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

AWS Big Data

A Gartner Marketing survey found only 14% of organizations have successfully implemented a C360 solution, due to lack of consensus on what a 360-degree view means, challenges with data quality, and lack of cross-functional governance structure for customer data. QuickSight offers scalable, serverless visualization capabilities.

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How Amazon Devices scaled and optimized real-time demand and supply forecasts using serverless analytics

AWS Big Data

With data volumes exhibiting a double-digit percentage growth rate year on year and the COVID pandemic disrupting global logistics in 2021, it became more critical to scale and generate near-real-time data. You can visually create, run, and monitor extract, transform, and load (ETL) pipelines to load data into your data lakes.

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Achieve your AI goals with an open data lakehouse approach

IBM Big Data Hub

Artificial intelligence (AI) is now at the forefront of how enterprises work with data to help reinvent operations, improve customer experiences, and maintain a competitive advantage. It’s no longer a nice-to-have, but an integral part of a successful data strategy. Why does AI need an open data lakehouse architecture?

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Achieving Trusted AI in Manufacturing

Cloudera

Demand forecasting: AI can be used to forecast demand for products based on historical data, trends, and external factors such as weather, holidays, seasonality, and market conditions. Trusted AI begins with trusted data What resolves the data challenge and fuels data-driven AI in manufacturing?

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How Etihad taps data science to optimise airline operations

CIO Business Intelligence

Despite the worldwide chaos, UAE national airline Etihad has managed to generate productivity gains and cost savings from insights using data science. Etihad began its data science journey with the Cloudera Data Platform and moved its data to the cloud to set up a data lake. A change was needed.

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Data science vs data analytics: Unpacking the differences

IBM Big Data Hub

Those who work in the field of data science are known as data scientists. Having the right data strategy and data architecture is especially important for an organization that plans to use automation and AI for its data analytics. Watsonx comprises of three powerful components: the watsonx.ai

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Straumann Group is transforming dentistry with data, AI

CIO Business Intelligence

Selling the value of data transformation Iyengar and his team are 18 months into a three- to five-year journey that started by building out the data layer — corralling data sources such as ERP, CRM, and legacy databases into data warehouses for structured data and data lakes for unstructured data.