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What is data analytics? Analyzing and managing data for decisions

CIO

It comprises the processes, tools and techniques of data analysis and management, including the collection, organization, and storage of data. The chief aim of data analytics is to apply statistical analysis and technologies on data to find trends and solve problems. It is frequently used for economic and sales forecasting.

Analytics 337
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Fundamentals of Data Engineering

Xebia

– Jesse Anderson The data engineering field could be thought of as a superset of business intelligence and data warehousing that brings more elements from software engineering. The data engineer is also expected to create agile data architectures that evolve as new trends emerge. Lastly, do not forget to back up your data.

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Improving Decision Making by 75% using Supply Chain Visualization

Sunflower Lab

Supply chain managers rely on data visualization tools & techniques to gain a comprehensive understanding of operational dynamics, identify trends, and anticipate potential issues. These visualizations provide stakeholders with intuitive ways to understand complex data patterns and trends.

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Data Lake Explained: A Comprehensive Guide to Its Architecture and Use Cases

Altexsoft

In 2010, a transformative concept took root in the realm of data storage and analytics — a data lake. The term was coined by James Dixon , Back-End Java, Data, and Business Intelligence Engineer, and it started a new era in how organizations could store, manage, and analyze their data. Data warehouse vs. data lake in a nutshell.

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5 Challenges of Data Integration (ETL) and How to Fix Them

Datavail

The fields of business intelligence and analytics depend on a reliable, consistent ETL pipeline that continually gives users access to fresh, up-to-the-minute information. To get the most from their ETL pipelines, businesses need to tweak and optimize their workflow on a regular basis.

Data 52
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Ask Miss O11y: Observability vs BI Tools & Data Warehouses

Honeycomb

You probably have already answered this before, but do you have a good rule of thumb for where o11y [observability] ends and BI [business intelligence]/data warehouses begin? Which is a very real, very common tradeoff that you have to make with massively parallelized distributed systems across flaky networks. And it’s fast.

Tools 52
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Social Sites Becoming Too Much of a Good Thing - Social Networking Fatigue?

elsua: The Knowledge Management Blog

Toolbox for IT Join Now / Sign In My Home Posts Connections Groups Blogs People Communities Vendors Messages Profile Achievements Journal Blog Bookmarks Account / E-mails Topics Business Intelligence C Languages CRM Database IT Management and Strategy Data Center Data Warehouse Emerging Technology and Trends Enterprise Architecture and EAI ERP Hardware (..)

Social 40