Introduction

As business drowns in data and technological advancements, the importance of data literacy has never been more apparent. Business increasingly relies on data and is under more pressure than ever to use data for decision-making in all areas of business. So the ability to understand, interpret, and communicate data effectively has become a critical skill for employees across all levels and functions.

‍

Why does data literacy matter?

According to Forrester's projections, by 2025, a staggering 70% of employees are expected to work extensively with data—an exponential increase from just 40% in 2018. It's a sign of the times: data-intensive roles taking over, and we all need to be ready to use analytics tools and technologies to drive business value.

But here's the thing: while everyone is diving headfirst into all things data, many companies are struggling to keep their heads above water.

‍

‍

The reality check: a huge data gap

Recent research from IDG Communications paints a picture of chaos in the realm of data and analytics leadership. Data is everywhere, but it's poorly organized, managed, and governed.

Moreover, despite the increasing popularity of data analytics tools, global adoption rates remain relatively low, averaging around 25-30% (Wiisdom Research). This discrepancy between the growing demand for data literacy and the underutilization of analytics tools shows a big problem that companies need to fix to make the most of data-based decisions.

As an analyst, I can see two challenges that come with bridging this gap.

On one hand, reports will remain the primary form for analytics consumption for the majority of business users, meaning that data literacy enablement is a must.

But on the other hand, the penetration of BI tools still remains relatively low, hovering around 30%, which is a considerable distance from the projected 70% of employees expected to work with analytics and data by 2025.

How can we close this gap? What strategies can we use to boost data literacy and improve the odds of achieving success with data-driven decision-making in business?

‍

Data Literacy: A critical missing piece

If we go back to the definition of data literacy... Gartner defines data literacy as the ability to read, write and communicate data in context, including an understanding of data sources and constructs, analytical methods and techniques applied, and the ability to describe the use case, application and resulting value.

To bridge the analytics gap and enable data literacy, it's essential to focus on three critical components:

• Skills - because that’s where data literacy typically begins: with understanding, interpreting, and communicating data.

• Applicability of these skills - because obtaining data literacy skills is only the first step; it's equally important to ensure that these skills can be applied effectively in real-life situations.

• Motivation to apply skills - because every new initiative requires ongoing motivation and encouragement to apply data literacy skills in daily workflows.

But let’s go one at a time and take a closer look at why each of these components is critical and how they can be practically implemented.

‍

Acquiring skills: the foundation of data literacy

When it comes to data literacy, acquiring the necessary skills is where it all begins. Understanding, interpreting, and communicating data effectively are fundamental skills that every individual within a company should possess.

1. Training and Education:

Gaining analytics skills involves implementing comprehensive analytics training and educational programs tailored to users' needs. These programs can range from basic BI tool navigation to advanced technologies like R or Python. However, it's crucial to tailor the analytics training to the specific needs and roles of individuals within the organization.

2. Hands-on Experience:

Practical experience is key for honing data literacy skills. Encouraging employees to work with real-world reports and data sets allows them to apply their knowledge in meaningful ways and gain confidence in their abilities.

3. Continuous Learning:

Data literacy is not a one-time achievement but an ongoing journey. Encouraging a culture of continuous learning and skill development ensures that employees stay up-to-date with the latest analytics updates and changes in BI tools.

While technical training in tools like R or Python is valuable, the primary focus should be on equipping users to navigate BI tools effectively. This includes familiarizing them with the BI environment, navigating through reports, working with filters, interpreting data accurately, and understanding different views.

Since the majority of users consume analytics rather than produce it, prioritizing user comfort and proficiency in utilizing BI tools is what really boosts analytics usage across the organization.

‍

Applicability: making data skills count

Imagine the scenario post-training: users just finished an analytics training session, feeling confident. But then, they’re at the desk, faced with a new report or a question about the calculations. Where should they even begin? That's where the rubber meets the road – applying what you've learned in real-life situations.

Analytics discoverability:

Creating a user-friendly space with a Google-like search experience is essential. Users should be able to easily navigate through the analytics environment, search for relevant reports, and access the information they need without unnecessary barriers. Search functionality coupled with intuitive categorization makes it easy to explore and discover insights.

Learn more about analytics discoverability in our e-book.

Interpretation through business glossary:

To have a common understanding of terminology and concepts you need a comprehensive business glossary. Rather than creating an abstract data glossary with only technical information, you should use and describe the knowledge included in your reports.

Why not involve users in the process of defining and refining business terms based on actual report usage? This way you can create a practical and actionable business glossary that actually helps.

Ongoing support and guidance:

Users generate a non-stop flow of questions, concerns, or access requests post-training. What you should do is offer easily accessible channels for users to seek assistance, report errors, request changes, or clarify doubts. Implementing a user-friendly support system with clear instructions and quick response mechanisms ensures that users feel supported and empowered in their analytics journey.

‍

Leading the change: from skills to habits

Ever noticed a surge in analytics usage right after training, only for it to dwindle over time? It's a common scenario. So, how do we keep users engaged with analytics regularly?

Regular user engagement sessions: Keep users in the loop with frequent sessions that involve analytics across the entire organization. Consistency is key to making analytics a part of everyday work life.

Departmental support: Offer tailored support at the department level, understanding their specific needs and challenges. Appoint analytics champions in each department to promote analytics usage.

Empowering analytics champions: Identify and empower analytics champions within the organization. These enthusiasts can inspire others and drive BI and analytics adoption by showcasing the benefits of analytics.

Gamification: Make analytics simple and fun! Let it be accessed from a single space with leaderboards, badges, and rewards for active interaction with reports from different BI tools. Try this with Accton Insight Hub.

Feedback mechanisms: End-users should have a voice in analytics creation and change. A working feedback loop helps identify areas for improvement and keeps users engaged by demonstrating that their input is valued.

Integration with daily workflows: Integrate analytics tools seamlessly into existing workflows to make them easily accessible during the course of daily work. This minimizes disruptions and encourages regular usage as users can seamlessly transition between their core tasks and analytics activities.

Celebrating success stories: Highlight success stories and praise little wins of your employees. Let them feel more confident and valued for their extra efforts.

Learning opportunities: Offer a variety of learning resources beyond formal training sessions, such as webinars, tutorials, and self-paced online courses. This allows users to deepen their understanding of analytics tools at their own pace and stay up-to-date with new features and functionalities.

‍

What data-literate future holds

The road to data literacy is both a challenge and an opportunity for organizations to succeed in the data-driven era. We've delved into the complexities of bridging the gap between data literacy demand and analytics adoption, exploring practical strategies to empower teams and cultivate a data-literate culture.

As we reflect on the journey thus far, it's crucial to recognize that data literacy is not just about acquiring skills - it's a mindset and a culture. Today data and insights are abundant, so the ability to navigate them with confidence and clarity is more important than ever.

Looking ahead, I believe that data literacy should be embraced as a strategic initiative.

Imagine a future where every member of your organization is not just capable of making data-driven business decisions but actually enjoys unlocking valuable insights. It's a future where data literacy transcends job titles and departmental silos, empowering individuals to rely both on data and their professional experience.

We as business chose this path together. Let's remember that the true value of data literacy lies not just in the skills we acquire, but in the impact we create. So let's continue to learn, grow, and innovate as we navigate business through the data world.

Thank you for joining me on this journey. Here's to a future where data literacy knows no bounds.