Data literacy is an essential skill for all professionals

“In a world of more data, the companies with more data-literate people are the ones that are going to win”

– Miro Kazakoff, MIT Sloan senior lecturer.

Companies are amessing more data than ever before. While 20 years ago, you might have needed to be an Amazon or Google to build a data lake, the cloud now makes big data more accessible and affordable. To add to these ever growing mountains of data being collected, most businesses are also undergoing a digital transformation to become more data-driven – meaning they are increasingly relying on data for decision making.

How the data landscape is changing

One way businesses are changing is by adopting a data mesh architecture where data and analytics functions are no longer confined to a central data team. Instead, data ownership is delegated to individual business domains. Each domain is responsible for their own data pipelines and sharing their data as a product with other domains. For instance, the marketing domain will be responsible for data and analytics functions with respect to marketing data. That data would be made available to other domains to use and consume.

While there are many advantages to a data mesh (more on those here), this style of architecture brings a new age where data will become a ubiquitous commodity within all business domains. A harsh reality of this change means non-data professionals (i.e. those who don’t work with data on a regular basis) will have to work with data in one way or another. Most, if not all people will need to interpret, translate, and communicate with data sooner rather than later.

The problem – most people are not confident or competent when it comes to working with data. A recent data literacy survey of over 9,000 individuals across a variety of roles and industries found that only one in five workers were confident when working with data. In addition, nearly three quarters of employees (74%) reported feeling overwhelmed when working with data.

Data skills are poor across all organisation levels

This lack of data skills is not unique to employees at the lower end of the organisation chart. A recent study found that only half of C-suite executives (52%) reported full confidence in their data literacy skills, and concerningly, 45% say they make decisions based on their ‘gut feeling’ rather than data driven insights.

Given the changes in the data and analytics landscape and the ever increasing reliance on data for decision making, there is a need to improve non-data professionals’ data capabilities across all levels of business. At the very least, non-data professionals need to become data literate so they too can contribute to a data-driven culture.

What is data literacy? The ability to read, write and communicate with data

So what exactly does being data literate mean? In the simplest terms, data literacy refers to the ability to read, write and communicate with data. Even with a basic level of data literacy, professionals can leverage their domain expertise to make data driven decisions fairly quickly.

Organisations that thrive will not only embrace new technology, they will also invest in upskilling their employees. Just as professionals are expected to be computer literate today, there is a growing expectation that data literacy will be a skill just as common in the future.

Data literacy means different things for different roles

Being data literate does not mean being an expert in data and analytics as one might expect data scientists and analysts to be. Nor does it mean someone is a specialist in one niche area of data. Rather, those who are data literate understand just enough of the basic data concepts relevant to their day to day role – that is, they know what they need to know to get their job done. Some examples include:

  • A basic understanding of data hygiene and being able to spot common data issues including duplicate records, inconsistent data types, and missing or incomplete data
  • Interpret and communicate the output of basic data analysis, summary statistics and their implications including correlations, means, medians and understand the basics of concepts such as variance and standard deviations
  • Knowing the limitations and the conclusions one can draw from certain statistical tests such as correlations, t-tests and ANOVAs
  • Knowing when it is appropriate to sum or subtract proportions, recalculate bases, and limitations when dealing with statistics drawn from small sample sizes
  • Knowledge of common data storage and file formats such as csv files, json data structures or SQL databases
  • The ability to interpret common data visualisations such as histograms, boxplots and scatterplots 
  • Knowledge of relevant data analysis software, techniques and frameworks

Of course, the bullet points above should not be used as criteria to assess someone’s level of data literacy. If someone’s role does not require them working with statistical tests, then they shouldn’t need to learn about correlations or t-tests. Being data literate simply means knowing enough about data to allow a non-data professional to confidently navigate their way through data that arises in their role and add value to a data-driven culture.

A data literate workforce is good for businesses and employees

Research from Forrester found that organisations that invest in data literacy programs see benefits such as improved customer experience, better decision making, improving productivity, and greater employee satisfaction and retention. 

Employees are also likely to see benefits with improved data literacy with data literate employees likely to receive higher salaries. This study found that employers were willing to offer an average pay increase of 20% to employees who demonstrated competent data literacy skills. 

Clearly there are benefits for both employees and businesses with organisation wide data literacy. So, what can businesses do to improve their employees’ data literacy skills?

Six steps to consider when improving employees’ data literacy

Improving employees’ data literacy requires businesses to drive change across three key areas – people, processes and technology. As a start, businesses need to have a clear and well defined data strategy (more on that here). That strategy needs to include a plan to provide employees with the necessary data literacy training.

Ultimately, businesses are the ones responsible for ensuring their employees are data literate. How this is done will largely depend on a business’ operating context, requirements, and their current skills gaps. There are however six broad steps businesses should consider to help improve their employees’ data literacy.

  1. There needs to be a top-down commitment to building a data driven culture. Businesses with a strong data driven culture have leaders who are committed to ensuring data is at the heart of all decisions. This sets the expectation for all employees to embrace data in decision making and makes it clear why data literacy is important and valued within the business.
  2. Employees will need a mixture of both hard and soft data literacy skills. Depending on employees’ and the business needs, there will likely be a mix of what types of skills need to be learned or refined. Consider offering training in hard skills like statistics and coding, but also softer skills like language, the types of inferences that can be made from data (i.e. correlation does not equal causation), and critical thinking using data. Recognise that some employees will need to focus in one area rather than others depending on their role and responsibilities.
  3. Offer incentives to increase participation. Some employees will have a natural curiosity to learn more about data and how to use it. Infact, there is a strong desire for improving data literacy – 59% of employees globally report wanting to become more data literate. However, some employees may be less enthusiastic about embracing data as part of their role. They may have little interest in, or have fears about working with data which creates pushback. Offering incentives may be the nudge some people need to get started. Incentives can be anything from dedicated time at work to develop skills, to recognising achievements, to bonuses or time off.

  4. Develop semi-structured internal training programs. Improving data literacy doesn’t require businesses to develop training courses from scratch. There are plenty of online short courses available that businesses can leverage to upskill their staff. At the very least, businesses should have a good idea of what skills they need to develop (hello data strategy). Subscribing to a training platform and filtering course choices down to what is relevant to a businesses’ needs will limit choices and provide direction for employees who may feel overwhelmed and don’t know where to start.

  5. Create a centre of excellence. Start by identifying your fluent data speakers. These individuals, likely the analysts and data scientists, can serve as the experts. Think of this as an area where a data novice can direct data specific questions to the experts. Data experts can provide advice on best practices, help answer more complex problems, and provide leadership. Experts can also champion the idea of understanding data, which in turns helps to reinforce a data-driven culture. This also helps to foster continual learning and development when those refining their data literacy skills get stuck.

  6. Track the effectiveness of data literacy training with fit-for-purpose measures and metrics. After all, anything worth doing is worth measuring. When evaluating the effectiveness of any internal training, management should track metrics such as course completion rate, certification rate, employees per course, and course dwell time, just to name a few. However, metrics are only one side of the coin. Quantitative data can be further enriched by collecting qualitative feedback from those who participate in training. The combination of these two factors will provide valuable insights into what work does and does not work, and how training can be adjusted to support the best outcomes possible for employees. 

The approach to improving data literacy across the workforce will be different for every organisation – there is no cookie cutter template and nor a quick fix solution. Instead, companies need to take a measured approach and understand their current data landscape, skills, capabilities, and data maturity, and compare that to what they want to be. Only then will companies be able to take a step forward on the journey towards becoming more data-driven, finally being able to turn mountains of data into meaningful insights.

How to improve employee data literacy

Data is becoming an integral part of nearly every role. The ability to read, write, and speak with data is highly valued, yet short in supply with one in five employees reporting being confident when working with data. This skill gap is one of the main reasons why businesses struggle to make sense of their massive amounts of data; particularly in the face of the changing data and analytics landscape. Both business and employees can benefit from improving data literacy across the workforce, but businesses must ensure this is done in a measured and targeted manner – that is, as part of a broader data strategy. While there is no standardised approach to improving data literacy across an organisation, there are general high level considerations businesses should contemplate when improving their employees’ data literacy to ensure this is done successfully.