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

CIO Business Intelligence

If anything, the past few years have shown us the levels of uncertainty we are facing. Infosys Living labs is a set of well-orchestrated innovation services for future-proofing customer businesses and de-risking their emerging technology transformations.

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Powerful Video Summaries, Powered by AI

Timo Elliott

He explains that automation and innovation have become critical as the world experiences supply chain disruptions, inflation, extreme weather events, worker shortages, and uncertainty. He gave an example of a mobile application used by a zoo in Sydney that brings together all the information employees need, from HR data to emergency data.

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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 is the ‘Mesh’ Resolving Bottlenecks of Data Management

Smart Data Collective

More case studies are added every day and give a clear hint – data analytics are all set to change, again! . Data Management before the ‘Mesh’. In the early days, organizations used a central data warehouse to drive their data analytics. This is also true that decentralized data management is not new.

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

Alation

It’s also the mechanism that brings data consumers and data producers closer together. Our legacy architecture, like that at most organizations, is a massive on-prem enterprise data warehouse,” Lavorini says. “As As we modernize our core banking platforms, the data goes with that modernization journey.”

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New Thinking, Old Thinking and a Fairytale

Peter James Thomas

An obvious parallel in my world is to consider another business activity that reached peak popularity in the 2000s, Data Warehouse programmes [4]. Figures suggest that both BPR and Data Warehouse programmes have a failure rate of 60 – 70% [5]. King was a wise King, but now he was gripped with uncertainty.

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Quantitative and Qualitative Data: A Vital Combination

Sisense

These techniques allow you to: See trends and relationships among factors so you can identify operational areas that can be optimized Compare your data against hypotheses and assumptions to show how decisions might affect your organization Anticipate risk and uncertainty via mathematically modeling.