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The evolving role of CFO is driven by data

CIO Business Intelligence

Data has always been central to agile business planning, forecasting and analysis – all tools which have become central to the modern CFO role. This level of data collection and insight requires the right technology. This all helps reconcile data from a wide variety of different sources into a trusted, compliant platform.

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Data science vs data analytics: Unpacking the differences

IBM Big Data Hub

Though you may encounter the terms “data science” and “data analytics” being used interchangeably in conversations or online, they refer to two distinctly different concepts. Meanwhile, data analytics is the act of examining datasets to extract value and find answers to specific questions.

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Here’s How To Implement Manufacturing Analytics Today

Smart Data Collective

Building a robust data platform can transform the way manufacturers handle their customers and supplies. Not only are real-time results available, but big data can also provide demand forecasts to guide the production chain based on historical data sales trends in order to stay on top of the demand.

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15 Best Data Analysis Tools You Can’t Miss in 2022

FineReport

Most data analysts are very familiar with Excel because of its simple operation and powerful data collection, storage, and analysis. Key features: Excel has basic features such as data calculation which is suitable for simple data analysis. SAS Forecasting. From SAS Forecast Server. From KNIME.

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How Jabil is building better, faster enterprise reporting

IBM Big Data Hub

Switching to IBM Business Analytics gave Jabil the ability to gather and structure data to provide a central approach to management. The integrated solution automatically handles reporting, analysis, planning and forecasting. Overall, the solution turns a massive data collection effort into a push-button activity.

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Create an end-to-end data strategy for Customer 360 on AWS

AWS Big Data

In this post, we discuss how you can use purpose-built AWS services to create an end-to-end data strategy for C360 to unify and govern customer data that address these challenges. We recommend building your data strategy around five pillars of C360, as shown in the following figure.

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On the Hunt for Patterns: from Hippocrates to Supercomputers

Ontotext

Such problems and the complexities related to such computationally-intensive tasks are essential in the fields of weather forecasting, molecular modeling, airplane and spacecraft aerodynamics, personalized medicine, self-driving cars. Behind the scenes of linking histopathology data and building a knowledge graph out of it.