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Based in Seattle, Suplari aims to help enterprises better manage their procurement process by leveraging machine learning to drive cost savings. The startup does this by pulling together a company's spend data from across all its various systems, then running the data through Suplari to surface irregularities and inefficiencies. Per Nikesh Parekh, CEO of the Suplari, the benefits can be wide-ranging, including smarter spending, a reduction in risk, or the ability to drive better compliance.
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Two thirds of financial services executives say disruptive technologies will have a positive impact on their business .
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Machine learning is having a profound impact on many organisations. It is enabling functionality ranging from personal shopping recommendations to automated customer support through to financial fraud detection and compliance. Ninety per cent of CIOs using machine learning expect it to help them drive greater top-line revenue growth. By Joe Hellerstein, Co-Founder and Chief Strategy Officer, Trifacta and Jim Gray Chair of Computer Science at UC Berkeley.
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Research from Blue Yonder, a JDA Company, in collaboration with Microsoft finds vast majority of brick and mortar retailers rely on manual intervention for core business processes such as replenishment and pricing.
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GigaSpaces' InsightEdge has been selected by Magic Software Enterprises to power their Magic xpi end-to-end integration platform. This integration will enable companies to make faster and smarter data-driven decisions to boost revenues, reduce costs, and mitigate risks.
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New Capgemini report reveals that upskilling programs help fulfil automation’s productivity potential, saving organizations millions of dollars a year.
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Hear LinkedIn’s senior SRE, Todd Palino, share how the company continually improves the state of its infrastructure, so that the developers who are rolling out applications have a framework that they can do it within, and they can do it safely. LinkedIn currently generates over 50 terabytes a day of unique metrics on applications. No human is going to look at 50 terabytes a day of data and get anything useful out of it, so LinkedIn relies on systems give them some useful signal out of all that noise. By moving down the road of machine learning, LinkedIn can now do anomaly detection using machine learning models.
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