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Data monetization: driving the new competitive edge in retail

CGI

Data monetization: driving the new competitive edge in retail. Retailers are fully aware that their future relies largely on their ability to use data efficiently. However, in today’s dynamic and highly competitive retail sector, retailers need to accelerate their plans and commit resolutely to the path of data monetization.

Retail 96
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Steelcase office furniture giant hit by Ryuk ransomware attack

Security Affairs

Steelcase is a US-based furniture company that produces office furniture, architectural and technology products for office environments and the education, health care and retail industries. It is the largest office furniture manufacturer in the world. Steelcase has 13,000 employees and $3.7 billion in 2020.

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Prometei botnet is targeting ProxyLogon Microsoft Exchange flaws

Security Affairs

The crypto-mining has a modular structure and employes multiple techniques to infect systems and evade detection. Prometei has been observed to be active in systems across a variety of industries, including: Finance, Insurance, Retail, Manufacturing, Utilities, Travel, and Construction.” ” concludes the report.

Mining 102
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Process Excellence: A transformational lever to extreme automation

IBM Big Data Hub

What do a Canadian energy company, a Dutch coffee retailer and a British multinational consumer packaged goods (CPG) company have in common right now? All are transforming their procurement operations by leveraging state-of-the-art process mining and intelligent automation technology. dollars annually in direct or indirect procurement.

Mining 66
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#ModernDataMasters: Mike Evans, Chief Technology Officer

Reltio

A passion of mine is imparting the knowledge and experience that the data leaders of today possess to the next generation of data professionals. For example, twenty or even ten years ago in food retail, focus on item cost was important, a description of the item and not much beyond that!

MDM 75
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Data science vs data analytics: Unpacking the differences

IBM Big Data Hub

This iterative process is known as the data science lifecycle, which usually follows seven phases: Identifying an opportunity or problem Data mining (extracting relevant data from large datasets) Data cleaning (removing duplicates, correcting errors, etc.) Diagnostic analytics: Diagnostic analytics helps pinpoint the reason an event occurred.

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Leaders need the technical detail

MIKE 2.0

Good examples of changes that are coming with more that is unknown than known include cyber currencies, blockchain, quantum computing, artificial intelligence, smart cities, augmented reality and additive manufacturing. These are some of the technologies that are likely to drive big decisions for leaders in the coming years.