Data literacy framework: a guide to creating an effective data literacy programme
Data literacy framework definition
If you’re researching how to pilot a data literacy project, you already recognise the importance of data literacy to your organisation. But before you can kick off a data literacy programme, you’ll need to build your data literacy framework.
We define a data literacy framework as a detailed vision and plan for establishing data literacy.
To add more detail, a data literacy framework does these things:
- Documents what data literacy skills are relevant to your industry
- Defines what data literacy competencies your employees need to use data effectively
- Defines the end goals for your project
Every data literacy framework will be unique, because data literacy is not a one-size-fits-all skill set. A data literacy programme that would be successful at one company won’t necessarily make sense anywhere else.
Data literacy research
Improving the data literacy of employees benefits a company’s bottom line. For more than a decade, research has shown that making business decisions based on data and business analytics — in other words, data-driven decision-making — measurably improves output and productivity. Learn more in our article about the benefits of data literacy in business.
Data literacy initiative
Since research shows that data-literate employees accomplish more, data literacy should be a goal for any modern organisation. In Talend’s 2022 Data Health Barometer, 65% of companies reported having started a data literacy programme.
If your company is in the 35% that has not started a programme yet, there’s no time like the present to make sure you don’t fall behind.
Data literacy framework benefits
Creating your data literacy framework is a transformational process. On a personal level, building the framework will help you understand where and how data is used across your organisation, and define your own role in building data literacy.
Data literacy frameworks help enterprises get deliberate about creating a data culture. Setting the organisational vision for data fluency forces an enterprise to define roles and responsibilities which are sometimes unclear — especially if your organisation doesn’t have a Chief Data Officer role.
While building the framework, you’ll probably find opportunities to integrate datasets and consolidate expensive data solutions. Simplifying your data landscape will break down data silos, improve communication, and cut costs.
The benefit to employees is enormous. The framework does more than set the stage for data literacy training. Your vision will also highlight opportunities to manage data better, consolidate tools, improve data visibility, and expand governed access to trusted data.
A clear, realistic data literacy framework establishes the path to an effective data culture. Ultimately, it will become much easier for both IT and business users to do their jobs.
How to design an effective data literacy framework
Level of data literacy
The first step to design a well-structured framework is to assess learners’ current level of data literacy. You can administer an assessment to determine the baseline metric for data literacy skills. Use data from this initial assessment to identify skills gaps and guide your data literacy training goals.
Remember that data literacy looks different for everyone. For data analytics roles, data literacy might require deep knowledge of algorithms and machine learning. For a graphic designer, data visualisation would be more important.
What are the three steps of data literacy?
The first step to data literacy isn’t necessarily data analysis. An understanding of data first relies on the ability to share information with words, numbers, graphics, and knowing how to ask the right questions. Understanding types of data, where data lives, and all the other specifics of data science are secondary to those foundational competencies.
Consider these three levels of data literacy to build data literacy skills at your organisation. With the third step, you can create a data culture across the entire organisation.
- Critical thinking and communication skills — knowing what questions to ask, how to exchange information effectively, and how to use data to create business value
- Building and maintaining dashboards — analysing and organising data from relevant data sources in order to answer the questions you’ve decided to ask
- Data ownership within a data culture (data stewardship) — managing datasets to maintain data quality, break down data silos, and ensure everyone is on the same page
Piloting a data literacy project
While data literacy should be a major, high-priority project for every organisation, it’s fine to start small. For example, you could pilot a data literacy programme in a single department. Even then, it’s important to take the time to research and develop your data literacy framework.
The data literacy framework is Step 1 of establishing a data literacy programme at your organisation. What are the key elements of a data literacy framework?
- Roles/responsibilities of stakeholders in the initiative
- Goals for upskilling employees
- Data-related tools needed
Data literacy example programme
Even a data management software company needs a data literacy programme. We’re not all data scientists here! Talend Senior Data Governance Analyst Bethany Gripp shares the strategy for her successful data literacy pilot.
You can build your own data literacy framework based on her example by asking and answering these questions:
- STEP 1: Planning and vision
- Who owns the data literacy programme?
- What are the roles and responsibilities of everyone involved?
- What are the data literacy programme’s end goals?
- What tools do we need to enable our employees?
- STEP 2: Communication
- How are we sharing about this programme internally to build buy-in?
- STEP 3: Increasing access
- Who should we expand data access to?
- How do we protect data that should be kept private?
- STEP 4: Enablement
- How do we train individual employees to use new, data-related tools?
- How do we create a common language for data across the organisation?
- STEP 5: Sustaining adoption via building a data culture
- How do we provide continued learning opportunities?
- How do we promote the value of data and encourage data-driven decisions?
- STEP 6: Evaluating and iterating
- How is our data literacy programme performing?
- Has use of data increased across project participants?
- Have training modules measurably improved understanding of data?
- Do decision-makers report increased use of data to make better decisions?
- What roadblocks have we run into, and how can we improve the programme?
At Talend, we use our own tools for metadata management, data governance, and data stewardship. Get help establishing your data literacy programme: book a consultation with a Talend expert.
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