What are some effective strategies for fostering a data culture and literacy in your organization or team?

A data goals and -vision helps to communicate what you want to achieve with data in your organisation. To link this with data literacy in your organisation you need to make clear what level of literacy is needed for each role/function in your organisation and how this level of literacy links to the organisation reaching its data goal/vision.

Besides formal and informal ways of data training focussing to make people more data literate, general awareness campaigns on the importance of data can increase the level of literacy across the organisation.

Data Training Programs: Formal courses, workshops, and online training programs that teach data-related skills such as data analysis, data science, machine learning, and data engineering. Data Learning Resources: Books, articles, blogs, tutorials, and documentation that provide information and guidance on data-related topics, tools, and techniques. Data Tools and Software: Software applications, platforms, and tools used for data collection, storage, analysis, visualization, and reporting, such as databases, data analytics software, and data visualization tools. Data Communities: Online forums, discussion groups, and communities where individuals

Identify and train passionate individuals in data skills. Establish cross-functional data communities for collaboration. Promote data literacy with accessible resources. Recognize contributions, support projects, and measure impact to foster a data-driven culture.

Policies and standards form the foundation on how data needs to be handled but can be often abstract for individual employees. To allow for an effective increase of data literacy across the organisation it is often useful to translate the content of these documents into practical examples that relate to the (daily) work of roles in an organisation.

The key to data storytelling is remembering that less is more: you do not need to show all of your information in your graph to prove you did the work - rather, you should only point towards the insights you've uncovered within the data. This is different than manipulating the data! Make sure your insights are data-driven and inherent in the data set, rather than trying to use the data to tell a specific story.

Don't make data a technology problem. Extracting value from data and producing insights is often seen as a task for tech teams. In my view, one reason for that is the data language and terminology that could feel cryptic and techy to the average data user and consumer. Data literacy also means finding common ground on speaking the same language of data across teams, i.e. business, change teams, project teams, tech teams, and data teams.

Improving the data literacy across the organisation is not a one size fits all thing. Different roles/jobs within an organisation need different levels of data literacy to be effective at their job and help the organisation reach its goals. A data literacy initiative therefore needs to keep these different groups into account when planning activities to ensure the level of data literacy meets the level requirements to be effective at a job/role.