Powering Artificial Intelligence with Data: Why Quality Matters.

Nowadays, artificial intelligence (AI) has positioned itself as a ubiquitous and transformative force in diverse fields, sectors, and industries, such as finance, healthcare, manufacturing, and entertainment. AI systems are considered machine learning and deep learning models, that logically rely heavily on the data that is used to train and validate those models. In this sense, data constitutes the fuel that then powers AI and allows it to realize predictions, generate insights, and produce outputs of diverse and countless pieces of information.

Nonetheless, the sources of data that fuels AI do not have the same creation. Considering that the quality of data has a direct impact on the performance, accuracy, and reliability of these AI models. For that reason, a high-quality data allows AI systems to elaborate on better predictions and also be able to produce more reliable outcomes, and not only that, but also to foster trust and confidence among their users. Contrasting with the poor-quality data that can undoubtedly lead to flawed results, and also to poor performance and failure.

In this article, we will be exploring the crucial role that data quality plays in AI models, the challenges that worldwide organizations face, and some best practices for ensuring top-notch data.

What are we saying about data quality, and why does it matter in AI?

First and foremost, what is data quality? Well, let's dive into the details. The association between these words or terms refers to a measure of how well the data meets the requirements and expectations of the intended use. This quality can be measured and evaluated by being supported on diverse dimensions, like accuracy, completeness, consistency, timeliness, relevance, its representation, etc. The quality of the data used in the AI systems is essential, knowing that it directly affects the outputs and value of these systems. Between the benefits of high-quality data in AI, we encounter the following:

• Better prediction and decisions: high-quality data allows these models to learn from relevant and reliable information with the objective of elaborating on accurate and confident predictions and decision-making processes. This could be exemplified in the healthcare arena, knowing that high-quality data can help these models diagnose diseases, suggest treatments, and incredibly monitor patients' conditions.
• More ethical and fair outcomes: In this area, high-quality data could provide unparalleled support to address biases in the data used, something that is crucial to prevent perpetuating and amplifying these biases in the AI-generated results. Exemplifying this, we can use the case of hiring, where high-quality data can provide matchless support to these models to minimize discrimination and promote diversity and inclusion.
• Elevated generalizability and strong performance: In this camp, high-quality data can enhance an AI model's ability to generalize fantastically across diverse and countless situations and inputs, ensuring that its performance and relevance across the contextualization of those situations and the corresponding user groups stay strongly elevated. For instance, in natural language processing, high-quality data can help splendidly facilitate the understanding of the AI models and be able to generate coherent and natural language across diverse domains and languages.

What about the challenges of ensuring the quality of the data in AI models?

Independently of the significant importance of data quality in AI, countless organizations find themselves facing diverse challenges with the objective of ensuring and maintaining that the data of their AI projects and operations stay at high standards. Between those challenges, we can encounter:

• Lack of data standards and governance: various firms lack consistent and clear data standards and government policies to be able to define clear and robust quality criteria, roles, and responsibilities for the data collection, processing, and management processes. Something that can undoubtedly lead to data silos, duplication, inconsistency, and lastly, but not least important, incompleteness.
• Difficulty in data labeling and annotation: Diverse AI applications demand annotated and labeled data, like in the case of images, text, and audio, in order to train and validate their AI models. However, these data processes—annotation and labeling—can certainly be tedious, time-consuming, and error-prone processes, more precisely when realized manually or by the implementation and use of different sources. Something that can result in issues with the quality of the data, like ambiguity, noise, and bias.
• Complexity and diversity of data sources and types: These AI systems normally require dealing with challenging and diverse data types and sources, as is the case with structured, unstructured, and semi-structured data; that comes from internal or external sources, like databases, sensors, social media, and web pages, among others. Something that can pose undoubtedly complex challenges for data integration, transformation, and analysis of it, but also fundamental aspects like the security and privacy of the data.

What, in my opinion, could be the best practices for ensuring the quality of data in AI models?

Overcoming these challenges and complexities is certainly, as I said, difficult, but there are ways to benefit from high-quality data in AI models, something that undoubtedly leads organizations to adopt and implement best practices with the clear objective of ensuring and improving the quality of the data throughout its lifecycle. In my opinion, some of the best practices are:?

• Constitute or implement data standards and governance: In this area, it is evident that organizations need to establish consistent and clear standards for the data used and governance policies that conduct them to define objectively the quality criteria, roles, and responsibilities for the corresponding process of the data, such as collection, processing, and management. From my perspective, this can undoubtedly support them in ensuring the quality, consistency, and accountability of the data across their organizations and its stakeholders.
• Leverage data quality tools and techniques: in the AI models scenario, in my opinion, the organizations need to leverage the tools and techniques for quality aspects of the data that they could be using, such as data profiling, cleansing, validation, and monitoring, with the objective of improving and maintaining the quality of the data at the highest possible level. This could be realizable by insisting on the highest possible standards, but without forgetting to deliver results appropriately and in a timely manner. These kinds of tools and techniques can help organizations identify, correct, and prevent quality issues, like errors, outliers, missing values, and duplicates.
• Use data-centric AI approaches: These kinds of approaches, in my opinion, are valuable, knowing that organizations require to use these approaches, like augmentation, synthetic data generation, and active learning, with the objective of enhancing and optimizing the data that is used in AI systems. In this sense, these approaches can provide unparalleled help to maximize the quantity, quality, and diversity of data, while minimizing the cost and effort of data labeling and annotation processes.

Conclusion

In the end, data quality implies and represents a fundamental factor that can be considered as a key to the success and value of AI systems and their corresponding models. Knowing that high-quality data allows these models to elaborate on better and more optimized predictions and also produce more reliable outcomes that logically fosters confidence and trust among the different and diverse users that make uses of them. Nevertheless, ensuring the quality of the data in artificial intelligence is not an easy task and logically requires careful planning and execution.

For the aforementioned reason, it will be highly recommended and suggested to the organizations to try to implement the best possible practices with the objective of ensuring and improving the quality of the data throughout its lifecycle, as is the case in establishing the highest possible data standards and governance, leveraging the tools and techniques to maximize the quality of the data, and employing data-centric AI approaches. I strongly believe that if the organizations do that in that way, they will unleash the full power of data in AI, drive innovation and efficiency, and allow them to growth in their respective industries and markets.

References and Recommended Readings

1. S, Brown. Why it’s time for ‘data-centric artificial intelligence’. MIT Sloan. 2021 Mar 10 [cited 2024 Feb 28]. Available from:https://mitsloan.mit.edu/ideas-made-to-matter/why-its-time-data-centric-artificial-intelligence
2. AIMultiple. Data Quality in AI: Challenges, Importance & Best Practices. 2021 Jan 8 [cited 2024 Feb 28]. Available from: https://research.aimultiple.com/data-quality-ai
3. Information Matters. Why Does Data Quality Matter? How to Evaluate and Improve Data Quality for Machine Learning Systems. 2022 Sep 15 [cited 2024 Feb 28]. Available from: https://informationmatters.org/2022/09/why-does-data-quality-matter-how-to-evaluate-and-improve-data-quality-for-machine-learning-systems