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The 4 Common Challenges of Predictive Analytics Solutions

Jet Global

Predictive analytics is a branch of analytics that uses historical data, machine learning, and Artificial Intelligence (AI) to help users act preemptively. Predictive analytics answers this question: “What is most likely to happen based on my current data, and what can I do to change that outcome?”

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The Benefits, Challenges and Risks of Predictive Analytics for Your Application

Jet Global

In this modern, turbulent market, predictive analytics has become a key feature for analytics software customers. Predictive analytics refers to the use of historical data, machine learning, and artificial intelligence to predict what will happen in the future.

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Top 5 Resources to Understand the Role of AI/ML in Embedded Analytics 

Jet Global

Every day, more companies unlock the potential of artificial intelligence (AI) and machine learning. When AI and machine learning are utilized in embedded analytics, the results are impressive. Much of this can be seen in modern solutions that offer advanced predictive analytics.

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Finding the Right Reporting Narrative in Banking and Insurance

Jet Global

While shifting influences such as trade tensions and Brexit are causing uncertain futures for business and investment, Atradius can also use its CXO Software solution to run quick predictive analytics, projecting several different scenarios in a submitted forecast. So How Did They Do It? But don’t just take our word for it.

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Unlocking the Power of AI with a Real-Time Data Strategy

CIO Business Intelligence

To succeed with real-time AI, data ecosystems need to excel at handling fast-moving streams of events, operational data, and machine learning models to leverage insights and automate decision-making. It’s also used to deploy machine learning models, data streaming platforms, and databases.

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3 Ways Embedded Analytics Boosts Data Literacy

Jet Global

White-labelled embedded analytics software kicks this up a notch, but allowing you to beautify dashboards with your customer’s personal branding, guaranteed to catch the eye of their buying team. The Embedded Analytics Buyer’s Guide Download Now 2.

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How to Handle Missing Data Values While Data Cleaning

Jet Global

In fact, most project teams spend 60 to 80 percent of total project time cleaning their data—and this goes for both BI and predictive analytics. To improve the effectiveness of the data cleaning process, the current trend is to migrate from manual data cleaning to more intelligent, machine learning-based processes.