Remove Dashboards Remove Data Warehouse Remove Insurance Remove Reporting
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5 misconceptions about cloud data warehouses

IBM Big Data Hub

In today’s world, data warehouses are a critical component of any organization’s technology ecosystem. They provide the backbone for a range of use cases such as business intelligence (BI) reporting, dashboarding, and machine-learning (ML)-based predictive analytics, that enable faster decision making and insights.

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Combine transactional, streaming, and third-party data on Amazon Redshift for financial services

AWS Big Data

The following are some of the key business use cases that highlight this need: Trade reporting – Since the global financial crisis of 2007–2008, regulators have increased their demands and scrutiny on regulatory reporting. This will be your OLTP data store for transactional data. version cluster. version cluster.

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Business Intelligence vs Data Science vs Data Analytics

FineReport

Definition: BI vs Data Science vs Data Analytics. Business Intelligence describes the process of using modern data warehouse technology, data analysis and processing technology, data mining, and data display technology for visualizing, analyzing data, and delivering insightful information.

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How gaming companies can use Amazon Redshift Serverless to build scalable analytical applications faster and easier

AWS Big Data

Data lakes are more focused around storing and maintaining all the data in an organization in one place. And unlike data warehouses, which are primarily analytical stores, a data hub is a combination of all types of repositories—analytical, transactional, operational, reference, and data I/O services, along with governance processes.

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Convergent Evolution

Peter James Thomas

From 2000 to 2015, I had some success [5] with designing and implementing Data Warehouse architectures much like the following: As a lot of my work then was in Insurance or related fields, the Analytical Repositories tended to be Actuarial Databases and / or Exposure Management Databases, developed in collaboration with such teams.

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Top 10 Reasons for Alation with Snowflake: Reduce Risk with Active Data Governance

Alation

A range of regulations exist: the General Data Protection Regulation (GDPR), California Consumer Privacy Act (CCPA), as well as industry regulations like the Health Insurance Portability and Accountability Act (HIPAA) and Sarbanes–Oxley Act (SOX). In an ideal world, you’d get compliance guidance before and as you use the data.

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Seven Steps to Success for Predictive Analytics in Financial Services

Birst BI

The output of these algorithms, when used in financial services, can be anything from a customer behavior score to a prediction of future trading trends, to flagging a fraudulent insurance claim. This may involve integrating different technologies, like cloud sources, on-premise databases, data warehouses and even spreadsheets.