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External Data Supports More Accurate Planning

David Menninger's Analyst Perspectives

One of the points that I look at is whether and to what extent the software provider offers out-of-the-box external data useful for forecasting, planning, analysis and evaluation. It is also essential for the effective application of AI using ML for business-focused planning and budgeting and predictive analytics.

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Why Do Some Companies Achieve More Predictive Analytics Success?

CIO Business Intelligence

There is growing belief that businesses are set to spend huge amounts of money on predictive analytics. While in 2021, the global market for corporate predictive analytics was worth $10 billion, it is forecast to balloon to $28 billion by 2026.

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Top 10 Analytics And Business Intelligence Trends For 2020

datapine

While we work on programs to avoid such inconvenience , AI and machine learning are revolutionizing the way we interact with our analytics and data management while increment in security measures must be taken into account. However, businesses today want to go further and predictive analytics is another trend to be closely monitored.

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Predictive analytics: opportunities and limits for the future of finance

Jedox

Predictive analytics is a discipline that’s been around in some form since the dawn of measurement. We’ve always been trying to predict the future; go back in history to look at prognosticators like Nostradamus and many other prophets. A Brief History of Predictive Analytics. What is Predictive Analytics?

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Error Metrics: How to Evaluate Your Forecasts

Jedox

When considering the performance of any forecasting model, the prediction values it produces must be evaluated. An error metric is a way to quantify the performance of a model and provides a way for the forecaster to quantitatively compare different models 1. Where y’ is forecasted value and y is the true value.

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Analytics Insights and Careers at the Speed of Data

Rocket-Powered Data Science

Focus on the strategies that aim these tools, talents, and technologies on reaching business mission and goals: e.g., data strategy, analytics strategy, observability strategy ( i.e., why and where are we deploying the data-streaming sensors, and what outcomes should they achieve?).

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10 Examples of How Big Data in Logistics Can Transform The Supply Chain

datapine

According to studies, 92% of data leaders say their businesses saw measurable value from their data and analytics investments. Your Chance: Want to test a professional logistics analytics software? Where is all of that data going to come from?

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