IoT is no longer the “next big thing” – it’s here, real, and transforming the world. McKinsey estimates that 127 devices are connected to the Internet every second; Statista expects 75B devices to be connected by 2025; and Gartner predicts that by 2023, more than 50% of enterprise-generated data will be created and processed outside of the data center or cloud. Yet the adoption of IoT technologies by the industry is moving slower than expected. While this can be attributed to multiple factors, the capability (or lack of) to cope with “data deluge” – how to store and manage data efficiently as well as to make sense of them – stands as a key one.
To support efficient storage and management of the vast data collected from IoT devices, the market has created solutions on two fronts: cloud infrastructure and embedded storage in edge devices. Even though, challenges still linger as for how to best distribute and process the data in the spectrum of varying computing power, storage cost, power consumption, and communication latency in the IoT networks. This has to be optimized to meet the application requirements and system constraints. The emerging Distributed Cloud concept, which seeks to extend cloud infrastructure to the network edge to provide a uniform environment in which to manage data and deploy analytics, may help to address some of the challenges.
Data provides no value unless we transform it into actionable insights. To that end, IoT practitioners have been utilizing data analytics tools to unlock the value hidden inside the seemingly chaotic data vaults. And let’s not lose sight of the ultimate goal of IoT – to help make better decisions, whether it’s improving user experiences, optimizing manufacturing, or personalizing marketing campaigns. The advance in data analytics, including AI, has produced early encouraging results.
In this track, we will focus on the challenges and opportunities surrounding, but not limited to, the following themes: IoT data storage and management, distributed cloud, predictive analytics including AI and ML, data quality and trustworthiness, and data governance, data gravity and data privacy.
Chung-Min Chen, Lutron Electronics
Chung-Min Chen is currently with Lutron where he provides data science consulting in various business areas. Prior to that he was VP of Data Science at Sidecar Interactive, a startup leveraging machine learning in digital marketing, and Sr. Director of Data Science and Analytics at iconectiv (an Ericsson company) where he helped build Ericsson’s analytics products that enable telecom carriers to better understand and manage mobile user experiences. He had also held research and management positions with Bellcore, Telcordia, and subsequently Applied Communication Sciences.
His interests span across database systems, distributed and parallel computing, mobile computing, and machine learning. He has published over 50 papers in leading ACM and IEEE journals and conferences including JACM, TOSN, TKDE, TMC, TON, JSAC, SIGKDD, PODS, SIGMOD. He received a PhD in CS from University of Maryland, College Park and BS in CSIE from National Taiwan University.
Mark Cusack, Yellowbrick Data
Mark Cusack is the CTO at Yellowbrick Data, which offers a high-performance MPP data warehouse for the hybrid cloud. Prior to joining Yellowbrick, Mark was vice president for data and analytics at Teradata, with lead product management responsibility for the data warehouse and machine learning portfolio. During his six-year tenure at Teradata, Mark also led the product management function responsible for the data warehouse ecosystem of products, supporting data loading, data virtualization, near real-time streaming, monitoring and management, and application development. He was chief architect of Teradata’s IoT analytics effort, applying novel machine learning approaches to automate the management of workloads and the detection of anomalies based on real-time analysis of telemetry data. Mark holds a PhD in computational physics from the University of Newcastle, UK.