A new wave of tech startups armed with AI technology is emerging, promising to address the challenges of the modern world. However, as the AI landscape continues to grow, concerns about the lack of differentiation among AI models are being raised. It is becoming evident that the true competitive advantage of AI companies lies not just in their models, but also in the quality and depth of their datasets.
The value proposition of AI companies now lies predominantly in the underpinning datasets, not just in their models.
Many AI-driven companies, including those in the biotechnology sector, are rushing to launch without a purpose-built technology stack to generate the necessary data for robust machine learning. This oversight could have significant implications for the long-term sustainability of their AI initiatives.
As experienced venture capitalists (VCs) know, it is not enough to simply assess the appeal of an AI model at a surface level. A thorough evaluation of the company’s tech stack is necessary to determine its suitability for the intended purpose. The absence of a well-crafted infrastructure for data acquisition and processing could signal the downfall of an otherwise promising venture.
In this article, we will explore practical frameworks derived from hands-on experience as both a CEO and CTO of machine learning-enabled startups. While not exhaustive, these frameworks aim to provide additional guidance for the assessment of companies’ data processes, the quality of their data, and ultimately, their potential for success.
Assessing data quality: What could go wrong?
Before delving into the frameworks, it is important to understand the basic factors that come into play when evaluating data quality. It is crucial to consider what could go wrong if the data is not up to par.
The first factor to consider is the relevance of the datasets. It is essential that the data aligns intricately with the problem the AI model aims to solve. For example, an AI model designed to predict housing prices requires data that encompasses economic indicators, interest rates, real income, and demographic shifts. In the context of drug discovery, experimental data must exhibit high predictiveness for effects in patients, necessitating careful consideration of relevant assays, cell lines, and model organisms.
Another critical aspect is the accuracy of the data. Even a small amount of inaccurate data can significantly impact the performance of an AI model. This is especially crucial in fields like medical diagnoses, where a minor error in the data could lead to misdiagnosis and have potential consequences for individuals’ lives.
The third factor is the coverage of data. It is important that the data includes all the necessary information for effective learning by the AI model. If important information is missing, the model will not be able to learn as effectively. For example, in the case of an AI model used for language translation, the data should include a variety of dialects to ensure accurate and comprehensive translation.
In conclusion, venture capitalists evaluating AI startups must go beyond assessing the appeal of the AI models and delve into the company’s tech stack. The value proposition of AI companies lies not just in their models but significantly in the quality and depth of their datasets. Assessing data quality in terms of relevance, accuracy, and coverage is crucial in determining the potential success of an AI startup.