There is a common misconception about data literacy and data-informed decision-making. It goes that once we implement the tools to analyze raw data, it will automatically turn into insights. This sounds great in theory. But even with the best data strategy in the world, the people behind these decisions require an understanding of particular competencies – those combinations of knowledge, skills and mindsets – to turn data into insights and better business decisions.
As we have spoken about before, there is a common misconception about data literacy and data-informed decision-making. It goes that once we implement the tools to analyze raw data, it will automatically turn into insights. This sounds great in theory. But even with the best data strategy in the world, the people behind these decisions require an understanding of particular competencies – those combinations of knowledge, skills and mindsets – to turn data into insights and better business decisions.
When trying to extract insights from data, technical skills like data extraction, preparation, analytics and visualization are naturally useful allies. But if those insights are not being critically challenged to avoid bias or incorrect assumptions, it’s easy to jump to the wrong conclusion. In our previous blog, we discussed the broad competencies that can support data literacy. Let’s explore the technical skills, soft skills and mindsets that underpin them.
Data Literacy Technical Skills
Let’s start with the technical skills. These range from taking raw data and extracting, transforming, and standardizing it, to performing analytics to generate insights.
The set of technical skills available are broad and include data extraction (extracting the data information from big-data systems and technologies), data preparation (cleaning, standardizing, and organizing the data to make it analytics ready), data analytics (turning data into insights), data visualization (the process of putting data and information into visual form to allow you to communicate the data better to others), Artificial Intelligence/Machine Learning (using computer algorithms to implement predictive models to forecast future events) and data science (using statistical algorithms and techniques to find patterns and then make predictions from them). Each of these elements are crucially important, but that’s not to say that everyone needs all of these skills. That’s why data is constantly referred to as a team sport. You need a group of people all with their individual specialisms to get the most out of data.
The Importance of Data Storytelling
Just because a role isn’t technical in nature, doesn‘t mean it’s not important. The interpretation of statistical and analytical results carry as much weight as the analysis itself. While individuals making decisions using insights obtained by others do not need to me masters of data analytics and data science, they do need to be able to interpret the results. This is where knowledge of statistical figures like mean values and percentages comes in, along with their significance and limitations. And of course, this is far more valuable alongside knowledge and context of the business related to the question being asked. However, in my opinion one of the single most important skills you need is data storytelling.
Communicating with data is a vital skill that applies across multiple stages of the process. You may have just come up with a decision that will save your organization millions of dollars, or save countless lives, but if you cannot communicate the right information at the right time in the right way to the right people, it may be ignored or ostracized. Finding key insights to inform a decision is one skill, but presenting it is another altogether. Good communication of data-informed decisions can help stakeholders understand, accept, and then act on them.
Appropriate and skillful communication of data includes an approach called analytic storytelling. This is the process of bringing data to life through a well-constructed narrative. In analytic storytelling, you use the right data and the right visualizations to support your decisions, along with the right amount of storytelling to get your message across at the right level. You do not want to overwhelm or bore your audience. Most importantly, you should not only present the data but also include the appropriate context. Give the audience the idea, the picture, and then the applicable details.
Data Literacy Soft Skills
This allows us to segue into the soft skills. It may sound counter intuitive to say but data literacy is not all about data. Yes, you need to analyze and work with the data at your disposal, but it also involves developing non-technical skills like curiosity, critical thinking, creativity, and collaboration to gain different perspectives and challenge assumptions about the information.
While advances in AI are making it possible for an increasing number of human tasks to be automated, machines still lack emotional and social skills as well as higher cognitive skills such as problem solving, critical thinking, creativity, and systematic decision-making. That’s why humans will never be taken out of the equation.
The Most Important Question is Why
It’s safe to say that curiosity is the greatest human asset and the most important question is ‘why?’. Asking this is how we all learn. However, we are always taught there is only one right answer, so we tend to stop looking for alternatives and interrogating beyond this given. But we shouldn’t and we can’t. To be truly data literate, we need to be intellectually humble and creatively curious. That means being willing to unlearn and look at data and scenarios under a new light. There are always alternative ideas and approaches out there, but if we are not curious enough to find or notice them, we will never use them.
Another key soft skill is ‘systems thinking’. This allows us to view issues holistically and see less obvious connections between things, while understanding why they behave a certain way. It is a way of looking at an organization (and the world) as a set of systems that all connect in some way. When viewing a business this way, identifying causes versus symptoms becomes easier, as decision-makers can consider how each part interacts with each other.
Ultimately, we can possess all the technical skills needed to leverage data in the corporate arena, but without the ability to understand what key stakeholders want, this data is not valuable. This is where ‘forever skills’ such as active listening come into play, which involves focusing on the complete message being communicated. This naturally involves paying attention and not being distracted, but also inclusion by taking on board different styles of problem-solving and a range of perspectives.
Changing Our Mindsets
Data literacy is more about evolving mindsets than anything else. Sure, there are tools and technologies that will make us more data literate. And those are critical. But a mindset shift sits at the heart of this upskilling as we transition from the information age to the knowledge age. In the information age, many have the belief that some data is better than none. In reality, some data can be very harmful indeed.
In the knowledge age, we need to think critically about data and information. We need to understand what data is relevant and what it is not. We need to learn how to interpret the results to avoid any implicit bias. Data needs to become a native language. If someone says something we think is incorrect or misleading, we need to question it. We need to be open to being wrong and having the intellectual humility to admit that and explore alternatives. All this only comes with a combination of technical and soft skills – crucial to becoming truly data literate.
For more information on how to upskill across each of these skills, visit Qlik’s Continuous Classroom to check out the courses and resources available.