As part of our New Year’s resolutions, we all have habits we’d like to eliminate, as well as others we’d like to establish. You may trade your tendency to procrastinate for after-lunch productivity, trade the late-night “doomsday” for a book before bed, or hit the gym instead of watching the motivational clips that prompt you to do so. Whatever it is, we know in the back of our minds that there is often a better or more challenging way to do things. But there’s a reason so many resolutions fail: we’re often unaware of how ingrained the unwanted habits are, making it harder to learn new ones. Replacing them requires a conscious commitment to change.
Developing professional skills is the same. Whether we’re earning a specific certification or developing less tangible (but no less important) traits, we need to learn consciously and make clear plans for acquiring skills – it’s important to be proactive, not passive.
In the world of technology and data, this mantra is even more fundamental. When society and businesses are evolving rapidly, it’s because innovation drives them, which means digital skills can become obsolete almost as soon as they’re learned. Continuous learning is something of a requirement for most job roles, but it’s even more important when it comes to technology. The art of “learning your craft” and using it well still has some influence, but adaptability is the name of the game when it comes to data literacy.
Kevin Hanegan, Chief Learning Officer at Qlik
It’s time to learn how to unlearn
However, if we come back to the notion of commitment to change, note that change is not a one-way street. Developing professional skills, especially in a fast-moving world like the data economy, does not necessarily mean adding unlimited new skills to build the largest possible portfolio. It is just as important to unlearn superfluous ones.
It may seem counterintuitive to intentionally remove capabilities, but no one has unlimited capacity to develop new ones while maintaining high standards of performance elsewhere. Furthermore, the new can sometimes conflict, if not contradict, with the old. During stressful times, especially when new skills have not fully become a habit, it’s easy to fall back on previous, outdated but familiar ways of working.
Unlearning is at the core of data literacy
In a professional context, data literacy is one of the most important areas to learn and unlearn skills quickly. Again, this may seem counterintuitive. After all, data is first and foremost facts and figures – while we certainly need to learn how to leverage new tools to better access data and glean insights from it, we are certainly primed to comprehend and comprehend once we have a have achieved a certain level of data literacy use data effectively.
Unfortunately, even for seasoned data practitioners, it’s more complicated. New data sources are constantly being created and incorporated into organizations, with their own patterns and behaviors to learn. Existing data sources also develop further, show new trends or become less important. Where does unlearning come into play? If we give equal status to new sources or ignore what they show us because they don’t meet our expectations, those watching will miss out on new opportunities and risks.
Ultimately, data literacy is a constant process of learning and unlearning, shedding assumptions and using our experience and understanding to get to the heart of what data is telling us. So how do you learn how to unlearn?
Three steps to unlearning
Broadly speaking, there are three steps to unlearning:
1. Know when it’s time to unlearn: Be aware when something we’ve learned no longer applies. Sometimes that’s easy – there could be a specific rule change. Typically it is more of a challenge as many skills (like habits) become unconscious. In data literacy, this means having a process that highlights when something isn’t working.
2. Autonomous learning: Once the need for change has been identified, we must first acquire the new skill. However, this must be done independently of the existing approach. Acquiring a new skill is a fragile process and there is a lack of evidence that established skills need to prove their worth so they cannot overlap with current skills – just as outdated and updated views of data should not be used at the same time. This is crucial to unlearn the previous skill.
3. Break the habit and make it again: To truly master a skill, it must become second nature, so that it can be used unconsciously, even when under pressure. However, this is not easy when experienced team members who are used to doing things a certain way are influencing those around them. Less experienced colleagues could follow their example as best practice even if there is a more productive solution trying to infuse from leadership. Giving them time to adopt new habits without being influenced by experienced colleagues who may find change more difficult could be beneficial.
All organizations need their people to thrive in the future of work, and data literacy is an important foundation upon which to build. But even the skills at the top need to be replaced at some point. After all, it’s nice to become an expert in a particular field, but sometimes the field itself changes. So if professional skill development is part of your New Year’s resolutions, consider what current experiences need to be recognized or forgotten before building new skills, and challenge yourself accordingly so as not to fall back into old habits.