Big Data and Analytics: Why You Need a Platform

When organizations need to move quickly to launch new, digitally transformed applications that rely on terabytes (if not petabytes) of data, they cannot afford to wait for legacy database management systems to catch up. Cloud computing platforms give organizations the ability to stand up systems quickly, but if the data layer cannot offer the linear scalability, low latency, high availability, performance, fault tolerance, security, and agility needed for today’s 24/7 applications, organizations will never realize their objectives.

According to PwC, nearly 4 out of 5 CEOs believe that remote collaboration will last after the pandemic. And Gartner predicts that through 2025, 80% of organizations trying to scale digital business will fail because they don’t take a modern approach to data and analytics governance.

The evolution of sensors, smart phones, medical devices, and wearables all collecting and uploading information in real-time has led to the rapid accumulation of health-related data, or Big Data. The sheer volume, velocity, and variety of the data being collected poses challenges for harnessing and ensuring its validity to benefit both the macro, population-level health and the micro, evidence-based precision medicine. Join Dr. Eric Louie, Chief Medical Officer, and Jessica Baker, Associate, as they discuss changes in healthcare data over time and how to overcome human biases, curate meaningful data sets, and better inform actionable decisions in the healthcare setting.

In this webinar we will discuss a more modern view of the data lake and consider best practices for planning and implementing a scalable enterprise data lake. The flaws in early data lakes were often rooted in the expectations of data consumers who put a premium on self-service data analytics. However, with no data governance mechanisms, data lakes quickly became more of a glorified “dumping ground,” “data swamp,” or “beta lake” for organizational data.In recent years, though, some innovations have allowed the data lake to evolve into an agile yet managed environment for accumulating shared data resources that can be optimally used for competitive advantage. Data lakes have evolved beyond the original on-premises concept based solely on Hadoop and now include pretty much any distributed computing platform (Hadoop, Spark, EMR, serverless, etc.) and any storage mechanism (HDFS, S3, ADLS), either on-premises or in the cloud.