AI is a data-hungry beast, but its implementation remains a challenge due to issues with data quality, quantity, velocity, availability, and interaction with production systems. Companies struggle with AI data, resulting in obstacles in training, inference, and wider deployment, as well as low ROI. The reason why business AI proof of concepts (POCs) and pilots have a lower success rate (around 54%) is mostly due to data-related challenges. These challenges often stem from compliance issues, privacy, scalability, and cost overruns, which can negatively impact AI initiatives, especially as businesses rely on technology to achieve commercial and competitive gains.
The importance of data availability and AI infrastructure
CEOs and boards demand double-digit efficiency and income gains from AI expansion, making data’s stranglehold a strategic priority. Accessible and timely data is crucial for AI development, and an AI infrastructure that provides data and integrates with production IT is essential. This strategy focuses on data availability and seamless integration with corporate systems, enabling more reliable and usable AI apps and capabilities.
The importance of data in AI success and failure cannot be neglected
There are various barriers to AI research and expansion, including a lack of executive support, finances, projects, security threats, and workforce issues. Nonetheless, across sectors and areas, data-related difficulties remain the most challenging AI concerns. A Deloitte survey found that 44% of international businesses struggle to get information and inputs for model training and the integration of AI with internal IT systems. Data is critical for AI’s development and usefulness, yet it faces various challenges in its application.
1. There is a lack of data quality and observability: GIGO (garbage in/garbage out) is a serious issue in AI, costing companies an average of $12.9 million each year. Data observability is essential for maintaining data quality and a consistent flow of AI data. Notwithstanding the limitations of today’s bigger and more complicated models, a Gartner survey found that over 90% of respondents want to invest in data observability and other quality solutions. These solutions are critical for tackling AI’s data problem since they aid in the identification, correction, and improvement of faults in data, storage, computing, and processing pipelines.
2. Inadequate data governance: Data management is critical for AI development, yet it is frequently disregarded. Not conforming to standards and norms can hinder alignment with company goals and result in compliance, regulatory, and security risks such as data corruption and poisoning, which can result in inaccurate or hazardous AI outputs.
3. Insufficient data available: The most significant data obstacle to AI success is gaining access to data for constructing and testing AI models.
Strategies for data success in AI
- Due to a lack of complete data availability and delivery, AI development companies usually cause accessibility issues. Various groups have different needs for data, and issues become too complex and are resolved slowly and at great expense by the IT department. To prevent these problems, it is essential to take big-picture data accessibility into account from the start.
- Put your attention on the AI infrastructure that links the models and data of production IT systems. Focus on providing accurate data to models and systems on schedule. A cloud-based architecture created for AI unifies its creation and implementation throughout the whole company. McKinsey suggests moving R&D spending to develop infrastructure for mass production, scalability of AI projects, adoption of MLOps, and data model monitoring.
AI data is fed by a balanced, faster infrastructure
- Computational needs for the full data lifecycle, such as data preparation, processing, model development, training, and inference, must be taken into account while designing and deploying AI. The total cost of ownership (TCO) and return on investment (ROI) for AI initiatives are directly impacted by optimizing the computational infrastructure and performance. Applications that are built for GPU usage might make the most of processor resources because GPUs can be up to 50 times quicker than CPUs. Processing times may be 90% faster when data loading and analytics are simplified, for example, by leveraging GPU acceleration for Apache Spark queries.
- Storage for AI processes and I/O performance are essential, especially during the phases of data collection, preprocessing, and model training. Quick data reads and exchanges with storage media enable differentiated performance. Differentiated storage performance is critical to preventing GPUs from having to wait on I/O.
Networking and adjacent technologies are two other types of data that may be fed into the AI.
Conclusion
Businesses will succeed in creating and delivering apps despite AI-related data challenges by working on data integration and accessibility via enhanced cloud infrastructure for AI.
