The importance of data specialists in AI projects
Jeanne-Louise Viljoen, Data Engineer at PBT Group
Artificial intelligence (AI) is one of the most rapidly evolving technologies to arrive in the market in recent years. However, the technology on its own means very little when it operates in isolation from anything else in the business. AI has the potential to revolutionise all industry sectors, but the quality of the data that fuels the proverbial engine and the expertise of the team who manages these projects at the organisation is critical for success.
At its core, AI is about solving business problems. Whether it is optimising costs or automating complex processes. But herein lies the challenge. An AI-driven solution is not one that can be ‘set-and-forget’. Instead, it requires an integrated approach between the data and the human operator to ensure maximum benefit. Poor data quality can derail even the most sophisticated AI models. The result is inaccurate outcomes, biased decisions, and failed projects.
Data transformation
One of the critical challenges in AI projects is the transformation of raw data into a form that AI systems can use. Raw data, in its unprocessed state, is like crude oil. The potential is there, but it is not immediately useful.
This is where data specialists come in. They play a crucial role in transforming the raw data into something that can benefit the AI models transformation process. These specialists are able to extract, clean, and structure data. While many companies may be tempted to leave this process to AI to manage, it requires human expertise to navigate the nuances and complexities of data management.
Modelling considerations
Another vital aspect of AI projects is data modelling. This is where the quality of data directly influences the accuracy and reliability of AI predictions. These data models must be carefully designed and structured. Throughout the process, the models must be tested and refined. Again, this is where data specialists become indispensable. Their understanding of the data’s origin, its context, and its potential biases allows them to create models that are not only accurate but also ethical and unbiased.
Ethical considerations in AI cannot be overstated. This is especially important given how AI systems have become more integrated into decision-making processes. Ensuring these systems operate fairly and without bias becomes a prime directive. Data specialists must remain at the forefront of this as they put the structures in place to ensure that the data used in AI models is representative, unbiased, and ethically sound.
Human foundations
The human element in AI projects is irreplaceable. Even though AI systems may automate many aspects of business operations, they still require the expertise and insights of data specialists to function correctly. It is this collaboration between technology and human ingenuity that provides the secret sauce to delivering a successful AI project.
The success of any AI project comes down to the quality of data used as well as the expertise of the teams who manage it. The role of data specialists is a vital cog in this wheel to leverage advanced technologies like AI to deliver business efficiencies. Join me next time as I look at how to identify the right skills needed to assemble an effective AI team within your business.
