills. 
  3. Link assignments to skills. There are a lot of generic data training courses out there that speak on a general level to how new data skills can be applied, but they often don’t speak specifically to employee use cases or even industry. A lot of the reason why data training doesn’t fully deliver has to do with the difficulty that learners have in connecting the concepts to their actual work. Again, think through how you’ll make the most of your employees’ new skills by tailoring assignments that help them to apply these skills and staffing them on the right projects. Once they see their manual efforts dropping and their efficiency rising, they will be hooked. 
  4. Foster empowerment and accountability. Empower the people who work at your organization to create change, but ensure that they’re accountable for their new skills as well. We like to use virtual instructor-led courses and design capstone projects at the end of training for the same reason: people are empowered when they learn with colleagues and expert instructors and feel more accountable to attend, study, and learn the skills.
  5. Provide ongoing resources. Even if the projects your employees are working on are not directly related to data, you can still sponsor data hackathons, lunch and learn presentations, and communities where knowledge sharing is the norm. This doesn’t have to be a heavy (or expensive) lift. Just bringing in other employees in your company who actually use data to speak to how they're using it within the organization is as simple as it is powerful. One significant untapped resource we see is that people in organizations don't share knowledge with each other. 

It bears mentioning that it's often hard for organizations to sponsor data training across the entire company. Many times training programs are focused on a subset of teams or departments. These investments are commendable, but they often don’t disseminate the necessary data knowledge and skills across teams. As a final recommendation, do whatever you can as a data leader to ensure that the training you offer is sufficient and widespread enough to maximize impact