Develop data literacy skills to advance your career

Data literacy levels

Data literacy is a complex ability that brings together a range of technical, interpretative and communication skills. Traditional literacy requires the ability to read and understand what others have written, comprehend and interpret that text, and write one's own thoughts. Data literacy also involves the ability to understand, interpret and communicate.

Because the different components of data literacy vary in difficulty, it's useful to classify different levels of literacy. There are generally four levels, which reflect the progression in skills in different applications of literacy in personal and business settings.

Basic data literacy

Basic data literacy is foundational. Users at this level can understand data concepts and terminology, such as the difference between structured and unstructured data or how to interpret KPIs. They can read simple visualizations, such as bar charts and line graphs.

In a data-oriented discussion, someone with basic data literacy should be able to participate and understand what data is relevant to their role. They also have some awareness of how data quality and privacy affect their work.

Working data literacy

Working data literacy means users confidently engage with data in their daily role. They can understand data and analyses from others, select data for their own simple visualizations and derive their own interpretations. Users identify data quality issues and articulate data requirements, although they might not be able to implement the changes themselves.

Career advancement increasingly depends on the ability to work confidently with data.

When it comes to data analysis, working data literacy includes an understanding of statistical concepts such as probability, data distributions and what makes for a valid sample.

Business users with working data literacy understand the difference between leading and lagging indicators as they affect the job role and can initiate data-oriented discussions.

Tactical data literacy

Tactical data literacy is the level at which people start to rely on data creatively. They can create new workflows or initiate new projects where data and analytics are critical to success. They know how to ensure data quality, validate data quality and select appropriate analytical methods. They also know how to communicate project outcomes with data.

A user might understand quite advanced concepts, such as the principles behind predictive analytics and modeling, even if they can't build or tune a model themselves. A user with tactical literacy could have well-informed conversations with data engineers or data scientists about how their work aligns.

Strategic data literacy

Strategic data literacy implies a more advanced understanding of data and a thorough understanding of how to establish a business strategy that depends on data and critical analytics for its success.

A key skill in strategic literacy is the ability to work with data across different business domains, not just one specialization. A strategically literate HR manager should be able to look at the metrics and analytics of a marketing campaign and understand its implications for promotions or hiring on that team.

Strategic data literacy involves an understanding of the principles behind data management and data analysis, combined with the ability to translate insights into business strategy across domains. Increasingly, strategic data literacy also requires a thorough grasp of the demands of data governance and policy compliance.

The component skills of data literacy

The various levels of data literacy require a range of skills that develop as someone progresses through the levels. Some require specific technical training and others develop through job experience. Data literacy also requires some business skills that enable a more complete approach to data use.

Data fundamentals

Data literacy requires some understanding of what data is, the various types and structures of data and how they vary. It's useful to know about the differences between numeric and text data in terms of how to organize and use such data.

The more diverse the data, the more understanding someone needs about these types and their usage.

Analysis and statistics

The most basic analysis of data requires some knowledge and skill, even if it is simply knowing the difference between basic statistical concepts, such as mean and median, or confidently handling percentages.

It's significant in business to understand the difference between a count of items and the distinct count of unique items, or similar aggregations such as sum maximum and minimum. The concepts might seem simple, but data literacy requires some working knowledge of their subtleties, especially when using databases or spreadsheets, or when data includes many repeated or empty values.

More advanced business analysis can include using KPIs and determining whether indicators are leading or lagging.

Beyond the basics of statistics, business analysts who want to be fully data literate also need to understand concepts such as standard deviations, weighted averages and moving averages. The techniques help make sense of trends and patterns in data that show a lot of variation in the short term, such as daily sales or web traffic.

The most advanced analytics skills include predictive analytics and the use of algorithmic analysis. Advanced business users might not deploy advanced techniques in their own work, but strategic data literacy does require understanding their potential and limitations. Given the range of analytic techniques available, it's also imperative to be able to choose the right tool for a given scenario.

Visualization and communication

Just as writing is a foundational skill for conventional literacy, communicating data insights is essential for data literacy. Visualizations are the most effective way to convey the patterns, trends and complexities of data analysis. Everyone needs the ability to read and interpret common data visualizations. Further development will expand to creating effective data visualizations, selecting the appropriate methods and best practices for presenting data findings.

Data storytelling in business is rising in importance. It's the ability to build a narrative around data findings, which might include multiple data visualizations and a series of interpretations of data, leading to a call to action or some other tactical or strategic conclusion.

As with analytics, it's vital to choose the right visualization tool for the job.

Data governance

The quality of data available for decision-making is a fundamental factor in ensuring effective business outcomes -- garbage-in, garbage-out, as the saying goes. Recognizing, articulating and addressing data quality issues is a vital skill for data literacy.

A knowledge of data privacy, the ethical use of data and data governance principles is also useful. Even if the data literate user is not directly responsible for data management processes, they still need to understand the limitations data management imposes on their work. Limitations can include restrictions on why some interesting analyses -- such as those involving personally identifiable information -- might not be permitted.

Non-technical skills

In addition to technical skills, there is a wide range of business abilities that can help a data-literate user to be more effective in their work.

Critical thinking is a generally useful skill that helps individuals question assumptions and conclusions, understand context and limitations, or recognize potential biases in thinking. All these issues can arise in data analysis.

Similarly, communication skills, such as being able to explain complex processes or concepts, lead discussions and train others, are helpful when users need to promote their data insights and the use of data within an organization.

Regardless of one's data literacy level, success also requires business acumen. People need to connect data analysis to business objectives, and that requires them to break down business objectives into actionable steps to identify data-oriented opportunities within corporate strategy and tactics.

Collaboration and organizational literacy

All forms of literacy are a form of communication between people. Data literacy is the same; communication and collaboration are at the heart of the effective use of data.

Developing new insights to determine new courses of action is a genuinely useful and valuable skill to have in business. All actions, decisions and strategies require the work of others to succeed.

A successful organization needs more than a small number of data-literate people who work as isolated experts in their field. It would be like the Middle Ages, where conventional literacy was limited to a small number of scribes and leaders. In the modern world, societies function much better because literacy is widespread. Organizations and businesses will also flourish when data literacy is a widespread skill that enables more insightful decisions at every level of the business.

Donald Farmer is a data strategist with 30+ years of experience, including as a product team leader at Microsoft and Qlik. He advises global clients on data, analytics, AI and innovation strategy, with expertise spanning from tech giants to startups. He lives in an experimental woodland home near Seattle.