Many industries are seeing sweeping changes with the introduction of Artificial Intelligence (AI). But leaders often disregard one of the most important elements of a successful AI implementation strategy – and that’s the quality of data.
If you’re looking to leverage AI for business growth, understanding the importance of data quality is a non-negotiable first step. In this piece, we’ll get to grips with why the quality of your data can truly make or break the potential of AI in business. We’ll look into the problems that come with poor data quality, how AI can step in to fix them, and why having data products is a big deal when it comes to training AI with high-quality data.
First and foremost: Data Quality
Poor quality data, characterised by incomplete fields, mismatched formats, or unrelatedness to business objectives, can trigger a whole host of issues for successful AI adoption. These could range from inaccurate predictions and decisions to, more destructively, generating biased algorithms – based on gender or race, for instance – leading to harmful consequences for those affected and ultimately damaging company reputation.
Take 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 proficient at detecting liver disease in women as compared to men, highlighting an important disparity in their accuracy and effectiveness.
It’s not just organisations grappling with this issue. Consumers are also being affected. Bias within algorithms for insurance underwriting has been noted, as the UK Ministry of Housing‘s algorithm to determine house building allocation faced significant problems, and employees have also been affected by biased CV screening processes.
So, what’s the reason behind this? This occurs when the training data for AI is tainted with biases. Inevitably, these biases are inherited when the AI is fed dirty and low-quality data which then affects 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 (ML) that learns and improves over time. And businesses can achieve this by investing in robust data strategies to help create and maintain clean training data.
Solving the data quality challenge with AI
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 recognising and correcting errors. These data products are effective at filling in data gaps, removing duplicates, and ensuring data correctness and consistency, which maintains the data’s accuracy and reliability.
In addition, they can integrate data from different sources, transforming the unwieldy process of manual or traditional data cleaning into a streamlined, automated process. The role of human supervision in strengthening 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.
Recognising a data product
The term ‘data product’ can often create confusion. This leads to different understandings of its meaning. In a plain sense, 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. Sorted by business entities and governed by domain, data products are – simply put – the best version of data.
They are comprehensive, clean, curated, continuously updated data sets, aligned to key entities such as customers, patients or vendors, that humans and machines can consume broadly and securely across a business. Data products, powered by AI-driven efficiency with human oversight to provide feedback, play a vital role in the collection and management of data, guaranteeing its quality and reliability.
Establishing a solid groundwork
To become a part of the AI revolution, business leaders must first ensure data quality is at the heart of their AI implementation strategy. Without attention to the data which builds intelligent systems, there could be some very real risks to the real people who benefit from these systems, be those customers, staff, patients, or whomever else the system touches.
The ordered approach to mastering and amplifying data which AI-powered data products can offer will assist companies across industry in deflecting the dangers of skewed and biased AI models. With a solid groundwork established, teams can then concentrate their efforts on crafting cutting-edge applications tailored precisely to meet the demands of their end-users, ensuring powerful and impactful solutions for years to come.
Author: Suki Dhuphar, Head of International Business at Tamr
