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What are decision support systems? Sifting data for better business decisions

CIO Business Intelligence

Decision support systems definition A decision support system (DSS) is an interactive information system that analyzes large volumes of data for informing business decisions. A DSS leverages a combination of raw data, documents, personal knowledge, and/or business models to help users make decisions. Data-driven DSS.

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Disrupt and Innovate in a Data-Driven World

Cloudera

If you do an internet search for ‘data-driven disruption’ you can find articles about almost every industry being disrupted by digitalisation and new applications of data. While there are instances of data-driven efforts in the nonprofit sector, they are not as widespread as they can be.

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Innovate What’s Next: How Living Labs Brings Ideas to Life

CIO Business Intelligence

Our world today is experiencing an extremely social, connected, competitive and technology-driven business environment. If anything, the past few years have shown us the levels of uncertainty we are facing. Infosys Living Labs helps customers solve business problems with their emerging technology solutions and service offerings.

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Quantitative and Qualitative Data: A Vital Combination

Sisense

Digging into quantitative data Why is quantitative data important What are the problems with quantitative data Exploring qualitative data Qualitative data benefits Getting the most from qualitative data Better together. Almost every modern organization is now a data-generating machine. or “how often?”

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Banking on mainframe-led digital transformation for financial services

IBM Big Data Hub

Banks have the most to gain if they succeed (and the most to lose if they fail) at bringing their mainframe application and data estates up to modern standards of cloud-like flexibility, agility and innovation to meet customer demand. Couldn’t execs have run better analyses to spot risks within the data?

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How Fifth Third Bank Implements a Data Mesh with Alation and Snowflake

Alation

Every organization wants to better serve its customers, and that goal is often achieved through data. Situationally, it was a really good time to deploy a data mesh architecture and its principles and invest in this space because we were doing so much tech modernization,” Lavorini says. “So So why not make data a part of it?”

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Data Science, Past & Future

Domino Data Lab

Paco Nathan presented, “Data Science, Past & Future” , at Rev. At Rev’s “ Data Science, Past & Future” , Paco Nathan covered contextual insight into some common impactful themes over the decades that also provided a “lens” help data scientists, researchers, and leaders consider the future.