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Data Warehouse Teams Adapt to Be Data Driven

TDAN

When companies embark on a journey of becoming data-driven, usually, this goes hand in and with using new technologies and concepts such as AI and data lakes or Hadoop and IoT. Suddenly, the data warehouse team and their software are not the only ones anymore that turn data […].

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Architectural patterns for real-time analytics using Amazon Kinesis Data Streams, part 1

AWS Big Data

We’re living in the age of real-time data and insights, driven by low-latency data streaming applications. The volume of time-sensitive data produced is increasing rapidly, with different formats of data being introduced across new businesses and customer use cases.

Analytics 115
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Lexmark International’s Vishal Gupta on next gen tech leadership

CIO Business Intelligence

As software and data move to the center of a company’s products and services, the background and skills of the executive leadership team must evolve. When IoT becomes the driver of a new solutions P&L, the general manager of that business will need more technology acumen than general managers of the past.

IoT 81
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What Stands Between IT and Business Success? Data Complexity

CIO Business Intelligence

By George Trujillo, Principal Data Strategist, DataStax. Any enterprise data management strategy has to begin with addressing the 800-pound gorilla in the corner: the “innovation gap” that exists between IT and business teams. This scarcity of quality data might feel akin to dying of thirst in the middle of the ocean.

IT 134
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Achieving Trusted AI in Manufacturing

Cloudera

With the ability of manufacturers to store a huge volume of historical data, AI can be applied in general business areas of any industry, like developing recommendations for marketing, supply chain optimization, and new product development. With AI, it can even prescribe the appropriate action that needs to be taken and when.

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Understanding Digital Interactions in Real-Time

CIO Business Intelligence

But this glittering prize might cause some organizations to overlook something significantly more important: constructing the kind of event-driven data architecture that supports robust real-time analytics. The foundation of an event-driven architecture. All in real-time, of course.

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Telecom Network Analytics: Transformation, Innovation, Automation

Cloudera

One of the most substantial big data workloads over the past fifteen years has been in the domain of telecom network analytics. The Dawn of Telco Big Data: 2007-2012. Suddenly, it was possible to build a data model of the network and create both a historical and predictive view of its behaviour. Where does it stand today?