Back to basics! Data literacy is described as the ability to read, work with, analyze, and argue with data. All employees in an organization involved with data-driven decisions should learn to think critically about the data they use for analytics and interpretation of the results of their work. It's key to understand more about this innovation in both business and society -- and how it affects your company.

Data has been one of the greatest drivers of innovation in both business and society in recent decades. Using data, organizations can make more data-driven decisions and inform business strategies with numbers instead of intuition. This often leads to increased performance, especially when compared to less data-driven firms.

Access to data, however, isn’t all it takes to set a business on the path to success. It also takes employees who understand and know how to leverage data. This has caused increased demand in the job market for business professionals with data literacy.

Below is an overview of data literacy, including the key concepts you should learn to better position yourself for success in an increasingly data-driven world.

WHAT IS DATA LITERACY?

Data literacy is a term used to describe an individual’s ability to read, understand, and utilize data in different ways. It doesn’t require an individual to be an expert—as a data scientist or analyst might be considered—but rather, to show an understanding of basic concepts, such as:

• Different types of data
• Common data sources
• Types of analysis
• Data hygiene
• Tools, techniques, and frameworks

Data literacy can help non-data professionals read and understand data and use it to inform their decision-making. As such, data literacy is increasingly important not just for executive leadership, but for managers and employees who want to increase the value they bring to their organization.

KEY DATA LITERACY SKILLS & CONCEPTS FOR BUSINESS

1. Data Analysis

Data analysis refers to reading and interpreting data to glean insights from it. While analysis can be conducted using statistical models, algorithms, and other complex tools and frameworks, you can also achieve it by simply reviewing data and drawing conclusions from it.

There several types of data analysis you can use. Four of the most common are:

• Descriptive analysis, which seeks to explain or describe what has happened
• Diagnostic analysis, which seeks to explain or diagnose why something has happened
• Predictive analysis, which seeks to forecast what might happen
• Prescriptive analysis, which seeks to prescribe a course of action that will lead to a desired outcome

2. Data Wrangling

Data wrangling is the act of transforming data from a raw state into a form that can be more readily used. The practice is also commonly known as data munging or data cleaning. While data wrangling can take many forms, the most common examples involve removing errors and filling gaps in data.

Data wrangling plays a critical role in reducing errors in the analysis that typically follows it. In many organizations, data is cleaned automatically through various algorithms and other tools, but every employee responsible for generating, capturing, or uploading data also plays a role in ensuring it meets the organization’s requirements.

3. Data Visualization

Data visualization is the process of creating graphical or visual representations of data and often a crucial piece of effectively communicating insights. Data visualizations serve an important role in making data more accessible to others both inside and outside an organization—especially those who may not be data literate. An example of data visualization in action is a chart or graph that helps investors understand a company’s quarterly earnings report.

Some common examples of data visualization techniques include creating charts, tables, maps, infographics, and even videos or GIFs. Visualizations can be generated using various tools and software, ranging from free options like Microsoft Excel and Google Charts to paid business software like Tableau and Microsoft BI.

4. The Data Ecosystem

The concept of the data ecosystem refers to all of the components an organization leverages to collect, store, and analyze data. This includes physical infrastructure, such as server space and cloud storage solutions, and non-physical components, such as data sources, programming languages, code packages, algorithms, and software.

Each organization’s data ecosystem is unique, though some can overlap if they leverage the same data sources or third-party tools. Understanding your organization’s data ecosystem can allow you to better understand how its pieces fit together and potentially uncover optimization opportunities.

5. Data Governance

Data governance refers to the processes and practices an organization uses to formally manage its data assets. The concept can be likened to a rulebook specifically designed to ensure an organization’s data remains accurate, secure, and complete. In fact, many organizations distribute a company “data policy” to new hires alongside the employee handbook.

Data governance is typically broken down into four key areas:

• Quality: How does the organization intend to ensure its data remains accurate, trustworthy, and complete?
• Security: How does the organization intend to secure its data from unauthorized access?
• Privacy: How does the organization intend to protect sensitive information that it may collect and store, such as customer financial information or employee records?
• Stewardship: How does the organization intend to ensure that its data processes are followed appropriately?

6. The Data Team

Finally, it’s important to understand who the key players are on your organization’s data team and the different roles they play—regardless of whether you directly work with them.

Data teams can be structured in several ways depending on your organization's size and how prominently data is leveraged in day-to-day activities. That being said, most data teams include:

• Data scientists, who leverage advanced mathematics, programming, and tools to conduct and manage large-scale analyses
• Data engineers, who are responsible for building and maintaining datasets that are leveraged in data projects
• Data analysts, who conduct the majority of the analyses an organization requires

DEVELOPING YOUR DATA LITERACY SKILLS

As businesses and organizations become more data-driven, the demand for professionals who demonstrate data literacy will continue to increase. As such, taking the time to develop your data literacy skills can be a smart investment.

You can become more data proficient in several ways. One common route is to volunteer for projects that expose you to your organization’s data and data team. Over time, you can gain a healthy understanding of how data is leveraged within your firm. Alternatively, you can enroll in an online data science or analytics course specifically designed to provide the foundational skills you need to excel in your role.

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Reference: [https://online.hbs.edu/blog/post/data-literacy]