Data Privacy Using AI-Driven Data Catalogs

When you need to monitor and control variation in your process, a control chart will help you to separate special causes from common cause variation or random variation. Yet how do todays types of data, including full inspection, impact this tool's effectiveness? The control chart was created in the 1920s, a time when measurement, data capture and calculation were done manually, making sampling essential. Today, how data is collected has all changed.

Many organizations are immature when it comes to data and analytics use. The answer lies in delivering a greater level of insight from data, straight to the point of need. There are so many Data Architecture best practices today, accumulated from years of practice. In this webinar, William will look at some Data Architecture best practices that he believes have emerged in the past two years and are not worked into many enterprise data programs yet. These are keepers and will be required to move towards, by one means or another, so it’s best to mindfully work them into the environment.

Moving on-premises legacy data and analytics workloads to the cloud is unavoidable if you want to overcome infrastructural constraints, facilitate proactive analytics, and lower costs. You need a service that enables seamless scalability for petabyte-scale data processing, interactive analytics, and machine learning. However, end-to-end, automated workload transformation and optimization on serverless services is not straightforward.

As organizations seek to deliver new applications and services, they often also need highly scalable and lightning-fast data management platforms to support these innovations. For instance, some new applications work against millions or billions of rows of data and require a response time in milliseconds. These might include financial services applications that need to process transactions with sub-millisecond response rates and be globally available.