# Towards Unlocking the Hidden Potentials of the Data-Centric AI Paradigm in the Modern Era
## 7 Challenges and Future Prospects
In contrast, an automated diagnosis system employed for cancer disease may need rigorous data quality enhancement to ensure accurate diagnoses. Hence, it is very tricky to assess what amount of data quality is reasonable in a DC-AI approach when it comes to diverse applications. Therefore, developing unified tools/techniques that can provide a reasonable analysis of the data is a pending issue, and requires further investigation from the AI community. * There is a misconception about the DC-AI approach and traditional pre-processing. It is important to note that pre-processing is a common practice while building AI systems, and has significance. In contrast, DC-AI is a complete discipline that includes pre-processing as one of the aspects. Pre-processing is applied to data that have already been collected, whereas DC-AI is all about the data-first strategy--what data to collect, from whom to collect them, how to collect them, how to improve them, what aspects to improve, etc. Also, DC-AI includes data versioning, completeness, timeliness, risk/error analysis, etc., which are not part of pre-processing at all. Therefore, convincing AI developers to accept this new paradigm may be challenging. Hence, the amalgamation of DC-AI and pre-processing is imperative to highlight the need for DC-AI in future endeavors. * Another challenge is to find some potential/attractive use cases, like the detection of defects in steel, to pinpoint the efficacy/benefits of DC-AI ([[https://venturebeat.com/ai/why-data-remains-the-greatest-challenge-for-machine-learning-projects/](https://venturebeat.com/ai/why-data-remains-the-greatest-challenge-for-machine-learning-projects/)]([https://venturebeat.com/ai/why-data-remains-the-greatest-challenge-for-machine-learning-projects/](https://venturebeat.com/ai/why-data-remains-the-greatest-challenge-for-machine-learning-projects/)), accessed on 5 March 2024). In the future, making the DC-AI approach a de facto standard for AI applications imposes various challenges, because many AI systems have already been developed and deployed in real-world settings. In some cases, obtaining more data to improve performance or fiddling with AI models is beneficial when it comes to applications like voice-activated assistants. In contrast, obtaining more data or improving models when all the data belong to one source (e.g., defect detection in a machine based on its operating sounds) may not be beneficial. Hence, it is challenging to differentiate applications that require DC-AI versus MC-AI. In the future, identifying relevant domains where DC-AI can bring more good than harm is an exciting research area. * DC-AI is about debugging and compiling data, but in the absence of sophisticated tools, it is hard to identify the ambiguous parts of the data and fine-tune them accordingly. In addition, separating faulty and non-faulty parts of data that are enclosed in diverse formats (e.g., tables, graphs, trajectories, and images) is also very challenging. Therefore, the development of data debugging and compiling tools similar to programming languages (i.e., Java, C/C++) is imperative in the coming years. The development of such compilers can give valuable hints about the type/nature of problems/vulnerabilities in the underlying data that can be corrected at the earliest. * Recently, the umbrella of DC-AI techniques has been expanding (or being amalgamated with traditional pre-processing methods), posing the challenge of which technique might yield promising results when it comes to different datasets/applications. For example, simple visualization may help to identify missing labels in 100 tuples. In contrast, one cannot use simple visualization when it comes to image data of different species, or a billion images for one application. Hence, it is difficult to select suitable DC-AI techniques for each respective AI application. In this regard, the selection of optimal DC-AI techniques that can yield reliable results, regardless of the domain, is an attractive area of future research.