How to Launch a Successful Data Literacy Program

Data literacy is becoming a new core skillset for modern digital  organizations. It makes sense that in a world with more data , companies with a more data-literate workforce will come out ahead.

Data literacy — the ability to work with, analyze, and communicate with data — helps people understand, interpret and act on data within a business context to optimize business outcomes.

A data-literate workforce is better prepared to gain insights from data — enabling domain experts to solve the problems they are closest to on their own, avoiding bottlenecks that occur when non-technical business users are forced to rely on IT and data experts.

In this guide, you’ll learn the business benefits of building a data literacy program, components that make up a successful initiative, and real-world success stories that will inspire you with innovative upskilling strategies.

The Business Case for Investing in Data Literacy

Data literacy does more than improve employee skillsets, it also positively correlates to corporate performance. According to The Data Literacy Project, improving data literacy leads to an increase in enterprise value of $320 to $534 million over organizations with lower data literacy.

The benefits of a data-literate workforce extend beyond bottom line returns for the company — they include:

• Increased productivity – Improving the ability for all employees to problem solve with data can accelerate processes, speed decision-making, and mitigate risks.
• Employee empowerment and satisfaction – Data literate employees are more invested in company success and report higher levels of job satisfaction.
• Enhanced collaboration – Data literacy encourages a culture of data-driven decision-making across teams and a shared understanding of business objectives.
• Innovation and growth – Workers empowered with data insights are more likely to identify hidden opportunities, explore new ideas, and share insights.

Launching a Successful Data Literacy Program

Upskilling employees in data requires more than ad hoc training and education sessions. A successful initiative requires a focused program with defined goals and outcomes.

We’ve compiled the key components of a data literacy program that leads to long term success for employees and the organization.

1. Conduct a Skills Gap Analysis

To determine the training, resources, and skills you need to implement a data literacy program, you’ll need to assess the gap between the skills your employees have and what they need to succeed in a data-literate environment.

The skills gap analysis will help you determine:

• Which skills your organization needs to meet your goals
• The types of training you need to provide
• Who should be required to learn each skill

Data literacy is comprised of many different aspects, and ranges from basic data interpretation and statistical analysis to understanding terminology and explaining results.

For example, you may need to ensure everyone involved in your data literacy program understands basic data interpretation and statistical analysis, but only need to provide machine learning and data science training to the analysts who work on forecasting.

Knowing your goals and objectives will help you establish what needs to be included in your data literacy program. After you run your skills gap analysis, review it alongside your objectives, goals, and KPIs, and use this opportunity to amend and update them as needed.

2. Create a Data Literacy Task Force

Your task force should include a good mix of internal champions and advocates who can keep your program moving forward.

As you build the team, ask yourself, who would be the best people in the organization to answer specific questions related to this program?

Here’s how to pick a team that encourages and energizes your organization along this endeavor:

Select a diverse team of experts and stakeholders, including team advocates

Remember, a mix of a top-down and bottom-up approach, including employees such as an analyst who can advocate for others, will help ensure your program is successful.

Define roles and responsibilities within the task force

Include roles that oversee implementation, including analytics software, courses and learning materials, and mentors.

Determine where the funding for initiatives will come from

While some resources will require spending (such as software), others might be free, such as courses and communities from vendors

Identify the levels of literacy each individual/team/department needs

It’s important to remember the needs of those who will use the information in their roles. There will be people on your team, especially in leadership, who will use the information they receive to make decisions.

While they won’t need to learn data science or machine learning, they will need to understand specific terms and concepts to use the information to make decisions. So, ensure your data literacy task force includes someone who can focus on training and providing resources for communicating data to other teams.

3. Design a Comprehensive Data Literacy Program

One of the main reasons data literacy programs fail is because of a one-size-fits-all approach. As you create your data literacy curriculum, strive to create a tiered curriculum based on individual and group needs.

It’s important to remember your teams’ differences in experience and knowledge. Additionally, if you provide analytics training for your data workers, you should also consider providing training to help them communicate and present ideas and concepts.

Here’s a list of other educational aspects you should cover as you develop a curriculum:

• Interpreting results and analytical terms
• Explaining concepts to non-technical and technical audiences
• Visualizing insights for reports and presentations
• Determining when and when not to apply machine learning and data science to decision making

4. Choose Your Training Methods

Like selecting a curriculum, choosing a training method and tools shouldn’t be one-size-fits-all. For your data literacy program to have the best chance at success, it’s crucial you select suitable training methods, formats, and tools for your teams.

The goal is to empower and energize people to adopt the program and learn. To do that, people need formats that match their learning styles.

Preferred learning format options include:

• Self-paced
• Scheduled or timed
• Individualized paths
• Group or community paths
• Reading lessons and resources
• Hands-on learning with products

As you consider the learning styles, you should also do the following:

• Compare in-person, online, and blended learning approaches
• Evaluate various data literacy training platforms and tools
• Customize training materials for different roles and departments
• Establish a timeline and milestones for the program
• Communicate the program’s goals and expectations to employees
• Ensure ongoing support and resources for employees during the program

No matter your learning format and setup, you must account for the current workload of your organization. Your organization may already be stretched thin, and adding a new learning program to their workload may cause friction and fatigue.

5. Integrate Easy-to-Use Analytics Tools

Once professionals in your organization are educated on how to work with data, they can move on to the real superpower — applying data insights to day-to-day decision-making with analytics.

One of the most effective ways to enable insights for non-technical users is with an easy-to-use, no-code analytics tool. Look for self-service, automated tools that enable knowledge workers to create their own workflows and understand data patterns and insights.

Solutions that also provide automated reporting services, such as dashboards, graphs, and charts, and the ability to automatically schedule and send the reports, are a plus.

6. Measure the Impact of Your Data Literacy Program

Whether you meet or surpass the business goals aligned with your data literacy program, you’ll still want other ways to measure your program’s impact and assess its effectiveness.

It’s a good idea to establish KPIs that help you evaluate your program’s overall contribution to your organization. Some KPIs can include:

• Percent of upskilled employees
• Project completion time
• Percent of data sources available (before and after)
• Percent of decisions made using advanced analytics (before and after)
• Efficiency increases, cost savings, and revenue generated (before and after)

If you aren’t tracking these metrics now, it may be hard to establish a baseline you can use to compare with later. In that case, estimates are fine but look to establish a baseline as soon as possible.

The key here is to demonstrate the value of your efforts, plus the progress – and hopefully success!

Data Literacy Success Stories

Now that you’ve learned best practices for developing a data literacy program and methods for evaluating success, let’s look at what success looks like with real-world examples.

Westrock

Westrock is a global leader in sustainable packaging, with 500+ production facilities operating in 40 countries worldwide.

Westrock piloted the use of Alteryx, an automated analytics tool, in the finance department before scaling data literacy initiatives across the organization.

Analytics and innovation leaders at Westrock used a range of training methods and styles mentioned earlier in this guide to upskill employees, including:

• Data analytics roadshows to build excitement about automated analytics capabilities and features
• Core training and certification sessions within business functions
• Workshops with Alteryx Customer Success Managers
• Gamified analytics challenges among colleagues

Listen to this podcast with Jay Harter, Senior Manager of Self-Service Analytics at Westrock and LaShell Estes, Senior Manager of Finance Innovation, to get inspired and learn more about how they implemented a successful data literacy program.

Jones Lang LaSalle  

The BI and Performance team at Jones Lang LaSalle (JLL), a global commercial real estate company, chose Alteryx to automate analytics and accelerate digital transformation.

To improve adoption of the no-code analytics platform across the organization, the team curated a gamification program called the “Alteryx Adventure.”

Read more about how gamification doubled the usage of Alteryx by global teams — contributing to an increase in employee engagement, improved productivity, and a reduction in risk.

Bank of America

Bank of America created an Alteryx User Group to support the use of automated analytics and improve operational performance across the organization. The user group includes centralized resources that make it easier for business users to upskill in analytics and automate manual financial processes — a primary goal of the company’s Operational Excellence Program.

Watch this video with David Hardister, Automation Lead at Bank of America and internal owner of the Alteryx User Group to learn more.

Conclusion

Launching a successful data literacy program isn’t easy, but it can lead to valuable business outcomes, greater process efficiency, and employee satisfaction.

You can read more here about how to succeed with company-wide data literacy.

If you’d like your team to use their new data skills in meaningful ways instead of menial and time-consuming tasks, check out The Analytics Leader’s Guide to Automating Business Processes.

You and your team can also give Alteryx a try for free! Start using the no-code, drag-and-drop solution today to see how easy it is to go from data to decisions in minutes.  Start 30-day free trial.