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DataOps For Business Analytics Teams

DataKitchen

Business analysts must rapidly deliver value and simultaneously manage fragile and error-prone analytics production pipelines. Data tables from IT and other data sources require a large amount of repetitive, manual work to be used in analytics. In business analytics, fire-fighting and stress are common.

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Automate deployment of an Amazon QuickSight analysis connecting to an Amazon Redshift data warehouse with an AWS CloudFormation template

AWS Big Data

Amazon Redshift is the most widely used data warehouse in the cloud, best suited for analyzing exabytes of data and running complex analytical queries. Amazon QuickSight is a fast business analytics service to build visualizations, perform ad hoc analysis, and quickly get business insights from your data.

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What is a business intelligence analyst? A key role for data-driven decisions

CIO Business Intelligence

This is done by mining complex data using BI software and tools , comparing data to competitors and industry trends, and creating visualizations that communicate findings to others in the organization.

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The Key to Unlocking IT Modernization’s Power? Enterprise level Transformation

Cloudera

The United States Veterans Administration (VA) over the last decade underwent a massive enterprise-wide IT transformation, eliminating its fragmented shadow IT and adopting a centralized system capable of supporting the agency’s 400,000 employees and more effectively utilizing its $240 billion-plus annual budget.

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Don’t Blink: You’ll Miss Something Amazing!

Cloudera

A unique architecture to optimize for real-time data warehousing and business analytics: Cloudera Data Platform (CDP) offers Apache Kudu as part of our Data Hub cloud service, providing a consistent, dependable way to support the ingestion of data streams into our analytics environment, in real time, and at any scale.

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5 Signs You’re Using Bad Data to Make Business Decisions

Jet Global

Are you making decisions based on bad data? It’s hard to answer that question because, truth be told, you don’t know you’re using bad data until it’s too late. . states that about 40 percent of enterprise data is either inaccurate, incomplete, or unavailable. Gartner study ?states Download Now.

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Data science vs data analytics: Unpacking the differences

IBM Big Data Hub

Data scientists also rely on data analytics to understand datasets and develop algorithms and machine learning models that benefit research or improve business performance. The dedicated data analyst Virtually any stakeholder of any discipline can analyze data.