Big data and business analytics have become a booming industry. Between 2015 and 2022, the global big data and analytics (BDA) market more than doubled from $122 billion to $274 billion, and continues to grow. Innovations in AI, and the sheer volume of data that businesses generate from their day-to-day operations, has made “big data” something that even small and medium-sized enterprises can leverage to their advantage. In fact, some might argue that data analytics isn’t a luxury, but a necessity. According to PwC, organizations that adopt a data-driven approach outperform their competitors by 6% in profitability and 5% in productivity.
by Spencer O’Leary
However, leveraging big data and analytics isn’t just a question of deploying the right technology – it’s also a question of knowing how to use it. As businesses increasingly rely on data to guide their strategies, drive decision-making, and maintain a competitive edge, the gap between those who are data-literate and those who are not is becoming more pronounced. In a 2024 survey by Gartner, it was revealed that poor data literacy is among the top five roadblocks to data and analytics success, with staff shortages, skills gaps, budget constraints, and challenging company culture also inhibiting the rollout of BDA strategies. 
This divide isn’t just a minor inconvenience. It’s a significant barrier to effective decision-making, leading to misguided strategies, missed opportunities, and ultimately, diminished business performance. Data illiteracy, the inability to read, work with, analyze, and argue with data, is a critical issue that modern enterprises cannot afford to ignore, no matter their size.
This challenge is further compounded by the rapid evolution of data tools and analytics, which have grown more complex and sophisticated over time. Many employees, even those in decision-making roles, struggle to keep pace with these advancements, leaving them ill-equipped to fully harness the power of the data at their disposal. What’s more, inconsistent data practices across different departments create additional layers of confusion, as employees grapple with varied data definitions, formats, and standards. In a recent Censuswide survey commissioned by ActiveOps, we found that a staggering 90% of operations leaders say “too much effort” is needed to extract meaning from the data because so much of it is siloed and fragmented across the organization.
Without a strong foundation of data literacy and standardized practices, organizations risk falling into a cycle of poor decision-making, where critical business choices are made based on incomplete, misunderstood, or misinterpreted data. So, what’s the cause? And what can be done about it?
Getting to the root of the issue
Data illiteracy isn’t a singular issue. Lots of challenges converge to make data illiteracy a threat to business success; it’s not necessarily down to how well-trained or competent members of the workforce are. Tools evolve, training practices become outdated, and inconsistent data can give employees “data blindness” which no amount of training can fix.
Lack of Training and Education: One of the most significant contributors to data illiteracy is the lack of adequate training and education. Many organizations fail to provide their employees with the necessary skills to work with data, often assuming that these skills are either innate or can be picked up on the job. This oversight leaves employees unprepared to handle the complexities of modern data tools and analytics, leading to a significant gap between the data they have access to and their ability to utilize it effectively.
Inconsistent Data Practices: Another major factor is the inconsistency in data practices across departments. When different teams use varying definitions, metrics, and data standards, it creates confusion and hampers the ability of employees to interpret and act on data correctly. This inconsistency not only leads to errors but also undermines the development of a cohesive data strategy, as employees struggle to reconcile conflicting data inputs. This is where the nuances of data literacy become apparent – it’s not an employee-specific issue, but an organizational one.
Complexity of Modern Data Tools: The rapid advancement of data tools and technologies has outpaced the ability of many employees to keep up. These tools, while powerful, are often complex and require a deep understanding of data science and analytics to be used effectively. For employees who lack this expertise, the tools can become a source of frustration rather than empowerment, exacerbating the issue of data illiteracy and leading to underutilization of valuable data resources.
The Impact on Decision-Making
When employees struggle to interpret data correctly, it often leads to misguided decisions that can steer the business off course. Not only can this impact efficiency and the company’s bottom line, it can lead to countless missed opportunities because the intelligence that should have been derived from data in order to pivot or move the business in response to market changes simply isn’t there.
It can also have a negative impact on morale and culture within the organization. Inconsistent data practices across departments create a chaotic environment where errors and confusion proliferate, making it difficult to establish a cohesive strategy or build trust in the data being used. This lack of confidence in data-driven decisions can lead to hesitation and inaction, and make any effort to incorporate new data-driven strategies even more difficult. If teams have no inherent trust in the data they are working with, they’re far more likely to push back on any new data-driven initiatives.
Combatting Data Illiteracy
There is no silver bullet to combat data illiteracy, because it’s so difficult to quantify. It isn’t a binary state; there is no bar to reach or box to check to say that your business is now data literate. It’s about ensuring that your team and your technology can work together harmoniously, and that the data environment is well-groomed and able to deliver on your analytical goals. For that reason, a multifaceted, holistic approach that focuses on continuous improvement is the way to go.
First and foremost, investing in comprehensive training programs is essential. Organizations must prioritize ongoing education to ensure that employees at all levels develop the necessary data skills. This training should not only cover the technical aspects of data analysis but also focus on how to interpret and apply data insights to make informed decisions. By equipping employees with these skills, organizations can empower them to use data confidently and effectively in their day-to-day roles.
Standardizing data practices across the organization is another crucial step in bridging the data skills gap. By creating consistent definitions, metrics, and processes, businesses can reduce confusion and ensure that all employees are working with the same understanding of the data. This standardization not only simplifies data interpretation but also fosters a more collaborative environment, as teams can more easily share and compare data without the risk of misinterpretation or conflict – this is where many businesses falter. Even the best trained teams cannot work with irrelevant, misclassified data.
Finally, simplifying modern data tools and analytics can make them more accessible to a broader range of employees. While advanced data science tools are powerful, they can be intimidating and difficult to use for those without specialized training. Organizations should consider adopting user-friendly data platforms that offer intuitive interfaces and guided analytics, making it easier for employees to interact with and analyze data. Additionally, providing support resources, such as tutorials, chatbots and help desks, can further ease the learning curve and encourage wider adoption. By focusing on these solutions, organizations can and will bridge the data skills gap, enhancing data literacy, and ultimately improving the quality of their decision-making processes.
Spencer O’Leary is CEO, North America at ActiveOps.
Photo by Adrien on Unsplash
