CIO Business Intelligence
JANUARY 17, 2024
The company has been on a continuous journey to adapt its internal and external processes to new business needs and opportunities since 2001.” And last year, with AI-powered Insights Delivery, it completed a phase of its journey using artificial intelligence to transform the way it works and delivers insights to clients. “To
Alation
MARCH 29, 2022
Business intelligence has a long history. How will moving to true self-service business intelligence — and achieving data democracy — impact your organization? A Short History of Business Intelligence. Modern business intelligence starts at the dawn of digital computing. Parting Words.
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CIO Business Intelligence
SEPTEMBER 28, 2022
Companies that did so in 2001 and 2008 were frequently punished for it by the market. Hence, larger businesses , particularly those that have already weathered past economic crises, tend to be much more likely to either maintain their IT spending levels or even to increase them during economic headwinds. t spur IT cutbacks.
CIO Business Intelligence
APRIL 4, 2022
“Our strategy in taking a hybrid approach has provided the agility we need to do advanced services in the cloud as we go through our digital transformation,” says Gabriel, who joined the company in 2001 and was promoted to executive vice president and CIO of Clean Harbors in 2018. The company’s 400 IT staff — located at its Norwell, Mass.,
CIO Business Intelligence
MAY 30, 2022
It was released as a reference model for enterprise architecture, offering insight into DoD’s own technical infrastructure, including how it’s structured, maintained, and configured to align with specific requirements. The Open Group also streamlined the documentation, removing anything redundant or outdated. in December 2003.
CIO Business Intelligence
MARCH 24, 2022
Established by the UK Parliament in 2001 to settle complaints about financial-services companies, the Financial Ombudsman’s Service (FOS) has served an increasing number of consumers, recently dealing with more than a million people a year.
Domino Data Lab
APRIL 3, 2019
Instead, we must build robust ML models which take into account inherent limitations in our data and embrace the responsibility for the outcomes. As the story goes, the general history of DG is punctuated by four eras: “Application Era” (1960–1990) – some data modeling, ?though There are models everywhere. It’s a mess.
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