For Schneider Electric, any AI application that delivers tangible business outcomes is a breakthrough. Our goal is to turn data into actionable insights. Powered by the Microsoft Azure platform and Schneider Electric’s EcoStruxure™ Industry IoT architecture, SCADAfarm is an integrated automation and information management solution developed for WaterForce, an irrigation solutions builder and water management company in New Zealand.
Schneider Electric, in collaboration with AVEVA, and Microsoft
As a solution builder, WaterForce now can offer additional value-add services such as fault diagnosis and performance benchmarking, driving forward its own digital transformation.
For Microsoft, offering lifecycle management of AI is a true breakthrough as well. Microsoft solutions can significantly cut time wasted on cleaning up and preparing data, enabling data scientists to do what they do best: data science. Security and compliance workflows (e.g., safety and regulatory reviews) can be integrated between experimentation and deployment of models, thereby feeding a most-valuable AI loop.
Together, we're helping customers benefit from Schneider Electric's deep domain expertise and Microsoft's trusted, secure cloud.
1 Elfrink, Wim. “The Internet of Things: Capturing the Accelerated Opportunity.” Cisco Blog, October 15, 2014. http://blogs.cisco.com/ioe/ the-internet-of-things-capturing-the-accelerated-opportunity. Cited in World Economic Forum, in collaboration with Accenture, “Industrial Internet of Things: Unleashing the Potential of Connected Products and Services,” January 2015. http://www3.weforum.org/docs/WEFUSA_IndustrialInternet_Report2015.pdf
2 Technology and Innovation for the Future of Production: Accelerating Value Creation, World Economic Forum, March 2017, http://www3.weforum.org/docs/WEF_White_Paper_Technology_Innovation_Future_of_Production_2017.pdf
3 IDC, “Worldwide IoT spending in 2021,” IDC’s Semiannual Worldwide Internet of Things Spending Guide, 2H16 update, May 2017.
4 “How AI Boosts Industry Profits and Innovation,” by Mark Purdy and Paul Daugherty. Accenture Research, June 2017. https://www.accenture.com/t20170620T055506__w__/us-en/_acnmedia/Accenture/next-gen-5/insight-ai-industry-growth/pdf/Accenture-AI-Industry-Growth-Full-Report.pdf?la=en
5 Jeff Leek, “The key word in ‘data science’ is not data, it is science,” December 2013. https://simplystatistics.org/2013/12/12/the-key-word-in-data-science-is-not-data-it-is-science/
6 George Anadiotis, “Data to analytics to AI: From descriptive to predictive analytics,” ZDNet, November 23, 2016 http://www.zdnet.com/article/data-to-analytics-to-ai-from-descriptive-to-predictive-analytics/
7 Michael Schrage, “4 Models for Using AI to Make Decisions,” Harvard Business Review , January 27, 2017. Business Review. https://hbr.org/2017/01/4-models-for-using-ai-to-make-decisions
8 CrowdFlower, “2016 Data Science Report,” http://visit.crowdflower.com/rs/416-ZBE-142/images/CrowdFlower_DataScienceReport_2016.pdf
9 “Data janitors” used by Josh Wills, as cited in Jessica, Leber, “In a Data Deluge, Companies Seek to Fill a New Role,” MIT Technology Review, May 22, 2013. https://www.technologyreview.com/s/513866/in-a-data-deluge-companies-seek-to-fill-a-new-role/
10 Susan More, Gartner, “How to Create a Business Case for Data Quality Improvement,” January 9, 2017. http://www.gartner.com/smarterwithgartner/how-to-create-a-business-case-for-data-quality-improvement/
11 Rob van der Meulen, "What Edge Computing Means for Infrastructure and Operations Leaders," Gartner. October 18, 2017. https://www.gartner.com/smarterwithgartner/what-edge-computing-means-for-infrastructure-and-operations-leaders/