Why Data Quality is Essential to AI Success

By Suki Dhuphar, Head of EMEA at Tamr.

  • 6 months ago Posted in

Artificial Intelligence (AI) is driving sweeping changes across various industries. Yet, its successful implementation hinges on an often-underestimated factor - data quality. This article examines the intricate relationship between data quality and AI, unveiling how the former can make or break AI's potential. We dive into the perils of low-quality data, explore how AI can resolve these challenges, and delve into the importance of data products to provide you with the most accurate version of your data, enabling higher-quality training data. We also shed light on the role of humans in this AI-driven ecosystem. If you're looking to leverage AI for business growth, understanding the importance of data quality is a non-negotiable first step.

Data Quality: The Bedrock of AI

Poor quality data, characterised by incomplete fields, mismatched formats, or irrelevance to business objectives, can trigger a plethora of issues for successful AI adoption. These could range from inaccurate predictions and decisions to, more detrimentally, generating biased algorithms – based on gender or race, for instance - leading to harmful consequences for those affected and damaging a company’s reputation. Consider this example in healthcare: a study carried out at University College London UCL in July 2022 highlighted a significant gender bias in AI tools used for liver disease screening. The research findings revealed that the AI’s algorithms were less adept at detecting liver disease in women as compared to men, highlighting an important discrepancy in their accuracy and effectiveness.

It's not just organisations grappling with this issue. Consumers are also affected. Bias within algorithms for insurance underwriting has been observed, and even the UK Ministry of Housing's algorithm to determine house building allocation faced significant problems. Employees have also been affected, as seen in biased CV screening processes.

So, why does this happen? This occurs when the training data for AI is tainted with biases. Inevitably, these biases are inherited by the AI through the dirty data, and low-quality data they are fed affecting all of the AI’s outputs. AI that truly excels in performance is built on a foundation of exceptional data - data that has been cleansed using machine learning that learns and improves over time. And businesses can achieve this by investing in robust data strategies to help generate and maintain clean training data.

The AI Solution to the Data Quality Challenge

While poor-quality data can undermine AI, AI itself can also provide a solution to this problem. AI-powered data products improve low-quality data by identifying and rectifying errors. These data products are effective at filling in data gaps, eliminating duplicates, and ensuring data correctness and consistency, which maintains the data's accuracy and reliability.

Furthermore, they can integrate data from disparate sources, transforming the cumbersome process of manual or traditional data cleaning into a streamlined, automated process. The role of human supervision in enhancing data quality, working alongside the AI that’s powering the data products, is paramount. AI, with a human in the loop to provide feedback, results in sharper and more precise systems.

What is a Data Product?

The term 'data product' often creates confusion, leading to different understandings of its meaning. For clarity's sake, a data product is a consumption-ready set of high-quality,

trustworthy, and accessible data that people across an organisation can use to solve business challenges. Organised by business entities and governed by domain, data products are the best version of data. They are comprehensive, clean, curated, continuously updated data sets, aligned to key entities such as customers, vendors, or patients, that humans and machines can consume broadly and securely across an enterprise. Data products, powered by AI-driven efficiency with human oversight to provide feedback, play a crucial role in the collection and management of data, guaranteeing its quality and reliability.

Unlocking the AI Revolution

The integration of AI into businesses has the potential to completely overturn innovation within a wide range of sectors. Yet, at the core of this AI revolution, data quality emerges as the ultimate game-changer. The risks of building intelligent systems on faulty or biased data that's meant to reflect real-life experiences of customers, staff, and patients are substantial and tangible.

AI-driven data products come to the rescue, enhancing data accuracy. They introduce an organised approach to mastering and amplifying data, assisting companies in avoiding skewed and biased AI models. This lays the groundwork for teams to concentrate on creating exceptional and valuable applications tailored to meet the precise needs of end-users.

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