Bringing learning agility to data literacy skill development

Throughout our learning careers, which typically start in elementary/primary school, we are trained to expect a linear progression all the way through our university degrees and employment. In today’s data-driven environment, however, this linear expectation that ends at university has lulled many of us into a false sense of security. The current environment requires a more flexible, resilient, and adaptable approach, especially when it comes to data-related skills, to succeed in modern data-driven businesses. 

Organizations are clamoring for professionals who can quickly process new information and are always learning to maximize the value and impact of data. Given this, it’s no surprise that awareness of learning agility is rising, as organizations seek to create a more agile and effective workforce for now and the future.

Learning agility is a concept that encourages an approach of learning from mistakes and staying open to new information. Researchers from the Center for Creative Leadership frame learning agility as the ability to remain open to new ways of thinking and to continuously learn new skills. 

Their research sets out four key enablers of learning agility:

• Innovating – Questioning the status quo and challenging assumptions to develop multiple perspectives and solutions to organizational hurdles.
• Risking – The willingness to leave a comfort zone and explore new opportunities to achieve personal growth.
• Performing – Staying immersed in a task, remaining present in difficult situations, and being open to the ambiguity present in many situations. 
• Reflecting – An ability to appraisal one’s work honestly and valuing feedback from others to find opportunities to improve.

Aside from the four key enablers of learning agility identified above, the researchers also identified a common inhibitor. Specifically, a common urge to stick to the traditional way of doing things while blindly defending the methodology used when questioned. This is something we are all guilty of, as it’s only human nature to want to protect our work and our reputation.

So, how does this relate to data literacy and data-driven decision making? When we talk about data literacy, we often discuss the need to reason and argue with data. This requires an understanding of the underlying frameworks for data analysis – allowing us to question the validity or relevancy of results.

When data presents us with results that challenge our assumptions, learning agility helps us adapt our understanding to this new evidence. We can also develop comfort with the use of data to analyze our own mistakes and understand where we went wrong.

Crucially, we can begin asking questions and querying data when we are not sure what we are looking at. By remaining immersed in the ambiguous results of some analysis, we may eventually land on insights that prove to be incredibly valuable.

Where many people may assume that data literacy is about replacing human decisions with data-driven ones, the reality is it's more accurate to describe data literacy as using data to empower our intuition and drive innovation. Ultimately, this is what makes data literacy and learning agility an ideal match.

About the author:

As the Global Head of Data Literacy at Qlik, I help individuals and organizations realize their data and analytical potential by strengthening their data literacy. If you’d like to learn more about creating a data-driven culture in your organization please feel free to contact me at Jordan.Morrow@qlik.com.