WHY MACHINE LEARNING IS THE FUTURE OFPREDICTIVE AND INDUSTRIAL MAINTENANCE

July 10, 2020

There’s no arguing that preventing failures and accidents is critical for industry. Unexpected incidents can grind operations to a halt for extended periods of time and necessitate expensive repairs. Just 12 hours of downtime for an oil production platform could cost six to eight million dollars in lost production opportunity alone. A single day of grounding for a plane costs roughly four to five million dollars. Because of these disruptions, industrial sectors are always on the lookout for newer, better maintenance methods, and the approach on everyone’s lips right now is predictive maintenance. While everyone agrees on the name, there is less consensus on what it means or how to implement it. But to truly unlock the potential of predictive maintenance, it needs to be paired with artificial intelligence (AI) and machine learning (ML).

Spotlight

Teradata Aster

eradata Aster is a market leader in big data analytics, enabling advanced analytics on big data with richer, deeper data processing at ultra-fast speeds, massive but cost-effective scaling, and the ability to seamlessly manage diverse workloads. From applications like fraud detection, customer intelligence, trending & forecasting to scenario modeling, customer personalization and targeting, and click stream analysis – it is evident that enabling big analytics and discovery has a material impact on the business.

OTHER WHITEPAPERS
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Four Proven Steps to Integrating Threat Intelligence for Higher-Fidelity Detection and Response

whitePaper | July 20, 2020

Accurate, trustworthy threat intelligence is a boon if you have it – but too much of it becomes a management headache. Analyst group 451 Research, surveying security leaders for its report Tackling the Visibility Gap in Information Security, found that 49% of enterprises using SIEM, EDR, and other security tools were overwhelmed by the day-to-day operation of managing and ingesting threat feeds into their growing technology stack.1

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Powering the modern life insurance carrier with advanced AI and data

whitePaper | December 27, 2022

Welcome back to our white paper series about how to use AI and data throughout the new business and underwriting processes. In the first white paper we discussed the importance of product design and the benefits of using AI.

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Understanding Internal and External Network Threats

whitePaper | July 16, 2020

In the two years since GDPR came into effect there have been more than 160,000 reported compliance breaches, resulting in fines totaling over €144 million1 . Whilst the majority of these breaches remain the result of human error, cybersecurity has an increasingly important role to play in ensuring customers’ personal information is kept safe.

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Osterman Research - Improve Performance of Microsoft Office 365

whitePaper | July 31, 2020

Microsoft 365 provides organizations with a range of efficient, new-style productivity and collaboration tools that are widely embraced across the world. However, Microsoft 365 comes with some shortcomings in certain key areas, such as security, auditing, archiving, backup and recovery, data protection, and more. Relying solely on the native capabilities in Microsoft 365 can lead to challenges, such as missed security threats and the inability to recover accidentally deleted data – all of which can be costly.

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Recommendations on Updating the National Artificial Intelligence Research and Development Strategic Plan

whitePaper | March 30, 2022

The Stanford Institute for Human-Centered Artificial Intelligence (HAI) offers the following submission for consideration in response to the Request for Information(RFI) by the White House Office of Science and Technology to the Update of the National Artificial Intelligence Research and Development Strategic Plan.

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Cloud Infrastructures –Last Call for Boarding

whitePaper | July 16, 2020

Is cloud computing becoming the norm? As part of the digital offensives by many companies, cloud infrastructures are fast becoming a crucial technology driver and success factor for innovative business models and digital products. The growing interest in the Internet of Things and machine learning sent the adoption rate of the cloud soaring at numerous companies. Now, it is a question of “how” rather than “whether” cloud services can be used successfully at German companies.

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Spotlight

Teradata Aster

eradata Aster is a market leader in big data analytics, enabling advanced analytics on big data with richer, deeper data processing at ultra-fast speeds, massive but cost-effective scaling, and the ability to seamlessly manage diverse workloads. From applications like fraud detection, customer intelligence, trending & forecasting to scenario modeling, customer personalization and targeting, and click stream analysis – it is evident that enabling big analytics and discovery has a material impact on the business.

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