Investing in Data Analytics
summary
Claims that the era of zettabytes (10 ZB in 2016 rising toward 40 ZB by 2020) creates investable opportunities in Industrial IoT are grounded in three measurable drivers: ~193,000 public APIs, >50% of enterprise apps on cloud, and ~50 billion connected devices. Concrete gaps include analytical databases ingesting millions of events per second, lack of real‑time data warehousing and joint code+data versioning, cold‑start training, and slow anomaly→causal inference loops. Highest payoff is in decentralized edge decision‑making and fast correlation/causality tools; risks include weak data acquisition strategy, poor governance, and legacy infra dependence.
AI-written summary · about
chapters (19)
- 0:00Introduction
- 0:30The Zettabyte Era
- 1:00The Intelligence Era
- 1:50The Alpha End
- 2:10The Drivers
- 3:13Every Role Needs Data
- 4:05Data Science Collaboration
- 4:48Industrial IoT
- 5:02Industrial Analytics
- 5:52Industrial Analytics Framework
- 7:33Data Warehouses
- 8:29Hybrid Schemas
- 9:17Applications
- 9:59Decentralization
- 10:50Intelligent Solutions
- 12:03Correlation Efficiency
- 12:51Risks
- 13:48Summary
- 14:25Questions
related talks
32mThe Next Wave: Data, AI And Analytics In 2019 And Beyond
24mmaximizing the potential of Big Data Initiatives
09mHow the analytic economy is bringing value to all economies
28mInvesting in Data: Unlocking Tomorrow's Opportunities
45mInnovating with Data
43mHow Big Data is Powering the Internet of Things (IoT) Revolution
57mBig Data: Threat or Opportunity?
30mValue Engineering: How to Get Your Big Data Investment.....