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Data literacy is the ability to read, write, understand, work with and communicate about data in context. Data literacy is more similar to a language than a tool. A data-literate professional possesses the knowledge and skills to access, interpret, act on, and communicate information to support organizational decision making.
Data literacy can help non-data professionals read and understand data as well as use it to inform their decision-making. In other words, data literacy is understanding when there is a need to collect or source data to answer a question and having a strategy to start that process. It’s being able to use data analysis software to perform tasks, explore, visualize, summarize and analyze the data using the appropriate techniques, and most importantly, it’s being able to draw the right conclusions from data and communicate them to the right audience in the right way.
Why Data Literacy is Important
Many organizations rely on data to make decisions to better understand outcomes and to plan for the future. Organization members must be able to use data to drive results, reinvent business processes, understand customers more deeply, discover new sources of revenue and better-balancing risks and rewards.
Companies also use data for experimentation and intervention. For example, a retail company may run an experiment to determine which type of email advertisement is more likely to lead to a purchase.
Improving business data literacy positively affects companies’ financial attributes.
Data literacy should lead to an insight that then leads to a decision. The goal of data literacy should be to lead an individual or organization to make smart, data informed decisions.
Better understanding of data life cycle is an essential part of data literacy. It will help us to be more critical consumers and producers of data and to identify cases where data can be analyzed, presented or communicated.
The Data Life Cycle
There are six steps in data lifecycle.
1. Define a Research Question
The question will allow us to explore relationships between variables under study. A thoughtful research question helps to storyboard the process to collecting the right data, performing the right analysis, creating the right visualizations and telling the right story about the findings.
The question should be relevant, feasible, and open-ended. The question should not be too simple or biased. A good research question is not easy to answer with a quick data analysis, rather it is an informed question that can be broken down into smaller closed into questions that can be answered using available data.
2. Collect and Organize Data
Collecting data means acquiring the data that will help us specifically answer the research question. We can translate the research question into data questions. After collecting data specific to the questions, we can put the data answers togetherto provide insightful research answers. Collecting the right data is necessary. The right data will help us answer our question.
When we collect data among individual subjects, we should sort or organize all the data values related to each phenomenon we want to measure or describe into separate variables.
3. Wrangle Data
The datasets are often imperfect or messy, requiring a lot of work to get them in a state where they can actually be analyzed or mined for insights. There are often missing data values and unanswered questions.
We should make sure that all of our variables are formatted correctly, and need to check if there are missing data values or data values that don’t make sense.
Data Wrangling is the process of removing errors and combining complex data sets to make them more accessible and easier to analyze.
4. Explore and Visualize Data
We can explore variables one at a time starting with the target variable and look deeper into specific individual variables, like BMI. Similarly, we can explore common scenarios that we encounter and how to choose the right plot and numeric summary to detect a relationship between the two variables. This process will lead to insights and new knowledge to help move the research forward.
Data visualization is the process of creating graphical or visual representations of data. Data visualization make it easier to identify patterns, trends and outliers in large data sets.
5. Analyze and Interpret Data
Analyzing data is a process of looking for patterns in data that has been collected through the question and figuring out what the patterns might mean. By analyzing the data using various data analysis techniques and tools, we can find trends, correlations, outliers, and variations that tell a story. It can be done by examining each component of the data in order to draw conclusions and explain the findings in the given context.
Interpreting the data is a process of trying to explain the patterns that were discovered.
6. Communicate with Data
Communicate means sharing or exchanging information to describe what happened in the past, sharing news or a great insight or sharing the prediction being made.
Depending on the audience, we may have to choose a few specific steps. What is most important for the audience to know? We can give a preview of the final findings so that the audience has something to look forward to.
When it comes to communicating with data, plots, maps and other data visualizations can have a big impact on the audience’s understanding. When designed and labeled well, data visualizations can help increase data literacy because patterns and relationships are made clear. When designed poorly, data visualizations can lead to misunderstanding, misinterpretation, and misuse.
We live in a data driven world with lots of digital transformation happening around us and data literacy can influence us to make better decisions. It has impact on business characteristics, sharing experiences and knowledge, better collaboration and continuous learning.