How AI Is Fuelling Data Democratisation

Data democratisation emerged as a key data trend for 2024. With many companies incorporating artificial intelligence (AI) into the data lifecycle and the use of it starting to grow exponentially, there is potential to combine these two components and transform how companies view and access their data. MJ Scholtz, Data Engineer at PBT Group, takes a closer look.

“Working across multiple platforms, tools, and environments, I have seen first-hand how companies are becoming hyper-focused on AI. One significant benefit is that AI can automate complex data analysis tasks, making it easier for non-technical users to extract meaningful insights from large datasets, enhancing end-user experience and assisting in quicker, more accurate data driven decision making. That being said, companies must be careful to not rely on it so much that they start to dampen their employees’ ability to think creatively. AI should be used as a tool to enhance our thought processes and analysing capabilities and not simply delegating these things to the technology,” says Scholtz.

Of course, AI is an important component of data democracy as it allows companies to break down many traditional barriers. For example, think of it as one of the most effective ways of reducing the technical skills gap in the company. Most AI tools provide users with an intuitive platform which introduces them to data within the business. Using these tools, they have a more accessible way to explore and discover data within the company without needing to have the technical skills of using databases or niche integration tools.

“Another way AI is achieving the move towards data democracy is by opening the reporting structure. Companies are traditionally hierarchical when it comes to reports. These are very use-case driven and often result in the Business Intelligence (BI) specialist or director putting the information in a report they think people might need. AI provides a broader view of the data as it allows users to explore what is available inside the business to make data-driven decisions at different organisational levels and become more inclusive in the process,” he says.

An improved analytical environment

While access to this data has become easier, the risk of potential breaches or misuse of sensitive information simultaneously grows. Ensuring that data democratisation efforts comply with privacy regulations and robust security measures is essential. AI can be hugely beneficial to helping companies meet and manage data privacy and security governance. For instance, AI can analyse sensitive information without requiring user input. This means the technology can analyse and mask sensitive information without needing user input and providing useful outputs. However, AI has little in the way of knowing how reliable or trustworthy that information is.

“Instead, AI should be seen as a useful tool to provide users with useful insights on how to improve data and identify potential outliers that might not conform to data standards,” says Scholtz.

He cites address information as an example.

“You might get a case where you have an address field that is somewhere in a foreign country. People may not understand in what format the postal or zip codes in those countries work. AI can analyse that and make suggestions based on the data without an employee needing the technical know-how or regional knowledge. AI therefore provides a company with a more reliable source of information than if they had to go through that data manually and analyse it themselves,” he adds.

However, Scholtz cautions that even though AI is impressive from a speed perspective and the way it is growing and evolving, there are still concerns about its accuracy and reliability.

“When making use of AI tools it is important to keep in mind that such tools can only process information they have access to, which means there is always a risk of data misinterpretation or bias in certain AI algorithms that can lead to flawed insights or decisions. Therefore, it is important to understand the limitations to an AI platform before relying on it for decision making. Without proper oversight and continuous monitoring, the democratisation process can add on to these biases, leading to significant organisational impacts. We must also remember that no machine will be able to think as creatively as people do. Even though AI may be quick to provide us with a solution, it might not always be the best solution in every scenario. If we stop thinking creatively and go to AI whenever we need to solve a problem, where are we headed as people?”

With that in mind, Scholtz recommends that companies implement transparent AI practices, provide adequate training for users, and establish strong governance frameworks to mitigate these risks.

Having a strong data governance framework will not only provide the end users with a better understanding of the data, but also ensures that data is accurate, consistent, and only accessible to authorised users. Additionally, it instils some confidence in the fact that insights, whether provided with the help of AI or not, can be trusted, enabling a culture of data-driven decision-making and innovation while safeguarding sensitive information and maintaining regulatory compliance.

 “The entire goal of data democratisation is to get a better understanding of data in the business and to allow people to contribute to insights and the accuracy of that data. AI has a significant role to play in this process. However, we must make sure that we do not become too reliant on AI, but rather allow it to facilitate our thought processes and not replace them,” concludes Scholtz.