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Data literacy training requires a dual approach
Data literacy training is a flexible learning process. Organizations can tailor training for their teams, but individuals can find educational resources to better their skills.
Data literacy is both an individual and organizational undertaking. Individuals can develop skills on their own, and organizations can provide curated training that self-teaching resources can't provide.
In most organizations, data literacy first appears as an individual skill. IT specialists or finance analysts may have varying levels of literacy. Marketing teams might have people who studied statistical analysis for campaigns. As organizations grow and data use matures, it's not enough to have individual points of expertise, however. It takes a group effort to develop teams and business units that can think together analytically and communicate with data. As the role of AI grows at every level of an organization, competence in working with and communicating with data is more necessary.
Developing expertise in specific products can be straightforward. Most software vendors offer training on their platforms. Developing data literacy in a more general sense -- thinking analytically and communicating effectively -- requires different approaches depending on whether it's an organization-wide or individual initiative.
Organizational training
In an organization-wide approach, the employer prepares a structured learning path that ensures a comprehensive and consistent experience for all employees. Employees follow the same program, which ensures that they align with their organization's strategic and tactical goals. They benefit from access to high-quality resources and tools that might not be available to individuals.
Organizations can curate training resources to offer the best training for each person's role. When an employer appreciates data literacy as a competency and has clear goals for data practice, structured training helps deliver the strategy -- from basic reporting to the latest in AI.
Workshops
One key component of organizational training is workshops, which should be highly interactive and provide hands-on experience with data tools and techniques. IT leaders can conduct workshops in-house, enabling employees to work on real-world problems and enhance their understanding and skills. Another option is to use external trainers, who can bring fresh perspectives and knowledge of new tech developments that are not yet in production, such as the latest uses of generative AI.
In either approach, employees learn together, build cohesion, collaborate and connect within their teams and across organizational boundaries. Workshops can develop a core competency and a shared language for communicating about data issues for data literacy.
Consultants
One of the downsides of an organizational approach is that a one-size-fits-all data literacy training might not address individuals' unique needs and learning paces. For some organizations, particularly smaller companies, developing and delivering programs can be a significant investment of time and resources, even if it only involves curating existing external content.
To that end, many consulting firms can deliver organizational training. A common option is using a well-recognized expert who can bring new insights or a local consultancy that provides flexible, cost-effective training. Some larger consulting firms might develop an entire strategic package tailored to an organization's needs, ranging from executive-level seminars to subject-specific workshops.
Individual training
Online courses, webinars, off-site training at conferences and books can all be practical learning methods for individuals. Individuals need self-discipline for self-learning formats, but the flexibility of their delivery can improve motivation and engagement. They can learn at their own pace and focus on topics they're interested in.
Individual training is especially appropriate in cases where an employee needs to develop a specific competence, such as data visualization, perhaps to a high level. Employees who learn individually often train or mentor others with their new skills. For organizations, individual training can potentially reduce the need to deliver internal training programs.
Online courses
Individuals can access online courses and tutorials for data literacy, data visualization and data analysis. Massive Open Online Courses (MOOCs) provide access to high-quality education from renowned institutions. Certificate programs from software vendors and educational institutions offer structured learning and formal recognition of skills that employees value as an investment in their careers.
Online courses from specialist companies such as Coursera and EdX are excellent career resources. Their programs often include MOOCs from MIT and UC Berkeley, although many institutions also host courses.
Webinars
Online webinars usually feature keynote-style presentations that provide insight into industry trends and best practices without delivering highly structured training content. For example, webinars are good resources for learning about AI trends.
Books
Don't overlook books as a resource for data literacy training. A well-stocked company bookshelf is an asset. Reading books and e-books on data literacy can help individuals deepen their understanding of specific topics. Employers can curate a diverse selection of recommended reads. For example, include books covering different aspects of data literacy, such as data analysis, visualization, machine learning and statistics.
Books must have offerings for all skill levels, from beginners to advanced practitioners. Update the bookshelf with new publications to keep up with trends. Encourage employees to suggest books or donate their copies, which develops a sense of ownership and engagement with the bookshelf.
Conferences
Offsite training at conferences provides a unique set of advantages. Taking employees out of the office environment reduces workday distractions so that they can immerse themselves in the learning process. Conferences also bring together professionals from various organizations and industries to network with peers and experts, share experiences and gain insights into other companies' best practices.
Attending conferences can be a reward or incentive for employees. It shows the organization values their professional development and is willing to invest in their growth.
Conferences can be effective training venues. Most prominent software vendors hold events showcasing their latest announcements, training and certifications for their user community.
Experiential learning
Where organizations already have proficient and well-provisioned data teams, hands-on experience with accurate data in production can be very effective for new hires or for the personal development of existing employees.
Too often trivialized as on-the-job training, an experiential approach can significantly improve skill understanding and retention through practical application. It provides immediate feedback and learning opportunities with real-world scenarios, making the skills acquired highly relevant to job responsibilities. For example, employees can learn to analyze company data to solve business problems, develop insights or improve decision-making processes.
Employees must be aware of ethical and privacy concerns when using data in production. The risk of errors is higher because mistakes made during experimentation can have real-world consequences. Even when working with correct data and real scenarios, consider creating isolated sandboxes for employees in training, where a specialist can review their work before integrating it into live operations.
The most effective training strategy should combine elements from organizational, individual and experiential learning to create a well-rounded data literacy development plan. Data literacy training is not a one-and-done endeavor. Individuals and organizations should commit to continuous learning and skills development as data technologies and best practices evolve.
Investing in data literacy training is not just about imparting technical proficiency. Prioritizing data literacy as a core competency empowers employees to think critically, ask the right questions and communicate effectively with data.
Donald Farmer is principal of TreeHive Strategy and advises software vendors, enterprises and investors on data and advanced analytics strategies. He has worked on some of the leading data technologies in the market and previously led design and innovation teams at Microsoft and Qlik.
