Remove AI Remove Data Silos Remove ML Remove Predictive Analytics
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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.

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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.

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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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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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A Guide to Data Analytics in the Travel Industry

Alation

While this industry has used data and analytics for a long time, many large travel organizations still struggle with data silos , which prevent them from gaining the most value from their data. What is big data in the travel and tourism industry?

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How to Leverage Data Analytics in the Hospitality Industry

Alation

The hospitality industry generates vast amounts of data from various sources, including customer bookings, transactions, loyalty programs, social media, and guest feedback. For example, hotels can use data analytics to identify booking patterns and optimize room rates, inventory, and staffing levels.

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How to Leverage Data Analytics in the Hospitality Industry

Alation

The hospitality industry generates vast amounts of data from various sources, including customer bookings, transactions, loyalty programs, social media, and guest feedback. For example, hotels can use data analytics to identify booking patterns and optimize room rates, inventory, and staffing levels.