As data volumes and data velocities increase, more and more decisions involving that data are being made using artificial intelligence and machine learning (AI and ML) technologies. However, operationalizing AI and ML models requires a dynamic, modern data architecture. Relying on traditional data management architectures that employ batch processing won’t be sufficient to deal with the demands AI and ML place on an organization’s data architecture.
AI and ML have become useful tools in the effort to improve an organization’s operations. Our research shows that the two most common benefits organizations report from their investments in AI and ML is that they gained a competitive advantage and improved the experience of their customers. They also report increasing sales and being able to respond faster to opportunities in the market. The algorithms used in these techniques are sophisticated enough to automatically sift through and analyze millions or billions of records to find patterns and anomalies. Performing these operations manually would be impossible to complete in a timely or cost- effective manner.
But AI and ML processing can place significant demands on an organization’s information technology infrastructure. First of all, large volumes of historical data are necessary to detect patterns accurately.
