What’s Ahead in Data Management in 2019?

Brand new research published from Dresner Advisory Services digs deeply into the trends in 2018 around big data analytics. Where are organizations heading in 2019? How are analytic and data warehouse architectures evolving to enable faster and deeper self-service analytics and BI for organizations looking to create a competitive edge? How is public, private and hybrid clouds factoring into deployment decisions? What are the hottest open source projects from Apache Spark to Kudu, Kafka, Hadoop, and beyond?

Equipment downtime is a multi-billion-dollar problem which will only continue to grow with exploding sensor data. According to IDC, by 2018 a third of industrial companies will be disrupted by “Industrial IoT enabled competitors.” So how can companies monetize their IoT investments for higher operational efficiencies and productivity? Anomaly Detection and Prediction is the silver bullet that companies need to maximize their machine uptime and performance. Watch the on-demand webinar featuring the Dean of Big Data, Bill Schmarzo, Chief Technology Officer, Big Data at Dell EMC and Seth Page, General Manager and Head of Partnerships at Progress DataRPM, to learn how zero factory downtime can be a reality.

As businesses strive to advance digital transformation efforts, legacy architectures can be a significant obstacle to enabling the agility required to succeed in today's ever-changing data landscape. Many enterprises struggle with scaling the delivery of data and analytics to accommodate the growing array of data domains, users, and use cases. As a result, data mesh and data fabric architectures are on the rise with the aim of abstracting data management complexity, increasing data availability, and fostering greater collaboration.

Rapid Response Analytics (RRA), an application suite that consists of two elements: curated, modular data kits called DOS™ Marts and Population Builder, a powerful self-service tool that lets any type of user, from physician executive to frontline nurses and population health teams explore their data and quickly build populations without needing to know how to write SQL and data science code. RRA increases an analytics team’s productivity by up to 10x and reduces its time to develop analytics by as much as 90 percent. Analysts can spend more time focusing on key strategic analysis and less time on repetitive tasks that can lead to inconsistent results and a backlog of requests. Learning Objectives: Discover how RRA is like a meal delivery kit that allows you to take components and customize them to quickly tailor and deliver meaningful insights.