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Augmented Analytics Must Provide Data Quality and Insight!

Smarten

Whether you are trying to solve a business problem, get to the heart of that problem, find a business opportunity, predict the need for resources, new products or locations or understanding changes in your customer buying behavior, you don’t have time to learn complex tools or take training in analytics.

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What is data analytics? Analyzing and managing data for decisions

CIO Business Intelligence

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 risk analysis.

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AI In Analytics: Today and Tomorrow!

Smarten

Assisted Predictive Modeling and Auto Insights to create predictive models using self-guiding UI wizard and auto-recommendations The Future of AI in Analytics The C=suite executive survey revealed that 93% felt that data strategy is critical to getting value from generative AI, but a full 57% had made no changes to their data.

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Top 10 Analytics And Business Intelligence Trends For 2020

datapine

Data exploded and became big. Spreadsheets finally took a backseat to actionable and insightful data visualizations and interactive business dashboards. The rise of self-service analytics democratized the data product chain. 2) Data Discovery/Visualization. We all gained access to the cloud.

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What is a Data Pipeline?

Jet Global

Real-Time Analytics Pipelines : These pipelines process and analyze data in real-time or near-real-time to support decision-making in applications such as fraud detection, monitoring IoT devices, and providing personalized recommendations. For example, migrating customer data from an on-premises database to a cloud-based CRM system.

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Seven Steps to Success for Predictive Analytics in Financial Services

Birst BI

The foundation of predictive analytics is based on probabilities. To generate accurate probabilities of future behavior, predictive analytics combine historical data from any number of applications with statistical algorithms. Integrate the data sources of the various behavioral attributes into a functional data model.

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Why you should care about debugging machine learning models

O'Reilly on Data

If you’re using Python and deep learning libraries, the CleverHans and Foolbox packages can also help you debug models and find adversarial examples. Small residuals usually mean a model is right, and large residuals usually mean a model is wrong. Interpretable ML models and explainable ML.