Helping Manufacturers improve quality, productivity, and reliability by applying analytics – including AI and Machine Learning – to their shop floors and IoT data.
No one doubts the value of the diverse data that’s ﬂooding in from the Industrial Internet of Things (IIoT). And the industrial sector is well-positioned to take advantage of it, given its range of controlled and monitored data sources: production line equipment, sensors in products being used in the market, sales data and more. But it’s a challenge to manage this massive volume and variety of data – from generation and collection through aggregation, analysis, implementation and storage – and then connect these capabilities under an advanced IIoT strategy.
Along the way, many questions arise:
- How do you know what data to collect, and what data to act on or store versus ignore?
- How do you ﬁlter out noise in the raw data to capture valuable intelligence in a timely manner?
- What’s the value of the analytical life cycle and how do you capitalize on its potential?
- How do you use newfound intelligence to make decisions that drive better business performance and lead to competitive advantage?
As organizations adopt new models for agile IT, edge analytics and platform-based security, IIoT forces a fundamental rethinking of business and operational strategies. To be successful, industrial leaders need an edge-to-enterprise IoT analytics platform and a strategy that generates intelligence in lockstep with business needs.
This eBook highlights ﬁve steps that can help you turn IIoT data into competitive advantage. Along the way, it discusses how to craft a comprehensive IIoT strategy, what to look for in an edge-to-enterprise analytics solution and how to evaluate IIoT solutions. It also shows how SAS’ expertise in analytics and artiﬁcial intelligence (AI) – combined with Intel’s leadership in IIoT information architecture – can turn raw data into rich insights and position you for astonishing results.