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Seven Steps to Success for Predictive Analytics in Financial Services

Birst BI

Today, the most common usage of business intelligence is for the production of descriptive analytics. . Descriptive Analytics: Valuable but limited insights into historical behavior. The vast majority of financial services companies use the data within their applications for what is called “ Descriptive Analytics.”

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Five Steps for Building a Successful BI Strategy

Sisense

A business intelligence strategy is a blueprint that enables businesses to measure their performance, find competitive advantages, and use data mining and statistics to steer the business towards success. . Most companies find themselves in the bottom left corner, in the Descriptive Analytics and Diagnostic Analytics sections.

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What Is The Difference Between Business Intelligence And Analytics?

datapine

There is not a clear line between business intelligence and analytics, but they are extremely connected and interlaced in their approach towards resolving business issues, providing insights on past and present data, and defining future decisions. What Is Business Intelligence And Analytics?

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How to supercharge data exploration with Pandas Profiling

Domino Data Lab

Predictive modeling efforts rely on dataset profiles , whether consisting of summary statistics or descriptive charts. The Importance of Exploratory Analytics in the Data Science Lifecycle. Computing interactions of all features on a pairwise basis can be useful for selecting, or de-selecting, for further research.

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5 Sources of Data for Customer Analytics and Their Benefits

Smart Data Collective

Customer service analytics assist you in tracking and comparing key performance indicators (KPIs) to service level agreements (SLAs). You can see which representatives are meeting their targets and which ones need to boost their statistics this way. Customer Experience Analytics. Customer Lifetime Analytics.

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Data Visualization and Visual Analytics: Seeing the World of Data

Sisense

The primary objective of data visualization is to clearly communicate what the data says, help explain trends and statistics, and show patterns that would otherwise be impossible to see. The role of visualizations in analytics. Data visualization can either be static or interactive. Visualizations: past, present, and future.

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Three Types of Actionable Business Analytics Not Called Predictive or Prescriptive

Rocket-Powered Data Science

What is the point of those obvious statistical inferences? In statistical terms, the joint probability of event Y and condition X co-occurring, designated P(X,Y), is essentially the probability P(Y) of event Y occurring. How do predictive and prescriptive analytics fit into this statistical framework? Pay attention!