Are you data literate? An easy way to tell is to ask yourself whether you know how to source, analyze, and share quantitative information. If you don’t, you should learn how. The good news is the process is easy, even if you’re not a numbers person. Here’s what you need to do to improve your data literacy skills. What Is Data Literacy? Data literacy is the ability to interpret and interact with information meaningfully. You work with, perform operations on, and extract insights from it. Like regular literacy, there are multiple levels of competency, ranging from basic to adept. In other words, you don’t need to be an expert in the field to demonstrate an adequate understanding. What is poor data literacy? It is the inability to interpret, interact with, and understand information. If you work in an office or administrative setting, this skills gap might manifest as needing help deciphering graphs and reports. You may have difficulty distinguishing valuable insights from the noise. At the very least, you might struggle to understand analytics. For the record, you shouldn’t confuse data literacy with analytics. While analysis technically falls under the umbrella of being data literate, it’s only a tiny part of what you need to know. Arguably, uncovering patterns or correlations is the most useful thing you can do with a dataset, but it’s not the only thing. You shouldn’t feel discouraged if you’re not data literate yet. After all, even today’s high-ranking professionals aren’t. Only half of C-suite executives feel confident in their data literacy skills, with 45% admitting they make decisions based on gut feelings instead of facts. What Are Data Literacy Skills? While dozens of data literacy skills exist, you only need a handful to start your educational journey. Mastering the basics helps you build a solid knowledge foundation. 1. Critical Thinking Critical thinking is arguably one of the most important skills because there’s simply too much data floating around, much of which is inaccurate, outdated, or irrelevant. Knowing what questions to ask can help you determine whether you want to use a source, reference a website, or perform operations on a dataset. 2. Problem-Solving Sometimes, data is incomplete or messy — it doesn’t always fit into spreadsheets neatly. Problem-solving skills can help you make those metrics workable. While you’ll have to rely on mathematics often, you don’t need to excel in the subject. You don’t necessarily even need to know how to perform calculations. More often than not, knowing how to get to the answer is enough. Say you’re trying to find how much one figure increased over time to determine growth. Instead of memorizing a formula, you’d simply use a percentage change calculator. Creatively approaching problems can help you interact with data meaningfully. 3. Communication Communication is one of the most essential skills to learn because all worthwhile findings are reviewed and replicated. These processes can help you identify mistakes and uncover opportunities. At work, viewing information as a shared resource can help foster a culture of collaboration and communication. 4. Visualization Visualization is similar to communication in that it helps you get your point across. The key difference is you prioritize storytelling. Graphs, charts, and tables make plain metrics more interesting. They also make complex topics easier to comprehend — numbers seem more real when accompanied by informative illustrations. Why Data Literacy Matters Data literacy isn’t a term you come across daily, so it might not seem important. In reality, you could consider it a life skill since you interact with information all day, every day. Utility bills, news reports, ingredient labels, medication dosages, online polls, and product recommendations are good examples. You may even read reports and process spreadsheets at work. These skills can help you better understand the world because you live in the digital age. Even if you don’t realize it, you absorb quantitative data daily. Knowing how to interpret, analyze, and explain it can help you turn seemingly meaningless numbers into meaningful insights. You learn how to develop arguments, follow along at work, and spot misinformation better. Interestingly, possessing data literacy skills also makes it easier to keep up with the latest technology trends. You need a solid understanding to get involved in trends like artificial intelligence and the Internet of Things. Recognizing the importance of sourcing, dataset cleaning and analytics goes a long way. Frankly, becoming data literate is important because there’s too much information. Experts estimate the total amount created, copied and consumed will go from 64.2 zettabytes in 2020 to 180 zettabytes in 2025 — a 180% increase in just five years. Knowing what makes metrics relevant, accurate, and reliable is essential. What You Need to Understand About Data Before you can become data literate, there are a few fundamentals you must understand. How It Works There are multiple data types, including datasets, physical documents, studies, databases and records. At its most basic, it is broken into two categories — qualitative or quantitative. Whether you leverage numbers or descriptions, you’re working with information. In data science, data is often either described as structured or unstructured. The first fits neatly into spreadsheet tables, often containing numbers, dates or words. The latter is messier. It can be social media posts, audio clips, emails, videos or documents. Where It Goes You often have to collect data to extract any meaningful results. While tons of information is available online, it may not be recent, accurate or specific enough to be useful to you. Common sources are surveys, records, studies and analytics. A few decades ago, information was usually physical — people stored records in filing cabinets and documents in briefcases. Now, almost everything is digitally stored on hard drives or kept in the cloud for remote access. What It Does You can do a lot to a dataset, including processing, analysis and insight extraction. These use cases are probably the main reason your employer collects information, unless they’re a financial or health care institution and are required to do so by law. Before you can do anything with data, you need to clean it, which involves eliminating outliers, filling in missing fields and transforming any mistyped figures. It’s a lengthy process. According to one survey, data scientists spend 39% of their time on preparation and cleaning. How to Become Data Literate Where should you go to improve your data literacy? Since just under half of employees say their employer has offered data training courses, there may not be many opportunities at work. Thankfully, you don’t need predefined courses to develop the core skills. The best way to learn is to practice — even if you’re inexperienced. Consider getting a dataset online to practice on. If you don’t know what to use, try the weather. It makes for good practice because there are always recent, accurate forecasts. At first, your goal should be to evaluate the raw numbers to ensure you understand what’s in front of you. Then, you can transition to cleaning, performing analysis and extracting insights. Figure out what questions you want to ask. Is the change from the first day to the last significant? What percentage of days were overcast? If you feel like taking things a step further, try analyzing two datasets at once — one on weather and another on beachgoers. Does the ultraviolet index affect how busy the beach is? While that example may seem overly simplistic, analysis often is. Whether you’re trying to extract insights on employee retention, construction material cost, return fraud or the housing market, you can uncover patterns by asking straightforward questions and plugging numbers into the correct formulas. Your practice isn’t finished when you produce your findings. At that point, you proceed to visualization. The best way to get better at reading graphs and tables is to make your own. This way, you’ll get used to the fundamentals, allowing you to instantly recognize a visual’s shared characteristics when someone else presents information. Making Yourself Data Literate You won’t improve your data literacy skills overnight. However, even though it will likely be time-consuming, it doesn’t have to be complicated. To make the process more enjoyable, work with information that interests you. There are patterns, trends and insights to uncover in every industry, and there is a wealth of information online for free. Are you data literate? An easy way to tell is to ask yourself whether you know how to source, analyze, and share quantitative information. If you don’t, you should learn how. Are you data literate? An easy way to tell is to ask yourself whether you know how to source, analyze, and share quantitative information. If you don’t, you should learn how. The good news is the process is easy, even if you’re not a numbers person. Here’s what you need to do to improve your data literacy skills. What Is Data Literacy? Data literacy is the ability to interpret and interact with information meaningfully. You work with, perform operations on, and extract insights from it. Like regular literacy, there are multiple levels of competency, ranging from basic to adept. In other words, you don’t need to be an expert in the field to demonstrate an adequate understanding. What is poor data literacy? It is the inability to interpret, interact with, and understand information. If you work in an office or administrative setting, this skills gap might manifest as needing help deciphering graphs and reports. You may have difficulty distinguishing valuable insights from the noise. At the very least, you might struggle to understand analytics. For the record, you shouldn’t confuse data literacy with analytics. While analysis technically falls under the umbrella of being data literate, it’s only a tiny part of what you need to know. Arguably, uncovering patterns or correlations is the most useful thing you can do with a dataset, but it’s not the only thing. You shouldn’t feel discouraged if you’re not data literate yet. After all, even today’s high-ranking professionals aren’t. Only half of C-suite executives feel confident in their data literacy skills, with 45% admitting they make decisions based on gut feelings instead of facts. half of C-suite executives half of C-suite executives What Are Data Literacy Skills? While dozens of data literacy skills exist, you only need a handful to start your educational journey. Mastering the basics helps you build a solid knowledge foundation. 1. Critical Thinking Critical thinking is arguably one of the most important skills because there’s simply too much data floating around, much of which is inaccurate, outdated, or irrelevant. Knowing what questions to ask can help you determine whether you want to use a source, reference a website, or perform operations on a dataset. 2. Problem-Solving Sometimes, data is incomplete or messy — it doesn’t always fit into spreadsheets neatly. Problem-solving skills can help you make those metrics workable. While you’ll have to rely on mathematics often, you don’t need to excel in the subject. You don’t necessarily even need to know how to perform calculations. More often than not, knowing how to get to the answer is enough. Say you’re trying to find how much one figure increased over time to determine growth. Instead of memorizing a formula, you’d simply use a percentage change calculator. Creatively approaching problems can help you interact with data meaningfully. 3. Communication Communication is one of the most essential skills to learn because all worthwhile findings are reviewed and replicated. These processes can help you identify mistakes and uncover opportunities. At work, viewing information as a shared resource can help foster a culture of collaboration and communication. can help foster a culture can help foster a culture 4. Visualization Visualization is similar to communication in that it helps you get your point across. The key difference is you prioritize storytelling. Graphs, charts, and tables make plain metrics more interesting. They also make complex topics easier to comprehend — numbers seem more real when accompanied by informative illustrations. Why Data Literacy Matters Data literacy isn’t a term you come across daily, so it might not seem important. In reality, you could consider it a life skill since you interact with information all day, every day. Utility bills, news reports, ingredient labels, medication dosages, online polls, and product recommendations are good examples. You may even read reports and process spreadsheets at work. These skills can help you better understand the world because you live in the digital age. Even if you don’t realize it, you absorb quantitative data daily. Knowing how to interpret, analyze, and explain it can help you turn seemingly meaningless numbers into meaningful insights. You learn how to develop arguments, follow along at work, and spot misinformation better. help you better understand the world help you better understand the world Interestingly, possessing data literacy skills also makes it easier to keep up with the latest technology trends. You need a solid understanding to get involved in trends like artificial intelligence and the Internet of Things. Recognizing the importance of sourcing, dataset cleaning and analytics goes a long way. Frankly, becoming data literate is important because there’s too much information. Experts estimate the total amount created, copied and consumed will go from 64.2 zettabytes in 2020 to 180 zettabytes in 2025 — a 180% increase in just five years. Knowing what makes metrics relevant, accurate, and reliable is essential. from 64.2 zettabytes in 2020 from 64.2 zettabytes in 2020 What You Need to Understand About Data Before you can become data literate, there are a few fundamentals you must understand. How It Works There are multiple data types, including datasets, physical documents, studies, databases and records. At its most basic, it is broken into two categories — qualitative or quantitative. Whether you leverage numbers or descriptions, you’re working with information. In data science, data is often either described as structured or unstructured. The first fits neatly into spreadsheet tables, often containing numbers, dates or words. The latter is messier. It can be social media posts, audio clips, emails, videos or documents. Where It Goes You often have to collect data to extract any meaningful results. While tons of information is available online, it may not be recent, accurate or specific enough to be useful to you. Common sources are surveys, records, studies and analytics. A few decades ago, information was usually physical — people stored records in filing cabinets and documents in briefcases. Now, almost everything is digitally stored on hard drives or kept in the cloud for remote access. What It Does You can do a lot to a dataset, including processing, analysis and insight extraction. These use cases are probably the main reason your employer collects information, unless they’re a financial or health care institution and are required to do so by law. Before you can do anything with data, you need to clean it, which involves eliminating outliers, filling in missing fields and transforming any mistyped figures. It’s a lengthy process. According to one survey, data scientists spend 39% of their time on preparation and cleaning. spend 39% of their time spend 39% of their time How to Become Data Literate Where should you go to improve your data literacy? Since just under half of employees say their employer has offered data training courses, there may not be many opportunities at work. Thankfully, you don’t need predefined courses to develop the core skills. The best way to learn is to practice — even if you’re inexperienced. just under half of employees just under half of employees Consider getting a dataset online to practice on. If you don’t know what to use, try the weather. It makes for good practice because there are always recent, accurate forecasts. At first, your goal should be to evaluate the raw numbers to ensure you understand what’s in front of you. Then, you can transition to cleaning, performing analysis and extracting insights. Figure out what questions you want to ask. Is the change from the first day to the last significant? What percentage of days were overcast? If you feel like taking things a step further, try analyzing two datasets at once — one on weather and another on beachgoers. Does the ultraviolet index affect how busy the beach is? While that example may seem overly simplistic, analysis often is. Whether you’re trying to extract insights on employee retention, construction material cost, return fraud or the housing market, you can uncover patterns by asking straightforward questions and plugging numbers into the correct formulas. Your practice isn’t finished when you produce your findings. At that point, you proceed to visualization. The best way to get better at reading graphs and tables is to make your own. This way, you’ll get used to the fundamentals, allowing you to instantly recognize a visual’s shared characteristics when someone else presents information. Making Yourself Data Literate You won’t improve your data literacy skills overnight. However, even though it will likely be time-consuming, it doesn’t have to be complicated. To make the process more enjoyable, work with information that interests you. There are patterns, trends and insights to uncover in every industry, and there is a wealth of information online for free.
