The relevance of data literacy in the context of AI
Jan de Villiers, Head of Cloud Academy at PBT Group
Even though artificial intelligence (AI) provides organisations across all industry sectors with opportunities to improve decision-making and enhance operational efficiencies, its success is reliant on the availability and quality of data used. For AI to deliver on its potential, it requires access to vast amounts of high-quality, diverse data to train algorithms, identify patterns, and generate actionable insights. Simply put, if there is no quality data, then AI cannot succeed.
And herein lies the challenge. The availability and quality of data present significant obstacles to the widespread adoption of AI. Furthermore, the more specialised the application, the more difficult it becomes to source sufficient, relevant, high-quality data. In fact, some large AI companies are beginning to exhaust the supply of publicly available data, which impacts the ability to train models effectively.
Even though some organisations are trying to throw more data at the problem, without carefully curated, high-quality data, the output of AI systems can be ineffective. Proper data curation is invaluable. It directly impacts the quality of AI models and, ultimately, the insights they generate. AI systems can unintentionally perpetuate the biases present in their training data, leading to skewed outcomes that can have far-reaching consequences. Even though it is impossible to eliminate all bias, organisations must try to reduce it as much as possible to mitigate its effects.
Managing the skills gap
Data privacy and security also present challenges. AI systems often rely on personal data, raising compliance issues and concerns around data ownership. These challenges, combined with the rapid pace of AI and data technology advancements, have created a noticeable skills gap. Specialists in data need to continually upskill to keep pace with evolving technologies and the growing volume of data. For many, this constant demand can be overwhelming, especially as the types of data continue to expand while trying to reskill becomes a perpetual challenge.
In this context, data literacy has become a fundamental skill regardless of industry sector. Think of data literacy as the ability to read, write, analyse, communicate, and reason with data within its context. In an AI-driven world, data and AI literacy is essential to address the challenges associated with AI and data management. It empowers employees with the understanding of how best to advocate for improved data practices, contribute to data curation processes, and engage meaningfully with AI systems.
When employees possess a solid foundation in data literacy, they can help improve the quality of data that AI systems rely on. This workforce can identify and mitigate biases in datasets, fostering more responsible AI practices. Importantly, data literacy also promotes a shared language for discussing data-related challenges, such as biases and privacy concerns, which are becoming more prevalent as AI systems evolve.
Enhancing what is there
Despite what many decision-makers think, AI does not replace the need for data literacy. Instead, it complements it. A significant portion of AI literacy stems from having data literacy skills in place. Concepts such as data provenance and lineage – understanding where data comes from, its quality, and potential biases – become even more critical in the context of AI. Additionally, data literacy is evolving to include a stronger focus on data ethics. In this way, individuals can better understand how to engage with AI responsibly and transparently.
Companies that invest in fostering a data-literate workforce will be better positioned to innovate and leverage the full potential of AI. Data literacy ensures that employees can actively participate in the ongoing journey towards responsible AI implementation. It equips them with the knowledge to navigate complex data landscapes, contribute to transparent AI systems, and ultimately drive business success in the AI age.
The cornerstone of successful AI adoption will remain rooted in data. Organisations must therefore ensure a high level of data literacy. Those businesses that prioritise data literacy will have the advantage of allowing them to make more informed decisions, innovate responsibly, and build AI systems that truly deliver on their promise.
