What is Data Literacy?

Data Literacy is the ability to read, write and communicate data in context, including an understanding of data sources and constructs, analytical methods and techniques applied, and the ability to describe the use case, application and resulting value.

Data Literacy is the ability to,

• Read data, which means understanding what data is and the aspects of the world it represents. Is it raw or polished and visualised?
• Work with data, including creating, acquiring, cleaning, and managing it. Each data user uses a different tool to achieve the results needed for the organisation.
• Analysing data, requires some technical, mathematical and statistical skills. Analysing data means taking raw data and creating insights from data. Basically, Analysis of data is the crux of moving forward with data-informed insights and augmented intelligence.
• Argue with data, which means persuading the audience by using data and statistics from Business Intelligence to Augmented Intelligence—gathering and analysing data, using human intelligence to improve data and then arguing with data. Source

Why is it so important?

Data literacy is integral to developing Data Culture in any organisation. It is the very first step you need to take to help your company flourish, especially in this technology-driven era where data is being increasingly used to make decisions for driving growth.

Companies with a higher data literacy rate can leverage data to better understand their customers’ needs and product usage. This insight allows them to develop improved products and create a delightful customer experience. It then drives increased revenue and accelerates business growth.  

Prior to me, some of our team have also written a  blog about Data Literacy and there’s some great material and free course in there. You can check out Bronwyn’s blog here and here

Data Literacy is different from person to person,

• Business Users are employees who are most likely to ignore the data available to them. They will make decisions based on their own understanding or intuition.
• Data Literates have a better understanding of the data. However, they may have trouble with determining which scenarios have better outcomes.
• Data Analysts enable businesses to maximise the value of their data assets through visualisation and reporting tools such as Microsoft Power BI or Qlik Sense.
• Data Scientists perform advanced analytics to extract value from data. Their work can vary from descriptive analytics to predictive analytics. Descriptive analytics evaluate data through a process known as exploratory data analysis. Predictive analytics are used in machine learning to apply modelling techniques that can detect anomalies or patterns.  
• Data Engineers provision and set up data platform technologies that are on-premises and in the cloud. They manage and secure the flow of structured and unstructured data from multiple sources. The data platforms they use can include relational databases, nonrelational databases, data streams, and file stores. Data engineers also ensure that data is securely and seamlessly integrated across data services.
• Data-driven Executives can interpret the results their teams present and make positive decisions.

It is essential to understand that developing Data Literacy in an organisation does not necessarily mean that every employee needs to be a data scientist. Data Literacy calls for all organisation members to have a certain level of understanding of data depending upon their job role and the decisions they need to make.

How to assess data literacy at your organisation?

Data and analytics leaders are responsible for creating the narrative for data literacy and highlighting the business value to be gained.

Begin assessing data literacy at your organisation with these questions:

• How many people in your business can interpret straightforward statistical operations such as correlations or judge averages?
• How many managers can construct a business case based on concrete, accurate and relevant numbers?
• How many managers can explain the output of their systems or processes?
• How many data scientists can explain the output of their machine learning algorithms?
• How many of your customers can truly appreciate and internalise the essence of the data you share with them?
• How many executives can understand and interpret reports and dashboards?

How to develop Data Literacy in your organisation?

• Defining Data Literacy in the organisation: The first thing to do is to define what data literacy means for your company. You will need to ascertain the level of data literacy of each of your employees.
• Planning a simple learning solution: Develop a simple learning plan for your employees to improve data literacy in the organisation.
• Data Culture Assessment: Now that you have information about all your employees, you will need to evaluate the data to understand the organisation’s data literacy. Get a clear picture to understand the changes you need to achieve the goal.
• Communication and Execution: As a Data Literacy project is essentially a change-management process, creating and executing a cohesive communication strategy is important. The company’s DNA should be incorporated when doing this step.
• Evaluation and Improvement: As with any project, it is necessary to evaluate the progress and gather monthly or quarterly reports to tweak the project to achieve improved data literacy. In order to build an ethical culture around data literacy at your organisation, it’s imperative to understand all aspects of Data Literacy so that it can be integrated and implemented appropriately. Source

“Data is the new currency; it’s the language of the business. We need to be able to speak that. In a world of more data, the companies with more data-literate people are the ones that are going to win.”

I hope you enjoyed reading my blog. Feel free to reach out to us. We do cool sh!t with data.

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