03/07/2024 | Press release | Distributed by Public on 03/07/2024 14:24
Artificial Intelligence (AI) has the potential to transform industries and foster innovation. However, navigating the path to successful AI deployments can be quite challenging, leaving many organizations to wonder why their AI projects fail.
According to one Gartner report, a staggering 85% of AI projects fail. Several factors contribute to this high failure rate, including poor data quality, lack of relevant data, and insufficient understanding of AI's capabilities and requirements. These issues underline the importance of robust data management and precise strategic planning for AI projects, including cloud-based models and LLMs.
Data is the lifeblood of AI and machine learning (ML) projects. Without robust data, AI models struggle to produce accurate and reliable results. A NewVantage survey from 2024 highlights this issue, with 92.7% of executives identifying data as the most significant barrier to successful AI implementation. Moreover, a Vanson Bourne survey reveals that 99% of AI and ML projects encounter data quality issues. These statistics underscore the critical need for effective data management and monitoring solutions.
Data observability refers to the ability to monitor and understand the state of data systems. It involves tracking data quality, lineage, and performance across data pipelines.
Organizations can ensure the success of an AI project by monitoring the data's freshness, volume, distribution, schema, and lineage. Dynatrace offers comprehensive data observability features designed to significantly enhance the success of AI projects, for example:
However, we can't just stop at data observability. As already mentioned, data isn't the sole reason why AI projects fail. Insufficient understanding of AI's capabilities and requirements is also part of it.
AI observability involves monitoring and understanding AI models and systems to ensure they perform as expected in real-world applications. It includes:
The following diagram shows how AI Observability can detect and resolve degradation in an AI model's performance:
Data and AI observability work together to provide a holistic view of the system. Data observability concentrates on the pipeline and infrastructure, while AI observability delves into the model's performance and results.
Effective AI observability relies on feedback loops, which rely on data observability. Identifying data drift, for instance, necessitates a profound comprehension of the data pipeline and its lineage.
Discrepancies in AI performance are often rooted in problems within the data pipeline. Data observability aids in tracking these issues to their source, enabling the swift identification and resolution of problems affecting AI systems.
Ensuring compliance with regulations and governance standards is crucial. Data observability ensures data management aligns with industry standards, while AI observability ensures that models adhere to ethical guidelines and regulations.
Dynatrace equips organizations with the necessary tools for data and AI observability, helping build sustainable AI applications. By providing comprehensive monitoring and understanding of data systems and AI models, Dynatrace ensures that AI projects are based on high-quality data and that AI models perform reliably and transparently in real-world applications.
[CTA]Learn more about the Dynatrace AI Observability solution.[/cta]