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Bringing Financial Services Business Use Cases to Life: Leveraging Data Analytics, ML/AI, and Gen AI

Cloudera

Key use cases include customer journey/customer 360, regulatory compliance, financial crime prevention, risk management, market risk, credit risk, liquidity risk, operational risk, systemic risk, climate risk, intraday risk management, finance, integrated risk and finance view, treasury management, advanced analytics, and emerging technology.

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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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Data architecture strategy for data quality

IBM Big Data Hub

Several factors determine the quality of your enterprise data like accuracy, completeness, consistency, to name a few. But there’s another factor of data quality that doesn’t get the recognition it deserves: your data architecture. How the right data architecture improves data quality.

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Critical Components of Big Data Architecture for a Translation Company

Smart Data Collective

Below we’ll go over how a translation company, and specifically one that provides translations for businesses, can easily align with big data architecture to deliver better business growth. How Does Big Data Architecture Fit with a Translation Company? That’s the data source part of the big data architecture.

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Uplevel your data architecture with real- time streaming using Amazon Data Firehose and Snowflake

AWS Big Data

Connectivity – Amazon Data Firehose can connect to public or private subnets in your VPC. This post explains how you can bring streaming data from AWS into Snowflake within seconds to perform advanced analytics. To try Amazon Kinesis Firehose with Snowflake, refer to the Amazon Data Firehose with Snowflake as destination lab.

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DataOps For Business Analytics Teams

DataKitchen

Their business unit colleagues ask an endless stream of urgent questions that require analytic insights. Business analysts must rapidly deliver value and simultaneously manage fragile and error-prone analytics production pipelines. In business analytics, fire-fighting and stress are common. Analytics Hub and Spoke.

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Data democratization: How data architecture can drive business decisions and AI initiatives

IBM Big Data Hub

Today, the way businesses use data is much more fluid; data literate employees use data across hundreds of apps, analyze data for better decision-making, and access data from numerous locations. This includes tools that do not require advanced technical skill or deep understanding of data analytics to use.