Data Collection Data Processing Metrics Risk 
IBM Big Data Hub
FEBRUARY 7, 2024
Investors, regulators and stakeholders are increasingly demanding that companies disclose their exposure to climate-related risks , such as dependence on fossil fuels or vulnerability to weather events. The goal is for there to be more nature by 2030 than there is today—which means taking actionable steps in 2024.
O'Reilly on Data
MARCH 31, 2020
But there’s a host of new challenges when it comes to managing AI projects: more unknowns, non-deterministic outcomes, new infrastructures, new processes and new tools. That foundation means that you have already shifted the culture and data infrastructure of your company. If you can’t walk, you’re unlikely to run.
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Smart Data Collective
AUGUST 4, 2022
An article in HR Voices titled Data Analytics in HR: Impacting the Future of Performance Management underscores some of the benefits. The authors state that data analytics saves managers time and reduces the risk of inadvertent bias.
CIO Business Intelligence
AUGUST 15, 2023
A risk-limiting audit (RLA) is one audit type used for election verification. And it yields multiple business metric improvements, such as limiting surplus inventory. For an in-depth look at this project and how it has evolved, see “ CarMax drives business value with GPT-3.5.”
IBM Big Data Hub
NOVEMBER 14, 2023
A value exchange system built on data products can drive business growth for your organization and gain competitive advantage. This growth could be internal cost effectiveness, stronger risk compliance, increasing the economic value of a partner ecosystem, or through new revenue streams.
AWS Big Data
MARCH 26, 2024
The following figure shows some of the metrics derived from the study. In this post, we discuss how you can use purpose-built AWS services to create an end-to-end data strategy for C360 to unify and govern customer data that address these challenges. Organizations using C360 achieved 43.9% reduction in sales cycle duration, 22.8%
Domino Data Lab
MAY 8, 2019
The lens of reductionism and an overemphasis on engineering becomes an Achilles heel for data science work. Instead, consider a “full stack” tracing from the point of data collection all the way out through inference. He talked about the risks of reductionism and Descartes’ questionable legacy. Ergo, less interpretable.
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