Driving AI Literacy Through Data Democratization: Building a Data-driven Culture

The lack of data literacy is holding organizations back. Over the past decade, we have been hearing ad nauseum about big data and how much data organizations collect, but many employees don’t know how to handle it or what to do with it.

In a 2023 report by Data Camp, 78% of US leaders and 89% of UK leaders believe that data literacy is important for their team ‘daily tasks.

Source Data Camp

Data literacy is the ability to explore, understand, and communicate with data in a meaningful way. If employees are questioning and arguing about data, they are data literate. If they simply accept data as presented, they are not. If they are curious about data and consider its context, they are data literate; if they look at data in isolation, they are not. If they understand the value of data and can balance experience, data, and critical thinking in their decision-making, they are data literate. If they just go with their gut, they are not.

Surveyed leaders also believe accurate decision-making (63%), ability to innovate (48%) and the abilit create better customer experiences (41%) re some of the main values added by data-literate employees. These capabilities are important drivers of success and increased enterprise value.

Lack of data skills and education is one significant contributor to organizations' lack of data literacy, but so is the lack of access to quality data, tools, and resources.

Data Democratization and Data Literacy

Data democratization can be an essential driver in improving data literacy across your organization.

Data democratization is a strategy that reduces the technical knowledge required to access data. With fewer technical barriers, non-technical people can access data to help elevate their data IQ. In the same way that access to a library improves reading skills and stimulates curiosity, access to data can help employees better understand it.

Non-democratized data is locked in complex technical data stores with strict and broad access restrictions. To get access to data, data engineers need to build data pipelines and navigate governance policies. With these types of barriers, accessing data takes time and requires scarce data engineering resources. Employees interested in exploring data also must get authority to access it. With these barriers, getting data must be well thought out and deliberate. Accessing and combining multiple data sets just to explore trends and validate ideas do not justify the resources required to access and scrub the data.

A data democratization strategy provides data consumers with the tools and authority to access appropriate data sets. Data democratization also enables greater data sharing across different departments and data silos. With greater data sharing, employees can explore data they may not be as familiar with to drive greater understanding and peak curiosity. Data from different domains may not be presented in the same way or use the same terminology or calculate metrics uniformly. Exposure to these differences expands literacy. It also facilitates greater cross-domain discussions around data, challenging assumptions, and increasing learning.

While data democratization can have positive impact on data literacy, without effective training, the system could break down. Just like democracy as a form of government does not work well with and uneducated electorate, democratizing data also requires education and training. Data literate workers should understand the basics of statistics. They should understand the concepts of average, median, and standard deviation. They need to understand the difference between correlation and causation and signal vs. noise.

Greater access and training teach employees how to tell stories with data. With business experience combined with data literacy and access, employes can use data to drive narrative that changes minds and drives progress. This is where significant business value is created as resources can be more effectively invested to drive greater enterprise performance.

Changing employee mindsets and providing them with the tools to drive greater performance also makes them happier and more likely to stay with their firm. Research from Tableau found that 80% of employees are more likely to stay at a company offering data skilling programs.

With greater access, employees can be inspired to learn more, grow their knowledge base, and drive even more exploration and understanding. This feedback loop leads to knowledge, literacy, better decisions, innovation, and higher enterprise value.

Data literacy supports AI literacy.

With the emergence of ChatGPT, the capability of generative AI has hit the mainstream, but we still have a long way to go to figure out the best way to implement it to drive efficiencies while managing risk. To reach its full potential greater AI literacy is required.

AI literacy is the expansion of data literacy. Like data literacy, AI literacy is having the skill and competencies required to use AI applications and technologies effectively. While AI literacy includes additional professional competencies, it starts in the same place as data literacy with critical thinking skills.

With AI still a relatively new technology, a vast amount of confusion exists about how it works and what it is capable of. AI literacy requires the basic understanding of how AI works. This may not include the sophisticated math and statistics that drive algorithms, but data literate workers must understand the concepts behind the models.

Understanding concepts such as neural networks, decision trees and linier regression and knowing the strengths and weakness of each approach in solving specific problems are important skill to learn to become more literate. You don’t have to be able to build a liner regression model but understanding what it is capable and not capable is important.

Like data literacy, AI literacy also requires employees to understand how data is collected and processed and how this influences output. The role of synthetic data is another important concept to be aware of.

Understanding where AI models go wrong is also key to being AI literate. Having a firm grasp on how to identify biases in models to ensure that they are used ethically is important for effective deployments. AI models are constantly evolving and learning. In some cases, new data can cause models to drift and performance can be degraded. Date literate employees should understand this risk.

Employees who are data literate will be able to see beyond typical AI use cases and innovate. Data-literate employees can become AI-literate and learn how to scale the value they contribute and increase their productivity. A healthy dose of skepticism around data quality is paramount to ensuring that AI models don’t make big mistakes or amplify biases. Data-literate employees can understand the quality and nuances of data used to train data models and interpret their behaviors and outputs.

Data democratization and AI literacy

As AI becomes more commonplace, it will be very important that people are able to keep them in check. To ensure AI is working properly the more data Literate and AI literate employees you have in your organization the lower your risk that AI make big mistakes. With the majority of employees understanding how AI works and testing tool and data at their disposal and integrated into workflows will be a competitive differentiator in the emerging AI age. If every employee can test a model every time they get an output that seems a bit off, the potential that AI will make big errors will be reduced.

How humans and machines interact with data and work together will profoundly impact the performance of competitive organizations. The fewer barriers and friction between people and machines, the more opportunity there is to collaborate around data and drive greater organizational performance.

While data democratization can increase the productivity of your typical employee, it can also benefit highly educated data scientists. Data scientists can improve the performance of their models with discreet data sets that may reside across the organization. In many cases, when data is not democratized, accessing data in other data silos requires authorization and data engineering skills. Without a data democratization strategy coupled with a data discovery strategy, data scientists may not even know that a data set that could improve their model exists. Without access to all data, data scientists may resort to poor data sets with errors or biases that can degrade an AI strategy.

Driving AI and Data literacy

Integrating data democratization, literacy and training into your culture will drive better outcomes. Easy access to training is a simple way to help drive data and AI literacy. While requiring a bit more investment, deploying a data democratization strategy to enable easy access to data enables employees to use their skills to contribute to improved enterprise performance. To strengthen your data culture encouraging more experienced data scientists to take leadership roles in driving greater literacy thought out the organization. Facilitating data chats and sparking discussion and data story telling exercises can help build confidence in data and AI understanding.