A conversation with Qlik’s Chief Learning Officer, Kevin Hanegan on Data Literacy, Storytelling, and Analytics.
Data is fast becoming one of the most plentiful and required resources at any organization’s disposal. EHS organizations are no exception to this rule, and the ability to access and interpret data is one of the highest ranked priorities for EHS software users, according to Verdantix. However – to do so, business leaders need to prioritize their workforce’s data literacy.
To investigate and better understand the skills required, Qlik’s Chief Learning Officer and author of Data Literacy in Practice: A complete guide to data literacy and making smarter decisions with data through intelligent actions, Kevin Hanegan, sits down with our Product Marketing Manager, Stuart Cook, to discuss:
- What is data literacy and storytelling?
- Examples of technical skills and soft skills.
- Incorporating AI in data analysis.
- How organizations can improve literacy.
Stuart Cook: What does Data Literacy mean?
Hanegan: One’s ability to interpret what statistics and analytics are telling them is arguably as essential as carrying out analytics. An organization can have the most powerful data analytics tools in the world – but if they don’t understand what the data is telling them, they won’t be able to make good decisions with it! One aspect of data literacy is knowledge of statistical figures, such as percentages and mean values – along with both the value they bring and the limitations.
In my opinion, one of the single most important skills you need is data storytelling, a term that often goes hand in hand with data literacy. Communicating with data is a vital skill – and one that is becoming more and more relevant in every organization we work with as data volumes increase.
Stuart Cook: Why is Data Storytelling so important?
Hanegan: Say you’ve been running the numbers and have come up with some strategic decisions that could save your organization millions of dollars. If you can’t communicate that information effectively, it could be ignored. This can be even more catastrophic in the realm of EHS where you may be looking at data that impacts people’s lives.
Data storytelling could be seen as an umbrella term for effectively presenting key insights. Within any organization, you need to work on communicating data in ways that key stakeholders understand and can act upon. Powerful data storytelling can help bring data to life through well-constructed narratives and powerful visuals.
Want to learn more about Data Literacy? Check out our blog How Analytics is Changing the EHS World and Why Companies Need to Capitalize (or risk falling behind)
Stuart Cook: How do you begin introducing the concept of data literacy to an organization?
Hanegan: We can begin with a common misconception about data literacy and data-informed decision-making that many organizations make. They assume “once we implement the tools to analyze raw data, we’ll get our insights”. Even with the best data strategy in the world, you need your decision makers to fully understand the competencies that can help turn data into insights.
When you try to extract insights from data, tools that help you with things like data extraction, preparation, analytics, and visualization are all obviously very helpful – but if you aren’t consciously aware of your own biases and assumptions, it’s very easy to reach the wrong answer. Once we’ve explained that, we can move onto technical skills, soft skills, and mindsets.
Stuart Cook: What are some effective first steps or strategies for organizations to foster a culture of data literacy?
Hanegan: Organizations must secure leadership buy-in, provide foundational training to all, and introduce user-friendly tools. It’s essential to embed data into daily decision-making, celebrate data-driven successes, and maintain an environment of continuous learning. In essence, nurturing data literacy blends top-down commitment with grassroots enthusiasm, ensuring informed decisions and fostering innovation.
Stuart Cook: Okay, so what are the technical skills associated with Data Literacy?
Hanegan: Technical skills range from extracting, transforming, and standardizing raw data to performing analytics to generate insights. What we’re getting at is – they’re broad and far-reaching.
Furthermore, data extraction is extracting data information from big-data systems and technologies. Data preparation means cleaning, standardizing, and organizing to make data ready for analysis. On the other hand, data analytics itself in its most simple form refers to turning data into insights. There are many more we could point to – and that’s why data analytics teams are consistently growing; organizations need a team of people with a variety of technical skills to get the most useful insights out of their data.
Stuart Cook: And what about ‘soft skills’ required to interpret data?
Hanegan: Soft skills are more akin to what we were discussing around the data storytelling side of things. It might sound counterintuitive to say data literacy isn’t all about data – but it really isn’t! Sure, organizations need to be able to analyze the data at their disposal, but they also need to think about the more creative side of things; thinking critically about their organizations’ data, being curious, bringing multiple perspectives, and leaving assumptions about data at the door.
Stuart Cook: How do you see the interplay between soft skills and technical skills? Can one compensate for the other?
Hanegan: Technical skills and soft skills are like two sides of the same coin; they complement rather than compensate for each other. While technical skills provide the foundation to extract, process, and analyze data effectively, soft skills, such as communication, critical thinking, and empathy are vital for interpreting the results and conveying them in a meaningful way to stakeholders. Therefore, a data-literate individual or team ideally would blend both skill sets, ensuring data insights are both accurate and impactful.
Stuart Cook: Do you think AI will help with those soft skills? Or even render the need for them redundant?
Hanegan: AI is absolutely helping to automate some aspects of data analytics – but they negate the emotional and social skills humans bring to data storytelling. As I have noted, critical thinking, creativity, problem solving; these things are all unique to humans, and a great data analysis team cannot be replaced.
Stuart Cook: Do you foresee a future where the human element in data analysis becomes minimal as AI continues to advance?
Hanegan: As AI technology continues to mature, its capabilities in data processing, pattern recognition, and predictive analytics will undoubtedly expand, automating many tasks that are currently manual and time-consuming. However, the human element in data analysis will always be crucial. AI can identify patterns and provide insights, but human intuition, contextual understanding, ethical considerations, and emotional intelligence are irreplaceable. These qualities guide how we interpret, value, and act upon the insights AI provides.
Stuart Cook: How should organizations prepare for the introduction of AI?
Hanegan: While AI will handle more routine analytics, the human role will likely shift towards more complex tasks such as formulating the right questions, ensuring ethical data use, and making nuanced judgments that consider societal and cultural contexts. Organizations should prepare by investing in continuous learning and emphasizing the development of soft skills. However, the goal isn’t just to keep up with AI, but to understand how to collaborate with it effectively, ensuring that human wisdom directs technological capabilities.
Stuart Cook: What are common pitfalls of organizations trying to improve data literacy, and how can they be avoided?
Hanegan: In the journey to improve data literacy, organizations often stumble by solely investing in tools without adequate training, or by adopting a one-size-fits-all approach. It’s crucial to remember that the right tools, customized training, and a balance between technical and soft skills are the trifecta for success. Additionally, fostering a holistic, data-informed mindset, paired with continuous learning, is the bedrock of a truly data-literate culture.
Stuart Cook: How can organizations improve upon existing data literacy?
Hanegan: By far, the most important question to ask yourself in any data analysis is ‘why?’. Furthermore, to go above and beyond, and be truly ‘data literate’, you can remind yourself to be curious, and creative in the way you go about your analysis. Ultimately, the best data storytellers are willing to challenge their own assumptions and look at data and scenarios from a different angle. For practical ways you can go about doing this, I would point to Qlik’s Continuous Classroom to check out the courses and resources available. There are tons of courses on upskilling and creative approaches to data analytics.
Sifting through data, creating unified data sets, understanding the latest trends, and setting the right KPIs is no simple task. But, with analytics software, the possibilities are endless. Cority’s Analytics Cloud enables organizations to make data-driven decisions that result in a healthier, safer workforce and a more sustainable future. To learn more, request a demo or read our eBook, Preventing Harm Before it Happens: How to Introduce Predictive Analytics into Your Incident Prevention Strategy to see how analytics can help your organization achieve it’s health and safety goals.