The role of AI in data democratisation
MJ Scholtz, Data Engineer at PBT Group
Data democratisation has become an important trend this year as organisations integrate artificial intelligence (AI) into their data lifecycle. I have seen how AI and data democratisation are transforming how companies access and use their data.
One of the benefits of AI in this regard is its ability to automate complex data analysis tasks. This automation makes it easier for non-technical users to extract meaningful insights from large datasets. The result is an improved end-user experience while also enabling the organisation to make more accurate, data-driven decisions.
Even so, it is crucial for companies to avoid becoming over-reliant on AI as it can have a negative impact on employees’ creativity. AI is there to enhance our thought processes and analytical capabilities, not replace them.
Improving internal processes
However, AI can help local businesses reduce the technical skills gap. Most AI tools offer an intuitive platform that introduces users to business data. These tools enable employees to explore and discover data without needing extensive technical skills.
AI can also open up the reporting structure within organisations. Traditionally, reporting is hierarchical and often limited to what a Business Intelligence (BI) specialist or director deems necessary. AI provides a broader view of the data, enabling users to explore what is available and make data-driven decisions at various organisational levels. This inclusivity is a significant step towards true data democratisation.
Even so, it does increase the risk of potential breaches or misuse of sensitive information. Companies must therefore ensure that data democratisation efforts comply with privacy regulations while extensive security measures are essential.
AI should be seen as a tool that offers insights into improving data and identifying potential outliers that might not conform to data standards. For example, AI can analyse and suggest corrections to address information in foreign countries where employees might lack the technical expertise or regional knowledge. This capability provides a more reliable source of information than manual data analysis.
Maintaining oversight
Despite its impressive speed and evolving capabilities, there are still limits when it comes to AI’s accuracy and reliability. AI tools can only process the information they access, leading to potential data misinterpretation or bias in certain algorithms. This risk underscores the importance of understanding an AI platform’s limitations before relying on it for decision-making.
Without proper oversight and continuous monitoring, the democratisation process can exacerbate potential biases, leading to significant organisational impacts. We must also remember that no machine can think as creatively as people do. If we rely solely on AI to solve problems, we risk stifling human creativity and innovation.
Being responsible
To mitigate these risks, companies should implement transparent AI practices, provide adequate user training, and establish strong governance frameworks. A robust data governance framework ensures that data is accurate, consistent, and accessible only to authorised users. In turn, this will instil confidence in the insights gained from the data.
The goal of data democratisation is to foster a better understanding of business data and allow employees to contribute to insights and data accuracy. AI has a significant role in this process, but we must ensure that it enhances our thought processes rather than replacing them.
