The Role of Big Data Analytics in Industry 4.0
Did you know that all manufacturing processes generate about a third of all data today? With the recent rise of digital manufacturing this percentage will likely continue to increase in the coming years. Industry 4.0 could be viewed as the next phase in digital manufacturing, and it is driven by several main technologies: (i) Internet of Things (IoT) and astonishing amounts of generated data; (ii) physical connectivity and integration; (iii) Big Data Analytics enabled by computational power; (iv) augmented-reality systems and (v) advanced robotics. Industry 4.0 promises higher productivity, rapid innovation, reduced costs, and improved customer satisfaction. However, the adoption has been very slow. The main reason is probably that many manufacturing companies still lack the foundational technology infrastructure that must be in place before they can fully exploit Industry 4.0.
Making Data Science: AIG, Amazon, Albertsons
Haftan Eckholdt, PhD
Developing an internal data science capability requires a cultural shift, a strategic mapping process thataligns with existing business objectives, a technical infrastructure that can host new processes, and an organizational structure that can alter business practice to create measurable impact on business functions. This workshop will take you through ways to consider the vast opportunities for data science to identify and prioritize what will add the most value to your organization, and then budget and hire into commitments. Learn the most effective ways to establish data science objectives from a business perspective including recruiting, retention, goaling, and improving business.
The talk will offer practical advices how to collect and effectively organize enormous amount of industrial data from various siloed sources into a cloud data lake and then unleash the full power of advanced analytics to help benefit manufacturing companies. The speaker will also cover a few use cases how machine learning and AI helped digital manufacturing organizations. The first one is predictive maintenance, where equipment with IoT is remotely monitored to early predict its failures, diagnose the root cause of the faults and predict equipment remaining useful life. The second one is supply chain management, where real-time machine learning is used to track the location of assets in transit, predict when shipments will arrive and provide end-to-end visibility of goods throughout the supply chain.
The DataOps Manifesto
Christopher P. Berg
The list of failed big data projects is long. They leave end-users, data analysts and data scientists frustrated with long lead times for changes. This presentation will illustrate how to make changes to big data, models, and visualizations quickly, with high quality, using the tools analytic teams love. We synthesize DevOps, Demming, and direct experience into the DataOps Manifesto.