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

CIO Business Intelligence

A DSS supports the management, operations, and planning levels of an organization in making better decisions by assessing the significance of uncertainties and the tradeoffs involved in making one decision over another. Model-driven DSS use data and parameters provided by decision-makers, but Power notes they are usually not data-intensive.

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Powerful Video Summaries, Powered by AI

Timo Elliott

He focuses on three big opportunities: faster innovation, empowering business people, and moving from analytics to action. He explains that automation and innovation have become critical as the world experiences supply chain disruptions, inflation, extreme weather events, worker shortages, and uncertainty.

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Perform time series forecasting using Amazon Redshift ML and Amazon Forecast

AWS Big Data

Amazon Redshift is a fully managed, petabyte-scale data warehouse service in the cloud. Tens of thousands of customers use Amazon Redshift to process exabytes of data every day to power their analytics workloads. For more information, see ElectricityLoadDiagrams20112014 Data Set (Dua, D. and Karra Taniskidou, E.

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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. Imtiaz (Taz) Sayed is the WW Analytics Tech Leader at AWS. Our world today is experiencing an extremely social, connected, competitive and technology-driven business environment. Jignesh Desai is the WW Migration Leader at AWS.

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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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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.

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Quantitative and Qualitative Data: A Vital Combination

Sisense

The challenge comes when the data becomes huge and fast-changing. Why is quantitative data important? Quantitative data is often viewed as the bedrock of your business intelligence and analytics program because it can reveal valuable insights for your organization. What are the problems with quantitative data?