What is data literacy and why is it useful?

Data literacy is a fundamental understanding of what data is, what data sources are, how to read and write data, and how to communicate different kinds of data. Situations where data literacy is particularly useful include:

• knowing when and how to access the right data to inform your decisions;
• understanding what pre-existing data sets are available to the business;
• knowing how to create, collect and successfully store data using the right computer technology;
• being able to understand what the data is saying, and using it to make key decisions, with awareness of their potential outcomes;
• being able to describe what the data is saying to the rest of the team using language that is easy for most colleagues to understand;
• needing to know if some data is potentially biased and why;
• understanding how to use data in people analytics;
• needing to find out whether data is valid;
• being able to clean data effectively.

Data literacy is becoming a potential skills gap as an employee’s responsibilities change to face modern business needs. When your employees have a good level of data literacy, your business can become more competitive in its field. This might even make your business more attractive to prospective new recruits who are looking to build their skill-sets for the modern world. A data literacy plan could be part of your overall upskilling strategy for your employees.

Data skills examples

When your employees are data literate, they can better understand why data looks the way it does, including the context surrounding the data itself. When looking at HR data collected by your business, it is good for your analytics team to understand why, for instance, staff turnover is particularly high at certain times of the year (such as seasonal workers leaving at the end of the Christmas period). When your employees understand the context that data is presented in, they are more likely to gain meaningful insights. 

Spreadsheet applications and data collection

Digital spreadsheet tools are also key to a data-literate employee’s skill-set. Spreadsheet applications allow their users to enter data and manage data collections over an extended period of time, and usually allow users to create easily shareable files for the rest of their team to read. Spreadsheet programs can also be used in conjunction with data capture tools such as via email surveys. 

How to create a data literacy project

If you are looking to build a data literacy project or programme, it is worth considering what skills you think are a priority for your employees, as well as the potential outcomes of any training that you offer them. It is also worth considering whether any online training courses might be of use here. This can include courses that help monitor employee learning trends and employ gamification to help information stick better in the minds of your employees.

A data literacy project might also include basic training courses in statistics so that employees can learn about the fundamentals behind analytics and data. If you’re looking to help strengthen your employees’ ability to communicate data insights effectively, you could combine this with a data visualisation course. It is a good idea to teach employees how to use any data intelligence tools that you are currently using as a business, and how to create reports with them.

You could also choose to invite data experts from inside or outside your company to provide talks on data literacy. It is helpful if they have good teaching skills and prior knowledge of the data collection and analytics tools that you are using as a business, and can walk your employees through their use in an easy-to-understand way.

One benefit of this strategy is that it means your employees are being taught data literacy by someone who is familiar with breaking down difficult ideas into more simple ones. This might make the subject more approachable for less confident employees.

Understanding common data terminology

One of the key steps in creating data-literate employees is helping them to understand some of the jargon surrounding data and data analytics. Some useful terms to know are:

• big data;
• artificial intelligence;
• machine learning;
• data cleaning;
• data mining.

Below, we will look at what a few of these terms mean. This can be useful not only to employees but also employers who are hoping to gain a better understanding of some basic data terms.

Big data

Big data is the term used to describe any large volumes of data that cannot easily be managed using standard data management methods. Businesses might be collecting this kind of data on a daily basis, and may be inundated by it. According to the industry expert Doug Laney, big data is characterised by the three Vs: volume, velocity and variety.

Volume relates to the sheer burden of big data on companies that collect it. Big data might flow from regular sources such as social media, customer transactions or smart devices. Storing this data can be a challenge without cloud storage or data lakes.

Velocity refers to the speed at which businesses must collect this data, which may come through from warehouse RFID tags or sensors.

Lastly, variety stands for the sheer diversity of data that modern companies must collect, which may come in the form of databases but also invoices via emails, or transaction information.

Artificial intelligence

Artificial intelligence (AI) refers broadly to the kind of intelligence that machines have, rather than people. AI often uses data sets to solve problems that are too complex or time-consuming for the human brain. Some examples of where you might find AI already being used in the workplace include online chatbots for customer service, recommendation algorithms, stock trading and the detection of fraud using fraud prevention software. 

Machine learning

Machine learning is a kind of artificial intelligence that is able to use data to learn and become more efficient at predicting outcomes over time. It does this using algorithms that help to decipher this data. Some machine learning tools can be supervised, while others can work unsupervised. Employees may come across machine learning tools in the workplace when using image recognition, data extraction, predictive analysis or language translation. 

Data cleaning

Data cleaning involves looking at a data set and making sure that the information in the set is correct, has not been corrupted, is formatted correctly and is not incomplete. This is important for employees to consider as they will often be gathering data from a wide range of sources.

One of the benefits of understanding how data cleaning works is that employees are less likely to input incorrect, mislabelled or duplicate data when combining multiple data sets (for instance into one spreadsheet). If your company handles large volumes of data, you might want to look at training employees in the use of data cleaning tools that can streamline the process.

Data mining

When your employees look through data sets in order to find trends, patterns or correlations, this is known as data mining. These trends and patterns in the data can then help your analytics team to make informed decisions by predicting outcomes. There are multiple uses for this process, such as helping to assess risks within the workplace, cutting back unnecessary costs, reducing staff turnover and improving relationships with customers. 

Although teaching data literacy to your employees might seem like a big task, it can form an integral part of your digital transformation and upskilling strategies. Data literacy involves teaching employees how to use data and communicate their findings effectively. When your employees – particularly your HR or analytics team – are confident in working with your business’s data, they are more likely to be able to use it successfully to predict outcomes and make better decisions.