How to Improve Data Literacy and Fluency with 5 Key Tips

Why Should You Develop Data Literacy and Fluency?

The global volume of data has been increasing. This is specifically attributed to the popularity of mobile devices and the Internet of Things (IoT) technology. To capture and make full use of this data, your business should invest in training and development programs to improve data literacy and fluency.

Once employees and even leaders develop these skills, they can make accurate decisions based on data rather than guesswork. Instead of pure observations, they can understand and analyze data to spot trends, predict potential risks, and explore untapped growth opportunities.

For example, you realize customers often leave complaints about a specific product due to its relevant post-sales customer service. This finding is not acquired by gut feelings or anecdotal evidence. Instead, you perform sentiment analysis of customer feedback data to identify certain issues and the root causes. This allows for making accurate, targeted solutions (e.g., retraining customer service agents or changing the return policy).

With data literacy and fluency, you can better meet customer needs, streamline workflows for better productivity, and gain a significant competitive edge.

Challenges to Consider

In reality, deploying programs to enhance data literacy and fluency is not as simple as it might seem. You can’t enter the office and simply announce that everyone will now use data for everyday tasks, hand out a stack of documents, and expect them to magically become data literate or fluent. There are plenty of realistic challenges that hinder any business from improving data literacy and fluency across teams effectively:

Limited Investment

Data literacy initiatives are not the only thing you need to focus on in your company. In fact, management must distribute resources (e.g., funding, time, and human) to different initiatives – like launching new products or optimizing operations. As these resources are finite, data literacy efforts can struggle to achieve essential support and focus.

Further, many organizations now invest heavily in cutting-edge tools and tech stacks for data analytics. However, they often overlook investments in employees who finally use these tools. Without proper training, these employees can misinterpret data, fail to derive meaningful insights, and even feel frustrated by the complexity of these tools. All these things result in their refusal and resistance to data usage.

Misalignment in Data Literacy Efforts

Various companies may fail to align data literacy and fluency efforts with their overall business objectives. Data literacy and fluency are not purely about reading, understanding, and using data. They empower employees to utilize data in a way that directly supports your company’s strategy and mission. If data literacy efforts are not designed with business strategies and values in mind, you may create generic or irrelevant training programs. These programs not only waste resources but also hardly solve your specific problems.

Moreover, each person in your company has different roles and responsibilities. This requires them to develop distinct skill sets and proficiency levels in data analytics. If you implement a “one-size-fits-all” training program, this data effort fails to tackle the unique demands of different employees.

Additionally, many organizations don’t consider the rapid pace of tech advancements when developing their data literacy programs. If your data effort is primarily about educating employees on how to use specific tools, failure to keep up with the latest technologies can make your training obsolete. This can render your workforce ill-equipped to survive the ever-changing tech era.

Not Having a Data Learning Mindset

Sustaining data literacy improvement requires a mindset shift among employees. In other words, companies need to design a positive and supportive environment for employees to apply their data skills effectively. However, many organizations fail to do so.

In particular, employees are put in a situation where they have to practice and use their skills for high-stakes tasks. Also, there are no tangible incentives (like recognition or bonuses) for them to proactively take part in training programs. Moreover, a company’s existing culture and business processes don’t support the wide adoption of new technologies or data-driven methods.

All these things prevent employees from developing a data-learning mindset. In other words, they only consider learning as a moment, not an ongoing process or part of a company’s culture. As a result, data usage is not a high priority for them and they still prefer doing their daily tasks in an old way.

5 Tips to Improve Data Literacy and Fluency Effectively

Facing these challenges, you need a well-structured strategy that aligns with your overall business goals while bringing the most impactful outcomes across departments. To improve data literacy and fluency effectively, you should consider eight important tips as follows:

Tip 1: Set SMART goals for short and long terms

Establishing immediate and future goals is essential to guide your efforts and develop a training program in alignment with your company’s mission. These goals should be Specific, Measurable, Achievable, Relevant, and Time-Bound (SMART). For example, your goal for this month can be increasing the percentage of employees who may confidently explain data visualizations by X% in the Y timeframe.

So, how can you set appropriate goals? Consider the following factors:

Align data literacy efforts with business goals. The initiatives need to be closely linked with your company’s strategic objectives. Ask what your business wants to achieve with better data literacy and how to measure the success of these initiatives.

Identify skill gaps. Evaluate the existing skills and fluency level of your employees through surveys, quizzes, or trusted assessment sources (like Aryng or The Data Literacy Project). Then, pinpoint what skills they need to meet business goals or how data fluent they should be to meet current and future demands.

Consider your available resources. Assess your current budget, time, and personnel resources for data literacy initiatives.

Tip 2: Build a data-first culture

A data-first culture is where data is a cornerstone of decision-making and other business tasks. This culture encourages employees to experiment with new solutions backed by data, streamlines workflows, and boosts productivity. There’s a lot to do if your business wants to build a data-first culture successfully.

Create a shared glossary of data terms and definitions. Synthesize common data terms and definitions (e.g., descriptive analysis, data sources, or metrics) to help everyone understand and use the same language when working with data. Also, avoid jargon or highly technical terms. Instead, leverage clear language that all employees can understand, regardless of their technical backgrounds.

Identify data owners and users. Define who owns specific datasets and has the authority to use them within your business. This ensures everyone is responsible for the quality, security, and ethical usage of data. For this reason, your business can manage data effectively and mitigate the risk of data breaches.

Foster a learning mindset. Gaining data skills shouldn’t be a moment. Instead, it should become part of daily work, from using new techs and practicing new skills to adopting them frequently. To encourage a data-learning mindset among employees, you should:
- Listen to employee needs by understanding what’s working in their current processes, what problems they’re encountering, and what they need to do their jobs better.
- Give employees the right motivation to embrace learning. These motivations can be external (e.g., bonuses or performance reviews) or internal (e.g., easier access to data).
- Celebrate and share real-world successes of applying data skills to inspire others to follow.

Acquire leadership support. The cultural shift needs to happen from the top to bottom levels. Accordingly, management and leaders must set an example by leveraging data in their decision-making.

Tip 3: Choose the right training methods

Choosing the right training methods is crucial to improve data literacy and fluency effectively. A one-size-fits-all approach never works well for everyone. It’s because employees at different roles and levels have distinct needs and skill gaps. For example, sales reps may need foundational skills while data analysts require advanced training. So, designing customized training programs for varying groups makes data learning relevant and effective.

Below are some popular training methods:

1. Workshops & Seminars

They bring teams together to discuss, practice, and apply data skills in a collaborative environment. Workshops and seminars are often ideal for teams that need a structured setting to learn basic data skills or collaborate on common issues. If you’re looking for real-time interaction with expert guidance and hands-on activities, these training methods are good options.

2. Online Courses & Tutorials

Today, you can access a wide range of online courses and tutorials worldwide. These courses allow you to learn at your own pace and fluency levels, approach diverse topics, and access a pool of global experts. Further, they suit those who prefer self-paced learning and flexible time.

Below are some courses and tutorials you may consider:

For Data Literacy: Courses are often about understanding data, reading dashboards, and telling data stories.

Data Literacy: Exploring and Visualizing Data Specialization by SAS: This four-course series helps you explore and visualize data in practice. It also focuses on how to conduct data analytics and build reports with SAS Visual Analytics.
Statistics Foundations 1: The Basics: This course helps learners with any technical background understand the essentials of statistics.
Learning Data Analytics: 1 Foundations: This course helps beginning and intermediate learners identify, interpret, clean, and analyze data.

For Data Fluency: Courses often focus on statistical analytics, predictive modeling, or data visualization tools.

Data Science: Statistics and Machine Learning Specialization: The course includes different topics like statistical inference, regression models, machine learning, and how to develop data products. At the end of the course, you’ll conduct a capstone project that requires you to use data skills and real-world data to build a data product.
Excel to MySQL: Analytic Techniques for Business Specialization: This course guides you to analyze data, build predictive models, and create visualizations by using powerful tools like Excel or MySQL. It also offers a capstone project where you can apply your skills to improve Airbnb’s business process.

3. Mentorship Programs

These programs will pair learners with experienced experts who offer tailored guidance. They’re suitable for those seeking one-by-one learning experiences to apply data skills in their specialized industries or deepen their understanding. With mentorship programs, you can receive direct, real-time feedback and personalized support, coupled with opportunities to discuss certain problems.

4. On-the-Job Training

This training program helps employees apply data-driven practices to workflows and perform role-specific tasks. It allows them to gain experience with real data and through practice instead of only learning theories.

What should you consider when designing a training program?

Data literacy and fluency are not a one-time event but an ongoing process. So, to ensure employees can easily digest content in busy times, you should deliver short, focused modules. Also, frequently update training materials to keep pace with the latest technologies and industry trends.

Further, conduct regular evaluations by using performance metrics, quizzes, and certifications to measure how much employees have learned. Collect feedback to understand what worked well and what to improve in the training process. Then, you can modify training methods based on results to address any challenges.

Tip 4: Focus on hands-on practice

Theoretical knowledge alone is insufficient to improve data literacy and fluency effectively. Employees learn best when they can use skills in real cases. Through hands-on practice, they can become more confident and proficient in working with data. Additionally, practical tasks often require collaboration across teams, which allows employees to learn how to communicate insights effectively.

Your company can foster practical application by encouraging employees to use knowledge and tools in their regular workflows. For example, a finance team can leverage Power BI dashboards to track monthly expenses and identify anomalies. Also, start from small-scale projects before diving into high-stakes mission tasks.

Moreover, you can organize company-wide competitions or hackathons for employees to handle data problems. You can also reward innovation and success stories to encourage participation. Plus, during training sessions or workshops, your business can design simulated projects that mimic real-world problems employees often confront.

Tip 5: Build technical and non-technical skills

Improving data literacy and fluency means equipping employees with both technical and non-technical skills. According to our years of experience working with different clients, we at Designveloper realize that, for example, a business analyst hardly draws persuasive conclusions and suggests suitable solutions if he/she doesn’t have skills like data visualization and storytelling.

Technical skills enable them to work with data-related technologies and tools productively. Depending on the ultimate purpose of training and your workforce’s fluency levels, you’ll decide which skills employees should understand or master after training. Below are some common technical skills:

Data Analysis: Learn how to identify, clean, organize, and analyze data to derive insights.
Visualization: Create clear and meaningful visual representations of data by using tools like Excel, Power BI, or Tableau.
Statistical and Mathematical Skills: Be familiar with basic statistical techniques.
Data Management: Understand how to leverage databases, manage data quality, and secure data.
Programming: Know such programming languages as Python, SQL, or R for data manipulation and task automation.

Beyond technical knowledge, soft skills also play a vital role in communicating and adopting data insights effectively. Below are some common non-technical skills to consider:

Data Storytelling: Present data results in a clear and interesting way so that even non-technical audiences can understand.
Critical Thinking: Assess data critically to ensure its relevance and accuracy as well as use it for informed decision-making.
Problem Solving: Adopt data insights to tackle real-world problems.
Communication: Share and communicate insights with stakeholders using clear, concise language and visuals.
Collaboration: Work with cross-functional teams to reach common goals.

It’s Your Turn

After this article, you’ll have a comprehensive overview of how to improve data literacy and fluency effectively. Each company and individual has their own ways of acquiring data skills. Despite some challenges you may encounter on the journey of data learning, tailoring a well-structured initiative with clear objectives and regular evaluation helps everyone in your company become proficient in using data. Now, it’s your turn to embrace learning! For more interesting topics about data, subscribe to our blog now!