Data quality and preparation for AI/ML applications
7/10 High26% of AI builders lack confidence in dataset preparation and trustworthiness of their data. This upstream bottleneck cascades into time-to-delivery delays, poor model performance, and suboptimal user experience.
Sources
- What are some common issues developers face when using Azure AI?
- Top Challenges in AI Agent Development and How to Overcome Them
- AI Agent Development: 10 Top Hurdles and How to Overcome Them
- What are some common challenges faced by TensorFlow ...
- What's helping devs thrive...
- sendbird.com › blog › agentic-ai-challenges
- Behind the code: How developers work in 2025 - Help Net Security
Collection History
A significant challenge often faced is data compatibility. Developers must ensure that their datasets are clean and adequately structured for Azure AI to process them effectively.
Another challenge related to training set size is the need for extensive data preprocessing and augmentation. Large training sets often contain noisy or irrelevant data that can negatively impact model performance. Developers may need to spend more time and effort on cleaning, preprocessing, and augmenting the data to improve the quality and diversity of the training set.
New research reveals 81% of AI practitioners say their companies still have significant data quality issues, which put returns at risk. Common pitfalls include incomplete records, inconsistencies across departments, bias in sources, restricted access, and outdated information.
26% of AI builders say they're not confident in how to prep the right datasets — or don't trust the data they have. This issue lives upstream but affects everything downstream — time to delivery, model performance, user experience.