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Data architecture strategy for data quality

IBM Journey to AI blog

The right data architecture can help your organization improve data quality because it provides the framework that determines how data is collected, transported, stored, secured, used and shared for business intelligence and data science use cases.

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

IBM Journey to AI blog

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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Data platform trinity: Competitive or complementary?

IBM Journey to AI blog

Data platform architecture has an interesting history. Towards the turn of millennium, enterprises started to realize that the reporting and business intelligence workload required a new solution rather than the transactional applications. It required a different data platform solution. It was Datawarehouse.

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Discover the Snowflake Architecture With All its Pros and Cons- NIX United

Mlearning.ai

Today, companies are facing a continual need to store tremendous volumes of data. The demand for information repositories enabling business intelligence and analytics is growing exponentially, giving birth to cloud solutions. Data warehousing is a vital constituent of any business intelligence operation.

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Principal Financial Group uses AWS Post Call Analytics solution to extract omnichannel customer insights

AWS Machine Learning Blog

After a few minutes, a transcript is produced with Amazon Transcribe Call Analytics and saved to another S3 bucket for processing by other business intelligence (BI) tools. PCA’s security features ensure that any PII data was redacted from the transcript, as well as from the audio file itself.

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Exploring the AI and data capabilities of watsonx

IBM Journey to AI blog

IBM software products are embedding watsonx capabilities across digital labor, IT automation, security, sustainability, and application modernization to help unlock new levels of business value for clients. foundation models to help users discover, augment, and enrich data with natural language.

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A brief history of Data Engineering: From IDS to Real-Time streaming

Artificial Corner

This period also saw the development of the first data warehouses, large storage repositories that held data from different sources in a consistent format. The concept of data warehousing was introduced by Bill Inmon, often referred to as the “father of data warehousing.”