Organisations need end-to-end solutions that combine data processing with machine learning capabilities. These streamlined solutions will simplify workflows, improve efficiency and ultimately accelerate business value.

Therefore organisations are moving data and its management to the cloud because the cloud’s benefits apply directly to data management, namely elasticity for high-performance analytics processing and big data scale, but with minimal administration and entry costs for cloud platforms and tools. These benefits contribute to the success of business-driven programs in analytics, data science, reporting, warehousing, and time-sensitive operations.

However, achieving these business and technology goals depends on creating a practical, sustainable, and optimized architecture for cloud data and its applications across BI, data science, and machine learning. There are many layers in a modern cloud data architecture, but two layers stand out because they determine success or failure: the cloud data platform and cloud data integration. In this session we'll discuss how these two layers work together to create a successful modern cloud data architecture.

As Simon Pilkowski, Global Lead Digital Performance Management at Boehringer Ingelheim, likes to say: "A fool with a tool is still a fool, but a fool with a process at least knows what to do next." And establishing a foundational, repeatable process is critical when governing marketing technology and data at scale.

Join Simon and Tim Baird, Director of Sales at ObservePoint, as they walk through how Boehringer Ingelheim fully automated their digital governance processes across more than 550 websites. Through the steps in their example, you will learn how to:

• Establish automated data governance across your digital properties
• Manage MarTech at scale throughout website mergers and updates
• Govern data collection for all your properties on an ongoing basis
• Use data governance to comply with privacy regulations