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Accessing Kafka’s Real-Time Analytics Is Easier Than Ever

TIBCO - Connected Intelligence

Everything from temperature sensor tracking and machinery wear-and-tear to social media metrics and targeted online searches to fraud and forecast trends can be critical information to a successful modern business. . The messages carry real-time inputs, such as which players are on the field, the score, and even the weather.

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A perfect match: How tech promoted a small soccer team to the top

CIO

Partnering with a company of this type allows access to cutting-edge technologies and ideas used in the context of football and sports management.” TSG partnered with SAP to steadily steer its business toward its goal of becoming an intelligent and socially sustainable company,” says Mayer.

Sport 130
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Lumada Video Insights And DataOps Add Value to Video Assets

Hu's Place - HitachiVantara

Most retail businesses, transportation centers, sports facilities, and school campuses in London have cameras for surveillance purposes which are not counted in the 51,000 installed by the police. Smart edge devices and video intelligence platforms that gather data and insights from anywhere.

Video 45
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Top Data Science experts you should know about

Apiumhub

He has also been named a top influencer in machine learning, artificial intelligence (AI), business intelligence (BI), and digital transformation. She acts as a liaison between IT and business, to make complex data make sense and drive profits. Jen Stirrup is a top influencer in Big Data and Business Intelligence.

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After a record year for Israeli startups, 16 investors tell us what’s next

TechCrunch

Some people are more excited by emerging areas like artificial intelligence, which has been a focus of the country’s military for years, and like cybersecurity is now producing many fresh teams of founders. Any area that is exposed to mass physical engagement (pockets in travel, food, sports, etc.) are at risk.

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Analytics Maturity Model: Levels, Technologies, and Applications

Altexsoft

We will describe each level from the following perspectives: differences on the operational level; analytics tools companies use to manage and analyze data; business intelligence applications in real life; challenges to overcome and key changes that lead to transition. Ground level of analytics. Data warehouse architecture.

Analytics 102