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Breaking down Business Intelligence

BizAcuity

The more effectively a company uses data, the better it performs. Cutting down latency or delay is now one of the most crucial elements of business intelligence strategy in present times. Integrating data through data warehouses and data lakes is one of the standard industry best practices for optimizing business intelligence.

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Top 5 Reasons You Should Become a Data Analyst

Smart Data Collective

As a data analyst, you will learn several technical skills that data analysts need to be successful, including: Programming skills. Data visualization capability. Data Mining skills. Data wrangling ability. Data analysts usually have comprehensive and always-changing skill sets.

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DirectX Visualization Optimizes Analytics Algorithmic Traders

Smart Data Collective

Learn how DirectX visualization can improve your study and assessment of different trading instruments for maximum productivity and profitability. A growing number of traders are using increasingly sophisticated data mining and machine learning tools to develop a competitive edge.

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Is Advanced Analytics the Next Logical Step Beyond Self-Serve Business Intelligence?

ElegantJ BI

Today’s Advanced Analytics Tools allow business users to leverage features like self-serve data preparation, smart data visualization and assisted predictive modeling.

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Is Advanced Analytics the Next Logical Step Beyond Self-Serve Business Intelligence?

ElegantJ BI

Today’s Advanced Analytics Tools allow business users to leverage features like self-serve data preparation, smart data visualization and assisted predictive modeling.

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Is Advanced Analytics the Next Logical Step Beyond Self-Serve Business Intelligence?

ElegantJ BI

Today’s Advanced Analytics Tools allow business users to leverage features like self-serve data preparation, smart data visualization and assisted predictive modeling.

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Data Analytics Lifecycle Phases

The BAWorld

Life Cycle Phases of Data Analytics This tutorial discusses the data analytics lifecycle phases that are essential to each data analytics process and how to implement them. As a result, they are more likely to remain present throughout the lifecycle of most data analytics projects. This is known as data mining.