As chief data officers (CDOs) rush to bring artificial intelligence (AI) innovations and deliver measurable ROI from data investments, they face a glaring problem: Most business users don’t have the expertise to unlock their data’s full potential. In fact, research shows poor data literacy and skills gaps are among the top five reasons most data and analytics initiatives don’t succeed.
So what can data leaders do to make data accessible and usable to the appropriate audiences? One critical part is building a data literacy program that equips teams with the skills to interpret and leverage data effectively. Another is getting the foundation right. Sounds too jargony? That’s because it often is. Before you know it, what seems like a simple task becomes muddled with so much complexity that it’s hard to see up from down.
That’s why, we have prepared this detailed guide. It starts with the basics of data literacy and breaks down complex concepts so you can confidently cut through the noise and build a data-driven culture for your organization.
Table of contents:
Data literacy is the ability to create, manage, analyze, understand, and communicate data. Unlike technical literacy which requires the ability to operate a technical tool or technology, data literacy is broader and much more context-dependent on specific use cases and applications.
Imagine you’re looking at a chart showing your company’s sales performance metrics. There’s a lot of information there, but how do you turn that sea of numbers into something meaningful?
Data literacy helps you understand the story behind these numbers, giving you the ability to spot key patterns and connect the dots in a meaningful way. With the right skills and tools, you can explore further and ask important questions like “Why did sales drop last month?” or “Which products are selling the most?” These insights can help you make smarter decisions that drive better business outcomes.
Unlike technical literacy, which requires the ability to operate a technical tool or technology, data literacy is broader and much more context-dependent. At its core, data literacy can mean different things to different members of an organization.
For instance, an analytics engineer might want to adopt technical data skills that help them build accurate ML models. On the other hand, a business executive might want to understand and communicate the implications of data for the broader organization.
Based on this premise, data literacy skills fall broadly under two categories: technical and non-technical. Here is a brief overview of these two categories:
1. Technical data literacy
This category is essential for roles working directly with data, including data engineers, analysts, and IT teams. These professionals require a range of technical skills to effectively manage, analyze, and present data. Some of these skills include:
Data management: This skill set includes accurately and efficiently collecting, storing, and maintaining data. It involves developing a deep understanding of data cleaning, warehousing, and governance to ensure data is readily available for analysis.
Data analysis: This section involves developing skills to use different statistical methods and analytical tools to identify patterns, trends, and outliers within datasets. Here, you may also need to learn to apply techniques like regression analysis, hypothesis testing, and time-series analysis.
Data visualization and reporting: By acquiring the skills to present data findings through visualizations like charts and graphs, you can better showcase your findings to stakeholders. This skill set also includes knowing how to work with tools like SQL, Python, or data visualization software.
Data modeling: Gaining expertise in creating and optimizing data models is essential to generate accurate answers and explore possible outcomes. Developing data modeling skills may also require you to learn about defining data relationships, identifying relevant metrics, and maintaining data integrity, all of which are critical for deriving accurate insights from data.
Data science: For teams focused on predictive modeling and prescriptive analytics, having the ability to develop, train, and evaluate machine learning models is essential.
2. Non-technical data literacy
Non-technical data literacy is about understanding, interpreting, and applying data to support decision-making. Mastering these skills is especially important for business executives who want to leverage data to guide strategies. Some of the non-technical data literacy skills include:
Research: By identifying the right data sources, data professionals can better understand where the data is coming from, recognize potential biases, and gain context around the specific dataset.
Data interpretation: It's not enough to just have data—it is equally important to understand what it really means. Mastering these skills involves using data visualizations to identify trends, spot opportunities, and recognize patterns that may not be immediately obvious.
Communication: Effectively communicating data involves translating complex information into clear, actionable insights that align with business goals. By honing data visualization and communication skills, professionals can deliver key findings to stakeholders in a way that is both understandable and impactful.
Critical thinking: Data literacy also includes the ability to ask the right questions—questions that can help data pros uncover granular insights and solve business problems. Leaders with strong critical-thinking skills can leverage their data to analyze what's happening, explore potential solutions, and discover new opportunities for growth.
Domain knowledge: Staying informed about new trends and developments in the analytics industry will help data teams keep up with industry leaders and maintain a competitive edge.
In a recent episode of “The Data Chief,” Proctor & Gamble Senior Vice President of Data and Analytics Alfredo Colas had this to say: “If we want to be here another 100 years or another 183 years, we need to be training the new generations about data so that they can be successful in the future.”
By fostering data literacy, you ensure decisions at all levels are guided by data. And when data is readily available, trusted, and used consistently by all, it naturally encourages open communication and innovative thinking. But this is just one side of the story. Here are the top ways companies use data literacy to remove barriers and create an environment that encourages a data-driven mindset:
1. Enhancing decision-making
In today’s fast-paced business world, business teams have the latest data at their fingertips. But only a few actually understand this data, and even fewer have the insights to take action. In fact, Gartner research shows that 83% of business strategies fail due to faulty assumptions. To avoid falling into this trap, it’s more important than ever for data leaders to promote data literacy and give every team member the confidence and skills to turn raw data into actionable intelligence.
Take Wellthy, for instance. By adopting ThoughtSpot and equipping its care teams with the necessary training, it’s empowered every member of its care team to answer questions with data. Because data isn’t hidden behind a curtain or controlled only by C-suite executives, Wellthy’s care team can provide a higher level of care to its customers—all while easing the workload on its data team. This example clearly shows data literacy can have a transformational impact across any organization.
2. Increasing efficiency
Here’s how most organizations still operate today: Every time business teams have a question, they must submit a request to the data team and wait weeks for a response.
This approach is problematic on several fronts. First, it puts unnecessary strain on data teams as they have to keep up with growing ad-hoc requests. Second, by the time the answers finally arrive, the moment has already passed. Despite their best efforts, business users are unable to capitalize on emerging trends and opportunities.
But what if every business user could access the data they need, whenever they need it? With data literacy, you put your data into the hands of decision-makers and the frontlines of your business.
They can instantly access, analyze, and interpret the information they need, enabling them to act quickly and boosting efficiency. In fact, studies reveal that 72% of data leaders say productivity has increased since empowering frontline workers with data. Data literacy is the key that allows your business to run more efficiently and even opens up new sources of revenue through integrations like embedded analytics.
3. Fostering clear communication
We’re an AI-Powered Analytics company, so you would probably expect us to go on and on about advanced analytics. However, we believe the power of data lies in true self-service, meaning everyone should be able to create their own data stories and clearly communicate insights.
When teams are data literate and have intuitive tools like ThoughtSpot’s Spotter at their disposal, they can easily visualize trends in data and effectively communicate their findings.
When Alanna Roesler wanted to create a more data-driven culture at Schneider Electric, she brought in ThoughtSpot to improve her team’s data access. The outcome? Schneider Electric’s People Team could pinpoint effective recruitment channels and build a clear data narrative, resulting in a more inclusive workplace culture and positive business outcomes.
Promoting and nurturing data literacy comes with its own set of challenges. From overcoming resistance to change to ensuring teams have the right skills and support, it takes an entire village to make it successful. That said, let’s discuss some top obstacles companies face:
Organizational silos: In many organizations, business departments still operate independently. Most of the data remains siloed within the data teams or IT specialists, restricting business users from accessing the insights they need. This siloed approach also restricts cross-team collaboration, slowing down the decision-making process.
Workforce resistance: Most users are so used to doing things a certain way that they can’t get excited about acquiring new skills or learning about a new analytics tool. They might resist change because they feel overwhelmed or don’t have time to learn something new unless they see the value in it for them.
Lack of data governance: Without clear guidelines around how data is collected, stored, and used, employees might struggle to trust or even fully understand the data they have access to. Poor data governance also leads to inconsistencies and errors in results, undermining users’ confidence in data-driven decision-making.
While the exact approach to building data literacy in an organization can vary, there are a number of tips that can apply to most businesses:
1. Educate employees on working with data
Employees need to trust the insights they gather from data. If data is messy or out-of-date, it provides much fewer actionable insights and can even lead to incorrect decision making. That’s why capabilities like Live Analytics, which ensure employees are always working with the freshest, most up-to-date data possible, are critical to building this trust.
Furthermore, they must know how to manipulate the data in a way that makes it usable for their given use case. While raw data is certainly important, it often needs to be processed into an accessible format for people of different technical backgrounds or domains, such as data visualizations like charts and graphs.
2. Utilize intuitive tools
Many organizations fall into the trap of trying to train employees to use complex tools to analyze data. However, most employees aren't software engineers or data scientists. That’s why it’s important to procure and build software that allows people of all technical backgrounds to access and analyze data.
Modern analytics solutions like ThoughtSpot give everyone quicker, easier access to the insights they need, making it the perfect platform for any data-driven organization. With Spotter as your AI analyst, business users can simply type a question in natural language to gain instant insights from their data. Such conversational experiences enhance data accessibility, making the entire process of data exploration intuitive and engaging for users.
3. Grow employee confidence
Many employees—particularly those with a less technical background—are usually nervous to start working with data. They might think doing so requires programming experience or a deep understanding of complex mathematics and statistics.
But that’s not the case. By providing personalized training and leveraging self-service BI, business leaders can assure their employees that they, too, can become confident with their data skills—even without deep technical proficiency.
For example, Matillion, a company specializing in cloud-based business intelligence analytics, faced challenges in making data easily accessible to all users. Their growing sales teams sought deeper insights into customer data and even their finance team required additional agility to analyze financial trends on-the-fly.
By deploying ThoughtSpot and engaging stakeholders early on, Matillion ensured a smooth transition and enthusiastic adoption across the organization. This structured approach increased the platform’s adoption rate by 60%, ensuring everybody could actively engage with the platform. Even better, with SpotIQ, business teams could easily identify anomalies in data, forecast trends, and make timely decisions.
4. Separate data literacy from technical literacy
When helping employees of different technical backgrounds get more comfortable working and communicating with data, it can be useful to differentiate data literacy from technical literacy.
Unlike technical literacy, data literacy does not require the operation of specific technology tools. Rather, data literacy is a broader approach to understanding and using data regardless of whether it’s in the context of programming and data science or business analysis and communications.
5. Have a data leader guiding efforts
All leaders — from middle management to the C-Suite — should embrace data literacy. However, it can still be very valuable to have a specific individual or set of individuals in charge of educating employees, standardizing data processes and systems, and setting the general narrative for how data is communicated. In organizations with a Chief Data Officer or other data leader, it often makes sense for them to fill this role.
Doing so will ensure that there is consistency across the organization so that departments can easily communicate and work together with a common language and process. This will maximize efficiency and reduce confusion caused by otherwise inconsistent standards.
6. Reduce employee resistance
As with all changes, some employees will be resistant to learning and implementing new processes. In addition to rewards and incentivization, it is often worthwhile to communicate to employees why the changes are being made, what’s in it for them, and how they can voice their feedback and concerns.
In reality, no company will be the same in how it speaks about data and what its common processes look like, so creating an ongoing conversation about how to best utilize data for the good of the broader company and for their specific role can ensure that the processes make sense and employees feel like they are heard.
7. Bring analytics to existing workflows
Asking employees to leave existing workflows and patterns causes friction, because it disrupts how they've gotten used to operating. Instead, consider ways to bring data to their current workflows by embedding analytics right into existing tools and software. This encourages employees to use data without alienating them with too much change.
8. Plan for data-driven decision-making
Once processes are in place and employees have an understanding of how to view and analyze data, it should become standard practice that these systems are utilized to make smarter, data-driven decisions. This will require employees to really understand and trust the data and the systems around the data. It also requires connecting systems together so that employees can use data insights to fuel actions in other applications. Over time as leaders and others throughout the organization start to make this the norm, the culture should become more data-driven.
9. Emphasize constant learning
Building data literacy is an ongoing and iterative process. Leaders should embrace new technologies and ensure all employees are kept up to date on how to use them and communicate their results. This will ensure that the business does not fall behind and can leverage the latest and greatest technologies to make it more efficient.
Data literacy empowers every employee to build and share knowledge and take smarter, more data-driven actions. However, this data-driven approach doesn’t mean that only managers and executives will be utilizing more data assets. In fact, 87% of surveyed business leaders say that their organizations will be more successful when their front-line workers have the data resources and technical capabilities to make important decisions in the moment. And yet even young people who are digital natives are not very confident in their data literacy, with just 43% of surveyed 16-21 year olds believing they are data literate.
This means that everyone from support staff to the C-Suite needs to understand how to work with data and make data-driven decisions. For this reason, the entire organization needs to be data literate regardless of their position.
💡Discover from industry leaders the best practices for developing a culture around data literacy
Step 1: Start with leadership
Management and executives drive not only process but also culture. In order to create a data-driven organization, leaders from all departments must be proactive in fostering an environment that thinks and acts with a data-first approach.
In addition to buying into programs to teach employees how to be data literate, leaders themselves should communicate using data and let it inform and drive their own strategic decisions. Doing so will set the common language and best practices that other employees will follow.
Step 2: Assess your organization’s current data literacy
To improve an organization’s overall data literacy, its leaders must first understand the current state of how employees are creating, using, and communicating data in all key business processes.
Data literacy efforts are often part of larger digital transformation initiatives. While these efforts typically focus on new systems and data sources, they must be paired with increasing everyone in the organization's ability to understand the insights those systems generate.
There’s also no one-size-fits-all approach to data literacy, thus leaders will need to analyze how each department is leveraging data. Some more technical departments may already be fluent in complex data topics, while others are still relying on manual and less data-driven approaches.
Step 3: Create measurable goals
For large organizations, helping every employee at every level become sufficiently data literate will take time and is often an ongoing process. Therefore, it’s important to set specific goals, targets, and KPIs for understanding progress.
For example, for departments that use repeatable and well-documented business workflows, like an accounting department, a helpful metric might be the percentage of the workflows that are incorporating key data sets. Furthermore, you could even track how many of these departments are contributing data back to key datasets to encourage a community of shared inter-departmental knowledge.
In setting up these goals, it’s important to make sure that an organization leverages best practices to manage and centralize data. Otherwise, there’s a risk of data becoming fragmented between different departments with no one source of truth, which can lead to significant friction, redundancy, and uncertainty in performing data-driven tasks.
Step 4: Develop a data literacy training plan
With the different learning styles in mind, leaders should develop a training plan that involves many different types of educational content — seminars, group classes, quizzes, online courses, games, and more.
Just like in other educational situations, it’s important to consider how different people learn. Are the employees visual, auditory, or reading and writing learners? Are they better in group classes or through independent exercises? What are their educational experiences or even language skills? Everyone is different, so data literacy training should allow all different types of learners to feel comfortable and gain these new skills in an environment that encourages them to do their best. This will ensure that different groups don’t get left behind simply because they don’t fit into a generic mold.
This also shouldn’t be a one-time process. Best practices in data analysis, statistics, and software change constantly, so training should be an ongoing part of the business to ensure that employees are up to date in their understanding of all these facets.
Step 5: Reward learning
Rewarding or incentivizing employees to become data literate can help to encourage faster adoption. For some departments, it may even make sense to tie compensation to data-related goals and KPIs.
In doing so, employees will be much more open to learning and adopting new systems, and they will likely be more willing to work with their colleagues to ensure its success. This should reduce friction and pushback that you might see when trying to get employees to change how they’re operating.
💡Leverage T-Mobile’s playbook on data democratization to build a data-driven of your own.
Becoming a data-driven organization requires all employees are data literate. To facilitate such a culture, you need an AI partner that empowers every member of your organization to initiate action.
ThoughtSpot is that partner. With decision-ready insights and interactive Liveboards, ThoughtSpot allows you to create personalized data experiences however you want them. You can easily sync ThoughSpot to your cloud data, build meaningful visualizations, and gain AI-powered insights, ensuring you’re always making the most of your data.
See how ThoughtSpot can help you make your business more data literate—schedule a demo.
