DataRobot Blog

APRIL 29, 2022

With the big data revolution of recent years, predictive models are being rapidly integrated into more and more business processes. This provides a great amount of benefit, but it also exposes institutions to greater risk and consequent exposure to operational losses.

Risk 111

Risk Modeling IT Risk Management 111

Smart Data Collective

MARCH 28, 2019

Nonetheless, the financial industry is using big data more than ever. The success of both Fintech companies and traditional banks will hinge on their ability to leverage big data to its fullest potential. How Financial Institutions Are Becoming the Unlikely Champions of Big Data.

Big Data 100

Big Data Statistics Risk Management 100

Smart Data Collective

APRIL 13, 2021

In the modern world of business, data is one of the most important resources for any organization trying to thrive. Business data is highly valuable for cybercriminals. They even go after meta data. Big data can reveal trade secrets, financial information, as well as passwords or access keys to crucial enterprise resources.

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Testing Behavioral Analytics Data-driven Big Data 124

Smart Data Collective

OCTOBER 31, 2022

By analyzing, identifying, and predicting these trends, analysts are able to help their clients minimize risk while enjoying large returns. Fortunately, the first robo-advisors were created in 2008. The flip side is that a system relying solely on AI data doesn’t make emotionally charged decisions.

Finance 138

Finance Cost-Benefit Marketing Statistics 138

Peter James Thomas

JUNE 20, 2019

Of course it can be argued that you can use statistics (and Google Trends in particular) to prove anything [1] , but I found the above figures striking. Here we come back to the upward trend in searches for Data Science. For example in 20 Risks that Beset Data Programmes. . [7]. Source: Google Trends.

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Cost-Benefit Data Warehouse Consulting Data Science 55

Domino Data Lab

JULY 22, 2019

He was saying this doesn’t belong just in statistics. He also really informed a lot of the early thinking about data visualization. It involved a lot of interesting work on something new that was data management. To some extent, academia still struggles a lot with how to stick data science into some sort of discipline.

Data Science 109

Data Science Machine Learning Data Governance Modeling 109

Domino Data Lab

AUGUST 8, 2019

2008 – Financial crisis : scientists flee Wall St. to join data science teams, e.g., to support advertising, social networks, gaming, and so on—I hired more than a few. 2018 – Global reckoning about data governance, aka “Oops! The probabilistic nature changes the risks and process required.

Data Science 71

Data Science Machine Learning Data Governance Statistics 71