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Business Intelligence vs Data Science vs Data Analytics

FineReport

Good data can give you keen insights, convincing evidence to make informed decisions. By observing and analyzing data, we can develop more accurate theories and formulate more effective solutions. For this reason, data science and/vs. Definition: BI vs Data Science vs Data Analytics. What is Business Intelligence?

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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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Everything You Need to Know About Real-Time Business Intelligence

Sisense

Unlike traditional models that look at historical data for patterns, real-time analytics focuses on understanding information as it arrives to help make faster, better decisions. Today, real time business intelligence is a necessity more than a luxury, so it’s important to understand exactly what it is, and what it can do for you.

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

AWS Big Data

In this post, we provide a solution architecture that describes how you can process data from three different types of sources—streaming, transactional, and third-party reference data—and aggregate them in Amazon Redshift for business intelligence (BI) reporting. This will be your OLTP data store for transactional data.

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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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Seven Steps to Success for Predictive Analytics in Financial Services

Birst BI

Because the data describing each transaction was in a database, this made it easy to retrieve and summarize multiple transactions together. This data retrieval and summarization capability gave rise to what we now know as the business intelligence industry. Add the predictive logic to the data model.

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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.