A Rating of AI in 2024: Innovation Held Back by Poor Data Quality

We’re fast approaching the end of 2024, meaning we’re nearing the 2nd birthday of the first release of OpenAI ’s ChatGPT. This anniversary has caused us at United Techno to reflect. ChatGPT turning 2 harks the date of the theoretical LLM (Language Learning Model) ‘genie in a bottle’. However, AI’s integration into daily operations is still in infancy. Why is this? In one answer: The persistent challenge of poor-quality data.

Much like the learned biases we humans have in our own decision making, the true power of ‘Artificial Intelligence’ (or at least our rudimentary versions of it thus far) lie on the accuracy and integrity of the data it is trained on. No matter how advanced an algorithm may be, its output is only as reliable as what’s feeding it. Inaccurate, incomplete, or biased information undermines even the most sophisticated of models, leading to flawed insights and potentially harmful consequences.

This is obviously not a big issue when a model like ChatGPT gets a trivia answer slightly wrong, however we should look at the broader implications. Whether it's medical decisions made in healthcare, finance decisions on loans, or decisions made with public safety in mind; it’s easy to see the limitations of where current AI finds itself.

With that said, where have the advancements been of late; and what insights can we draw from this? This year has seen remarkable advancements in AI. Key trends have continued to shape its growth:

• Generative AI: Technologies like ChatGPT and MidJourney have become even more refined, integrating deeply into business operations. Given this, AI is now becoming instrumental in creating new content, handling customer service, and automating product development tasks. This flows on to the second and third trends.
• Automation and Efficiency: AI is automating complex processes across industries, from fraud detection in financial services to optimizing supply chain logistics in manufacturing.
• Predictive Analytics: AI’s predictive capabilities have matured, particularly in sectors like healthcare, finance, and marketing. These systems can forecast trends, behaviours, and outcomes with increasing accuracy, giving companies a competitive edge.

Despite these leaps forward, the Achilles heel of AI remains - bad data.

While AI is more powerful than ever, poor data quality significantly hampers its potential. Several challenges continue to plague AI systems:

• Bias and Inaccuracy: AI models trained on biased or inaccurate data produce skewed and unfair outcomes, especially in critical areas like hiring, law enforcement, and lending. The consequences can range from discriminatory practices to flawed business decisions. Cleaning and preparing data to eliminate bias is an ongoing challenge for organizations.
• Data Silos: Data fragmentation is another major issue. Many companies operate with data stored in silos, leading to inconsistent and incomplete data sets. This makes it difficult to train AI models that require vast, integrated, and high-quality data to perform effectively.
• Volume and Veracity: As businesses generate more data, the sheer volume makes it difficult to manage and ensure its quality. Without proper oversight, AI systems sifting through these vast quantities of data can end up producing inaccurate or misleading insights.

Clean Data: The Foundation of AI Success

To unlock the full potential of AI, companies in 2024 are recognizing that the quality of their data is paramount. More organizations are taking proactive steps to address this:

• Data Management and Governance: Businesses are investing in data governance frameworks, prioritizing the accuracy, consistency, and reliability of their data. By cleaning and organizing their data, they can ensure that AI models perform optimally.
• Data Provenance and Traceability: Tracking the origin and evolution of data has become a key focus. Understanding where data comes from and how it has been altered builds trust in AI systems and allows for more reliable outputs.
• Cross-functional Collaboration: IT teams and business units are working more closely than ever to ensure that data is clean and fit for purpose. By collaborating, companies can break down silos and maintain data quality across departments.

AI is revolutionizing industries, but poor data quality remains a significant barrier to realizing its full potential. As we edge towards the later stages of 2024, the importance of clean, well-structured data cannot be overstated. Businesses that prioritize data governance and collaborate across functions will be the ones that truly harness the transformative power of AI.

To fully leverage the power of AI, organizations must prioritize data quality now more than ever. Start by implementing robust data governance practices and fostering cross-functional collaboration to ensure clean, reliable data. Take the first step toward unlocking AI's full potential—because the future of your business depends on it.

At United Techno , we understand that clean, reliable data is the cornerstone of AI success. By investing in strong data governance and fostering collaboration across your organization, you can unlock AI’s full potential to drive innovation and efficiency. Partner with us to ensure your data is ready to power the next generation of AI solutions—because your business deserves nothing less.