The ultimate guide to data literacy training

Data literacy fundamentals

Data literacy training can be a complex and daunting topic. If you’re starting a data literacy program, there’s a simple question to start with. What are the fundamentals for improving data literacy at your organisation?

Gartner’s definition of data literacy is “the ability to read, write, and communicate data in context.” “In context” is a key callout.

An organisation’s data culture is the ultimate context for its data. Even highly data-literate hires will need training to learn a common language of data before they can do their job well. They’ll also need to learn company-specific data governance and regulatory requirements.

Data literacy training

How do you develop data literacy skills? There is no one-size-fits-all answer to this question.

Job-specific upskilling will vary widely. The size and structure of your teams, the baseline level of data literacy, and your goals for improving specific data literacy competencies will all be factors shaping your training program.

If you’re starting a data literacy training project from the ground up, read our article about building a data literacy framework.

Benchmarking data literacy skills

Any time you start a journey, you need to start by establishing where you are on the map. How data literate are your employees right now? Even simple quizzes can tell you a lot about the current state of data literacy at your organisation. Some may be machine learning experts. Others may not even know what algorithms are — and for their jobs, they may not need to.

Understanding baseline knowledge is crucial to designing an effective data literacy program. Ask these questions to begin assessing data literacy:

• Can employees demonstrate basic data analysis and comprehension, such as judging averages and interpreting straightforward data visualisations?
• Can managers explain their teams’ outputs and build data-driven business cases?
• Can data scientists explain to non-experts the outputs of their machine learning algorithms?
• Does everyone use the same data standards and definitions?

Artificial intelligence can help provide a more objective understanding of data upskilling needs. Accenture shared in its Future Skills Pilot Report that “with a skills-first approach, in which AI is used to identify gaps and provide people with targeted training, skill gaps are filled faster and with 70% greater efficiency.”

Once you set benchmarks, you’ll know what skills gaps to address with data literacy training. The best training programs upskill workers by building on their existing skill sets.

Once your training program is underway, keep conducting assessments. A phased evaluation approach helps you measure and improve the effectiveness of your data literacy training modules.

Example: data literacy benchmarking questions

You’ll need to ask the right questions to identify skills gaps in your workforce, or in yourself. A data-literate professional would say yes to these questions about working with data:

• Are you comfortable formulating business-relevant questions that can be answered with data?
• Do you know how to access data sources to get data relevant to those questions
• Do you understand and follow your organisation’s data governance policies?
• Do you know how to evaluate the trustworthiness of your data?
• Can you make correlations and perform data analysis to find answers to your questions?
• Can you make your case with data storytelling and/or data visualisations (graphs, charts, and infographics)?
• Can you make data-driven decisions and create business value with data?
• Are you comfortable using data-related tools on the job? (For example, Microsoft Excel, Airtable, Tableau, or Looker)

Training for these data skills could look very different at different organisations. For example, finance or healthcare organisations typically need to provide data privacy courses. Regulatory compliance and data governance require data-related training across the company.

Data literacy courses

Are the data literacy trainings you need already available? Before investing heavily, research online classes and data science courses. What you need might exist as traditional college or university courses, MOOCs (Massive Open Online Courses), or other trainings.

Before putting a lot of resources into building your own programs or paying consultants to design a data literacy training program for you, look for online classes on data analytics and related data competencies. Don’t limit yourself to professional development resources like LinkedIn Learning, either. Consider all these options:

• Traditional college or university courses — many companies provide an education stipend for employees to enroll in higher education classes at a local or online institution
• Massively Open Online Courses — for example through Edx’s MOOC.org or Coursera
• Bootcamps — these include crash courses for those wanting to become data scientists and data analysts, but you can find intensive trainings for many other data topics

Third-party courses are also valuable for filling gaps in an in-house data literacy training program. A large company may have dozens or hundreds of employees who would benefit from training on business analytics and Excel. Only a handful would benefit from training in data storytelling or data visualisation. While those learners would benefit from domain-specific data literacy training, it wouldn’t be cost-effective to create those specific trainings yourself. Online courses make it easy to provide individualised training to individuals who need it.

Here are some of the most popular self-paced online courses for general data literacy:

• Data Fluency: Exploring and Describing Data on LinkedIn Learning
• The Data Literacy Course: Learn How to Work With Data on Udemy
• Data — What it is, What We Can Do With It from Johns Hopkins University on Coursera

Make sure any self-paced courses you choose are not just well-reviewed, but also regularly updated.

Data literacy activities

The big data era has seen a proliferation of tools to train data users. MIT Sloan researchers point out that it can be harder to find tools and activities to train data learners. For example, a simple online search will surface dozens of tools and trainings for analytics tools like Tableau. But to use the tool successfully, users need to understand the fundamentals of business intelligence.

To drive successful data literacy initiatives, data-driven companies must teach employees a common language for data. It’s also valuable to train data users to participate in data management, for example, with data stewardship tools. This keeps decision-makers involved in maintaining the quality and trustworthiness of their own datasets. Data sharers who both produce and use data struggle with data less and understand data better. 

When it comes time to teach groups about data,” say Gartner, “make sure it’s in a fun and open environment, and think outside the box for training ideas.” Consider taking opportunities to gamify data literacy learning. For example, start your next all-hands meeting with a trivia game related to data governance.

Crowdsourced data literacy activities

Lunch and learns are a great way to offer a la carte training in a fun, casual environment. These informal trainings used to be limited to small, in-office brown bag sessions. Now, it’s easy for most companies to share those meetings as video calls. More employees can attend, and you can record sessions to reuse as on-demand trainings.

Imagine coworkers teaching each other about data in sessions like these:

• Mastering pivot tables in Microsoft Excel
• Data visualisation techniques to try in Tableau
• Data dictionary walkthrough: What’s in it and why it matters
• Understanding data distributions: A primer for non-analysts
• Data storytelling tips from the Marketing team

Data literacy leadership training

When you’re tasked with finding or designing data literacy training modules, you might wish you had a data literacy leadership training module yourself. Fortunately, Gartner has a data literacy resource for data and analytics leaders. Read Gartner’s guide to learn even more about best practises for data literacy training.

Data literacy workshops

While data literacy courses will provide data literacy skills, they won’t be tailored to your organization. Also, not everyone benefits from sitting in front of a machine, learning alone.

Online and offline workshops are a powerful way to provide data literacy training for a whole team. For example, Qlik offers data literacy workshops to improve corporate performance.

Group activities like workshops help employees bond while learning. Social learners will particularly benefit from workshops. If your team is competitive, consider workshops that gamify data science or data-driven decision-making. 

Data literacy solutions

To put data literacy training into practice, you’ll need to give decision makers and business users access to enterprise data and data analytics tools. That means finding and investing in data literacy solutions.

Maintaining a robust data culture will require a complete data management tool. That’s how you provide all employees with flexible, governed access to enterprise data. When data users have data visibility and data literacy, they’ll know if their data is trustworthy.

Learn more about data literacy platforms in our article on data literacy tools.

Data literacy training examples

Watch this video to see demos of how Talendians use Talend’s own products. Or if you prefer, read all about Talend’s data literacy project on the Talend blog.

Talend provides complete, flexible, and trusted modern data management solutions. If you’re interested in building or scaling your data literacy program, get advice from a Talend expert today.