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Enabling AI-powered business intelligence across the enterprise

IBM Big Data Hub

Beyond the traditional data roles—data engineers, analysts, architects—decision-makers across an organization need flexible, self-service access to data-driven insights accelerated by artificial intelligence (AI). But most businesses are behind.

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Critical Components of Big Data Architecture for a Translation Company

Smart Data Collective

However, big data often encapsulates using constantly growing data sets to determine business intelligence objectives, such as when to expand into a new market, which product might perform overseas, and which regions to expand into. How Does Big Data Architecture Fit with a Translation Company?

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Data democratization: How data architecture can drive business decisions and AI initiatives

IBM Big Data Hub

Today, the way businesses use data is much more fluid; data literate employees use data across hundreds of apps, analyze data for better decision-making, and access data from numerous locations. This lets users across the organization treat the data like a product with widespread access.

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SAP enhances Datasphere and SAC for AI-driven transformation

CIO Business Intelligence

The company is expanding its partnership with Collibra to integrate Collibra’s AI Governance platform with SAP data assets to facilitate data governance for non-SAP data assets in customer environments. “We We are also seeing customers bringing in other data assets from other apps or data sources.

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Statistics Changing Marketing Strategies

TDAN

When it comes to marketing, business owners need to be fast in adjusting their strategies to fit the continuous advancement in technologies. Today, nearly everyone has a mobile phone or another smart mobile device with them at all times.

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How the right data and AI foundation can empower a successful ESG strategy

IBM Big Data Hub

A well-designed data architecture should support business intelligence and analysis, automation, and AI—all of which can help organizations to quickly seize market opportunities, build customer value, drive major efficiencies, and respond to risks such as supply chain disruptions.

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Combine transactional, streaming, and third-party data on Amazon Redshift for financial services

AWS Big Data

Financial services customers are using data from different sources that originate at different frequencies, which includes real time, batch, and archived datasets. Additionally, they need streaming architectures to handle growing trade volumes, market volatility, and regulatory demands.