What is Data Literacy?

Data literacy has transformed from a niche skill for IT specialists into a fundamental competency required across all industries and roles. Its evolution reflects the increasing importance put on data in decision-making and the democratisation of tools and resources that make data accessible to everyone.

What is the meaning of data literacy?

• In short, data literacy is the ability to understand, work with, analyse and communicate with data.
• The most important outcome of data literacy is gaining insights, and being able to communicate these effectively with stakeholders.
• To go from a culture where decisions are primarily made based on gut feelings, to one that is evidence-based, data literacy is a core competency and building block of any organisation.

When we break the elements that make up data literacy into a more granular framework, we can see four dimensions at play:

• Data attitude:
  • understanding the role of data
  • having confidence in using data
  • data values and ethics
• Data awareness:
  • the ability to find problems through data
  • the ability to solve problems by using data consciously
  • ability to actively acquire, update, store and share data resources to make sustainable use of them
  • the awareness around security and governance needs of data
• Data knowledge:
  • knowledge reserve for data mining, such as the basic concepts, representation forms, storage formats and transmission rules of data
• Data skills:
  • the practical ability to acquire, store, process, analyze, apply and display data by using various tools and means

What is the importance of data literacy?

Currently, the average C-Suite estimates that over half (55%) of their workforce are confident in their data literacy skills. In reality, only 11% of employees are fully confident in their ability to read, analyse, work with and communicate with data. These numbers paint a challenging picture.

Data leaders often skew towards a focus on technology and process, leaving the adoption of implemented technologies as an afterthought or not getting the investment required for wide-scale education. And if they do, dealing with the complexity of behavioural change, leveraging educational principles and giving adequate support to a vast group of people with differing abilities often falls outside of the capabilities of the business. And that’s where the trouble starts.

Companies are pushing for data democratisation, which is great. However, without adequate support, employees will continue to feel overwhelmed. Qlik measured this to be the case for 74% of employees. Most of them don’t have a background in data and analytics, and it’s also not the point to turn everyone into an expert analyst.

What is necessary, is that people avoid using data altogether because they don’t know what questions to ask, or where to start looking to use data in a valuable way. This needs to be fixed as a top priority or leaders risk losing out on efficiency, solid decision-making, innovation and credibility within their organisation.

Data literacy needs to be a foundational pillar of every data strategy.

The gap between expectation and reality won’t be relieved until business users have the right mindset and capability to leverage the value of data, collaborate more closely with their data teams on solving problems, and for data teams to understand what data products will best service corporate goals. It’s clear as day that data tools are only as effective as their users, and that’s where additional investment is critical.

And being effective boils down to getting every person in the business to understand:

• What data is and why it matters
• What their role is in the creation and usage of said data
• How to question the quality of the data and challenge it
• How to derive tangible insights from it that make a difference
• How to share their insights in a digestible, audience-aware way to spark further action

What is the impact of inadequate data literacy in an organisation?

When data literacy is low, decision-making suffers. Companies might rely on guesses or instinct rather than informed decisions, often leading to suboptimal outcomes.

As tech enterpreneur Suhail Doshi stated, "Most of the world will make decisions by either guessing or using their gut. They will be either lucky or wrong."

This approach limits growth and competitive advantages. Businesses miss out on opportunities due to poor data interpretation.

These downfalls can be mitigated by conducting regular data literacy assessments, aligning these with growth strategies, and evaluating improvements in decision-making outcomes.

What barriers do companies face in adopting data literacy?

• Data overwhelm is a massive challenge. Knowing where to start is often a problem.
• The expectation of being data-driven versus the actual skillset of being data-driven causes team stress
• Effective data use hinges on quality, accessibility, and understanding of tools and concepts, all of which require adequate training to grasp their impact

Growing amount of data that needs processing and analysis

Information overload is a serious problem for most enterprise businesses. While data storage capacity has increased dramatically, this has led to more data than most teams know what to do with. It leads to tons of information, but a poor level of insight.

The reality is that a lot of data teams are reactive, don’t have the bandwidth to empower other people to self-serve and are implementing technology without bringing the wider organisation on the journey. This leads to bottlenecks and broken processes where the business doesn’t even know what untapped potential is in their data.

Data sets can be of tremendous value, if teams are aware of how to leverage them. When more people feel comfortable taking on tasks that can lighten the load of the data team, they will have more breathing room to drive the value they should be delivering. But none of this is possible without investing in upskilling. The expectation that technology just delivers on its own is false, and human capabilities need to be front of mind in every transformational process.

The data literacy gap

A lot of companies are on the journey of making their data more readily accessible, and adapting to provide real-time analytics. But without the fundamental knowledge of understanding the “why”, data product delivery will still miss out on its intended purpose if not created in a collaborative fashion with people who understand the importance of data. This gap needs to be bridged if employees are to deliver data-driven initiatives, and it will only widen with AI and Machine Learning taking the spotlight.

Rising expectations for enterprises to be data-driven in decision-making

With new tools coming to market every year, the expectation grows in equal measure that decisions will become more and more evidence-based and accurate. But without a data-literate staff, these tools alone won’t be the solution. It requires an organisational cultural shift.

Business as usual needs to be assessed, gaps addressed and a change management plan put in place. The starting point is addressing the human side of data and analytics. When people can see the purpose of changing their behaviours, and are given the skills to adapt, the outcomes will extend beyond expectations.

And it’s not just employees. Senior leaders are inherently expected to be data-driven, but many are still lacking in their own data-driven mindset, often relying on experience and being unaware of their own biases which can lead to detrimental miscalculations.

When bridging the data literacy skills gap, businesses can expect to see a positive impact in the following areas:

• A 3.2x higher return on their data initiatives n 2024, only 15% of companies reported getting actionable insights from their data investments, yet those that did saw a 3.2x higher return on their data initiatives.
• Employees who identify as data-literate were at least 50% more likely than their data-novice peers to feel empowered and trusted to make better decisions. (Accenture)
• When employees take a  collaborative data exploration approach, efficiency improves by 47%. - 2024
• Organisations with strong data cultures saw 2.8x higher innovation rates
• Businesses prioritizing governance saw 76% fewer incidents
• Early adopters of integrated intelligence reported 3.2x higher ROI on data initiatives
• 85% of C-suite executives believe being data-literate will be as vital in the future as their ability to use a computer is today (Data Literacy Project)
• 90% of global employees believe that openly sharing data would make their organisation fairer and more responsible (Data Literacy Project)
• In the C-suite, 89% of leaders expect team members to be able to explain how data has informed their decisions (Data Literacy project)
• 90% of C-suite executives say that data enables them to better navigate the uncertain business environment created during the pandemic, and that it was critical to the success of their organisation. (80%) (Data Litearcy Project)

Both leaders and individual contributors need to be brought on the journey of why data is important and what role they play in the value chain. If people don’t know what data they can access, how to assess data quality, how to work closely with the data team and ask them better questions, the accumulation of tools and information will just end up being a money pit with the promised ROI materialising.

With the global data analytics market is set to reach over $105 billion by 2027, the time is now to bring data literacy to the forefront of both your corporate and data strategy. As Miro Kazakoff, a senior lecturer at MIT Sloan, puts it, "In a world of more data, the companies with more data-literate people are the ones that are going to win."

Key topics that need to be addressed in any data literacy programme

When we talk about data literacy, it includes a wide range of topics. While technical skills are beneficial for those who need them in their roles, the foundations are mostly relating to mindset, communication and critical thinking.

Let’s take a look at some of the topics you need to include when launching a data literacy programme in your company:

Foundations of data and data culture

• What is data?
• Why is data important?
• How do employees develop their confidence in working with data?
• How can employees at all levels engage with data without fear of making mistakes?
• Who are the data team and what do they do?
• What’s your role in creating data?
• What role does feedback and iterative learning play in building data literacy?
• How do you create a culture that encourages questions and exploration with data?

Communicating and storytelling with data

• What are different ways of visualising data to make it more understandable?
• How can you tailor your communication to suit different audiences (e.g., technical teams versus senior leaders)?
• What makes a data story compelling, and how do you align it with organisational goals?
• How can you use data storytelling to foster collaboration across teams?

Data governance, ownership and ethical use

• What does good data governance and ownership look like, and why is it crucial?
• How can ethical considerations guide data practices in your organisation?
• What steps can be taken to ensure data privacy and security?

Role-specific applications of data literacy

• How does data literacy support decision-making in roles like marketing, finance, or operations?
• What tools, like Power BI or Tableau and techniques are most relevant to different functions or personas (e.g., beginner vs. advanced users)?
• How can you elevate both competent Excel users and beginners in their technical skillset after the foundations have been laid?

How to implement effective data literacy training programmes for all levels of staff

Start by assessing existing skills. This can be done by surveys and tests. At Data Literacy Academy, we assess every single learner in the same way to create a baseline understanding of their current capabilities. This is needed to evaluate what the impact on the learner is. It helps identify strengths and weaknesses, and is used to put them on the correct learning track.

Now let’s dive into the best practices of building a data literacy academy and what it takes to make it effective.

1. Engage with experts: Internal teams are often stretched for time or lacking the necessary skillset to build a successful academy. It’s also costly to hire extra people, so working with external partners is your best bet at getting to your desired goals within a quicker timeframe. It also adds credibility and means you can get the wider industry expertise outside professionals bring to the table.
2. → At Data Literacy Academy, data literacy is what we do all day, every day. This means we are constantly evolving our education, bringing in new experts while taking the pressure off internal teams. With our change management approach, we get the C-Suite, middle management and learners to align on the need for more data literacy, and get them excited to get started. Collaborating closely with internal communications, L&D and other teams mean we deliver a service that fully aligns to the current ways of working of your business, without requiring a full-time team to do the heavy lifting.
3. Curriculum design: A strong curriculum based on the skills rather than job titles needs to be identified and developed. It should include modules for beginners, intermediate, and advanced levels. Educational expertise is highly important as an understanding of pedagogical principles will ensure the learning has the desired outcomes.
4. → At Data Literacy Academy, we work with professional educators, data leaders and the CPD team to ensure our certifications meet the needs of our customers and have the most engaging format for learners. The focus isn’t only on getting high engagement, but also on creating feedback loops to ensure the learning is put into action after class.
5. Schedule regular training: Data literacy is not something you set and forget. Consistent education over an extended period of time, broken up in bite-size chunks is more effective than a single workshop could ever be. Building data literacy education as a foundation of your company culture means there will be a stronger growth of your data communities. It also means that it will stimulate a mindset shift where you no longer have the desire to become more data-driven, but truly see all departments use evidence-based thinking to achieve their goals.
6. → At Data Literacy Academy, we provide live learning with weekly hourly allocated slots. This model is proven to create high engagement, stimulate team conversations and actions, while also providing enough topical depth to have a real impact. There are options for 8-month certifications, 1-month certifications and one-off leadership workshops. We also offer a self-guided platform where learners can consume shorter virtual lessons, which is beneficial to extend learning to a wider audience or as a starting point to get familiar with the language and concepts of data.

How do you measure the impact of data literacy?

Monitor employee engagement and usage of data tools

Engagement with data tools signals data literacy's effectiveness. Metrics include the frequency of tool usage, diversity of tools used, and depth of features utilised. Use analytics like Power BI or Tableau's built-in analytics tracking to gather these numbers. Employee feedback and adoption rates can further illustrate engagement. High usage and positive feedback often correlate with successful programs. Emphasising user feedback loop improvements could inform how programs should adapt.

An interesting niche area involves the use of employee engagement software. Recent data suggests AI-driven analytics enhance engagement levels, providing additional context for evaluating data literacy efforts.

Assess performance improvements linked to data initiatives

Performance improvements are where data literacy shows its value. Measure this through financial metrics, like revenue growth or cost reductions, directly linked to data projects. KPIs such as time saved on data tasks or error rates after data initiatives provide more granular insights into performance betterment. Enterprises might consider using dashboards to track these metrics consistently over time.

Scholarly articles and business analyses often underline a few perennial challenges. While some industries quickly realise performance gains, others may experience lag due to structural or cultural barriers. Look into "The Data Revolution" by Rob Kitchin for further context. This book discusses systematic changes needed to align organisational performance with data literacy advances and critiques areas where assumptions might falter.

Employee skill assessment scores pre- and post-training

Assessing employee skills before and after training gives a clear indication of knowledge gained.

Enterprises should compare these scores over different periods, adjusting training materials and strategies to address gaps. Using online tools for testing can streamline this process. However, some experts argue that over-relying on tests may ignore behavioural changes, advising a blend of qualitative and quantitative evaluations to get a more rounded view of data literacy advancement.

Regular assessments and feedback for continuous improvement.

Without regular assessments, enterprises may struggle to maintain momentum in their data literacy journey. Iteration is key. Regular feedback sessions using tools like MyDatabilities or similar platforms can spotlight areas needing revamping, ensuring literacy programmes remain agile and responsive.

In-depth exploration of continuous improvement methodologies is crucial for mature data literacy practices. Books like "The New Continuous Improvement" by Gerhard Plenert offer frameworks for institutionalizing this approach. Some caution that without embedding these assessment routines into the organisation's culture, there's a risk of losing sight of long-term literacy goals. An ongoing dialogue, therefore, becomes essential to balance immediate needs with future aspirations.