Remove Data Scientist Remove Data Silos Remove Predictive Analytics
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Data Integration for AI: Top Use Cases and Steps for Success

Precisely

Without it, you risk flawed predictions that contain AI hallucination or bias and cause you to miss valuable opportunities. Thats where data integration comes in. If you cant use predictive analytics and make quick, confident data-driven decisions, you risk falling behind to your competitors that can.

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Air Quality Data Challenge Winners

Ocean Protocol

Ocean Protocol hosts data challenges like these to attract data scientists to publish high quality data assets on the Ocean Market. Feedback from contestants also drives innovation and improvements to the Ocean tech stack.

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How Investment Banks and Asset Managers Should Be Leveraging Data in Snowflake

phData

This is due to a fragmented ecosystem of data silos, a lack of real-time fraud detection capabilities, and manual or delayed customer analytics, which results in many false positives. Snowflake Marketplace offers data from leading industry providers such as Axiom, S&P Global, and FactSet.

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Predict-ETH 4: Dive into Decentralized Data

Ocean Protocol

Calling all Oceaners, data scientists, and traders! It is time to take part in decentralized data science with Ocean Protocol! About Predict-ETH Competition Ocean Protocol’s Predict-ETH data challenge is an exciting opportunity for data scientists to showcase their skills and potentially win big.

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Using Snowflake Data as an Insurance Company

phData

Insurance companies often face challenges with data silos and inconsistencies among their legacy systems. To address these issues, they need a centralized and integrated data platform that serves as a single source of truth, preferably with strong data governance capabilities.

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Solving Complex Telecom Challenges with Data Governance and Location Analytics

Precisely

Here are some of the key trends and challenges facing telecommunications companies today: The growth of AI and machine learning: Telecom companies use artificial intelligence and machine learning (AI/ML) for predictive analytics and network troubleshooting. In other words, the data needs to be freed from its silos.

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5 misconceptions about cloud data warehouses

IBM Journey to AI blog

In today’s world, data warehouses are a critical component of any organization’s technology ecosystem. They provide the backbone for a range of use cases such as business intelligence (BI) reporting, dashboarding, and machine-learning (ML)-based predictive analytics, that enable faster decision making and insights.