What People Really Do With IoT and Big Data

In this 30 minute demo, you'll see a demo of how easy it is to connect to Yellowbrick data warehouse from MicroStrategy, query tables with billions of rows from a TPC-DS workload, and see results in milliseconds or seconds.

Staying competitive in today’s economy requires that organizations derive intelligent insights from data and make their processes automated, intelligent, adaptive, and self-optimizing. This is where artificial intelligence (AI) and machine learning (ML) play a central role in business transformation.

The economic model and elegance of cloud environments are motivating companies to assess their existing on-premises data warehouses and modernize their enterprise information environments. However, confusion about what is meant by “modernization” has led some to believe that “lifting and shifting” their on-premises implementation to a cloud environment is the default approach. In these cases, the results are mixed—while the system has technically been moved to a modern cloud platform, by no means is it modernized.

The Internet of Things, social media and enterprise content all have one thing in common: they generate vast quantities of data — ever-faster. So much so that picking out relevant information and assembling it into meaningful patterns is a tall order. These new data sources also present another challenge. They generate unstructured data - such as text and emojis - that don’t fit neatly into traditional database structures. This data can however hold valuable insights on important and hard-to-quantify concepts such as social sentiment.