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Data Teams and Their Types of Data Journeys

DataKitchen

Data Teams and Their Types of Data Journeys In the rapidly evolving landscape of data management and analytics, data teams face various challenges ranging from data ingestion to end-to-end observability. It explores why DataKitchen’s ‘Data Journeys’ capability can solve these challenges.

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Digital twin helps NTT Indycar deliver better race experience to fans

CIO Business Intelligence

NTT, which partners with Penske Entertainment for the NTT Indycar Series, including the Indy 500 race, collected an estimated 8 billion data points through the sensors on Ericsson’s car and that of his 32 competitors. On a given race day, it is the second-largest city in Indiana,” Indart says.

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Digital twin helps NTT Indycar deliver better race experience to fans

CIO Business Intelligence

NTT, which partners with Penske Entertainment for the NTT Indycar Series, including the Indy 500 race, collected an estimated 8 billion data points through the sensors on Ericsson’s car and that of his 32 competitors. On a given race day, it is the second-largest city in Indiana,” Indart says.

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Operational Finance in the Age of Covid-19: Time to Change the Basics?

Jet Global

Not only have finance teams had to close companies’ books remotely, but they’ve also been required to provide the insight and information needed for some extremely complex decision-making, and continuously plan and forecast for events with little or no historical context. Tip 3: Make decisions with operational data.

Finance 98
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Data-driven competitive advantage in the financial services industry

Cloudera

Cloudera’s customers in the financial services industry have realized greater business efficiencies and positive outcomes as they harness the value of their data to achieve growth across their organizations. Data enables better informed critical decisions, such as what new markets to expand in and how to do so.

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Introducing watsonx: The future of AI for business

IBM Big Data Hub

At IBM, we believe it is time to place the power of AI in the hands of all kinds of “AI builders” — from data scientists to developers to everyday users who have never written a single line of code. A data store built on open lakehouse architecture, it runs both on premises and across multi-cloud environments.

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12 considerations when choosing MES software

IBM Big Data Hub

They can create and distribute information across the production line, dispatching work orders and work instructions based on trigger events. Data analytics and reporting: The MES software should provide comprehensive analytics and reporting capabilities, allowing you to track and analyze KPIs, production trends and efficiency metrics.