"Also, remember that data literacy is not just a set of technical skills. There is an equal need and weight for soft skills and business skills. This can be misleading for some technical resources within an organization, as those technical resources may believe they are data literate by default as they are data architects or data analysts. They have the existing technical skills, but maybe they do not have any deep proficiencies in other skills such as communicating with data, challenging assumptions, and mitigating bias, or perhaps they do not have an open mindset to be open to different perspectives." (Angelika Klidas & Kevin Hanegan, "Data Literacy in Practice", 2022)
"Critical thinking is part of data literacy; it is the ability to question the logic of arguments or assumptions and examine evidence in order to determine whether a claim is true, false, or uncertain." (Angelika Klidas & Kevin Hanegan, "Data Literacy in Practice", 2022)
"Current decision-making in business suffers from insight gaps. Organizations invest in data and analytics, hoping that will provide them with insights that they can use to make decisions, but in reality, there are many challenges and obstacles that get in the way of that process. One of the biggest challenges is that these organizations tend to focus on technology and hard skills only. They are definitely important, but you will not automatically get insights and better decisions with hard skills alone. Using data to make better data-informed decisions requires not only hard skills but also soft skills as well as mindsets." (Angelika Klidas & Kevin Hanegan, "Data Literacy in Practice", 2022)
"Data literacy is not achieved by mastering a uniform set of competencies that applies to everyone. Those that are relevant to each individual can vary significantly depending on how they interact with data and which part of the data process they are involved in." (Angelika Klidas & Kevin Hanegan, "Data Literacy in Practice", 2022)
"Decision-makers are constantly provided data in the form of numbers or insights, or similar. The challenge is that we tend to believe every number or piece of data we hear, especially when it comes from a trusted source. However, even if the source is trusted and the data is correct, insights from the data are created when we put it in context and apply meaning to it. This means that we may have put incorrect meaning to the data and then made decisions based on that, which is not ideal. This is why anyone involved in the process needs to have the skills to think critically about the data, to try to understand the context, and to understand the complexity of the situation where the answer is not limited to just one specific thing. Critical thinking allows individuals to assess limitations of what was presented, as well as mitigate any cognitive bias that they may have." (Angelika Klidas & Kevin Hanegan, "Data Literacy in Practice", 2022)
"Data literacy is something that affects everyone and every organization. The more people who can debate, analyze, work with, and use data in their daily roles, the better data-informed decision-making will be." (Angelika Klidas & Kevin Hanegan, "Data Literacy in Practice", 2022)
"It is also important to note that data literacy is not about expecting to or becoming an expert; rather, it is a journey that must begin somewhere." (Angelika Klidas & Kevin Hanegan, "Data Literacy in Practice", 2022)
"Organizations must have a plan and vision for data literacy, which they then communicate to all employees. They will need to develop and foster a culture that embraces data literacy and data-informed decisions. They will need to provide employees with access to various learning content specific to data literacy. Along their journey, they will need to make sure they benchmark and measure progress toward their vision and celebrate successes along the way." (Angelika Klidas & Kevin Hanegan, "Data Literacy in Practice", 2022)
"People can get confused or experience anxiety when they have to work with data and analytics. As we data literacy geeks say, people shouldn’t be pushed to work with data and analytics - they should do this because they want to. [...] Visualizations that are not understood present another risk. To be successful with data and analytics, we need visualizations that are presented in a clear meaningful way. If we do not take care of the data literacy levels within an organization, we might lose our public. Therefore, it is necessary to think of the risk of overwhelming our readers/viewers." (Angelika Klidas & Kevin Hanegan, "Data Literacy in Practice", 2022)
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