Cloudera

JULY 15, 2021

In this session we explored what firms are doing to approach the uncertainty with more predictability. integrate such extreme events, Machine Learning and AI can be utilized to help take a fresh look at models and how they can be scaled. ML and AI can react quickly and handle mass amounts of data to give leading indicators.

Risk 99

Risk Optimization Management Insurance 99

CIO Business Intelligence

APRIL 28, 2022

However, many financial services companies still prefer to build their own data centers rather than leverage cloud solutions. Much of this reluctance stems from the regulatory environment, arising from lengthy reviews and approvals processes, or even simple near-term regulatory uncertainty. .

Analytics 78

Analytics Insurance Big Data Uncertainty 78

CIO Business Intelligence

MARCH 29, 2022

It isn’t practical to save all your data, but it is important to realize data may be valuable for other projects. You lose that add-on value when you throw data away. . This type of data waste results in missing out on the second project advantage. And the problem is not just a matter of too many copies of data.

Cost-Benefit 52

Cost-Benefit Machine Learning Metadata Data Quality 52

CIO Business Intelligence

JANUARY 4, 2023

Moving forward, we will see workflows that are more capable and widely adopted to facilitate edge-core-cloud needs like generating meshes, performing 3D simulations, performing post-simulation data analysis, and feeding data into machine learning models—which support, guide, and in some case replace the need for simulation.

Cost-Benefit 57

Cost-Benefit Machine Learning Modeling Uncertainty 57

Domino Data Lab

JUNE 23, 2019

Pete Skomoroch ’s “ Product Management for AI ”session at Rev provided a “crash course” on what product managers and leaders need to know about shipping machine learning (ML) projects and how to navigate key challenges. Be aware that machine learning often involves working on something that isn’t guaranteed to work.

Management 110

Management Machine Learning Experimentation Metrics 110

Timo Elliott

JANUARY 4, 2021

First, because uncertainty exploded. Organizations are interested in anything that can help, such as self-service, cloud, machine learning, and robotic process automation. As I’ve said, data storytelling isn’t fundamentally about technology. Dashboards and analytics have been around for a long, long time.

Dashboards 139

Dashboards Visualization Data-driven Forecasting 139

Jen Stirrup

NOVEMBER 15, 2022

The foundation should be well structured and have essential data quality measures, monitoring and good data engineering practices. Systems thinking helps the organization frame the problems in a way that provides actionable insights by considering the overall design, not just the data on its own.

Data Science 145

Data Science Digital Transformation Data-driven Measurement 145

Domino Data Lab

JULY 22, 2019

data science’s emergence as an interdisciplinary field – from industry, not academia. why data governance, in the context of machine learning is no longer a “dry topic” and how the WSJ’s “global reckoning on data governance” is potentially connected to “premiums on leveraging data science teams for novel business cases”.

Data Science 109

Data Science Machine Learning Data Governance Modeling 109

The Unofficial Google Data Science Blog

JULY 18, 2023

Editor's note : The relationship between reliability and validity are somewhat analogous to that between the notions of statistical uncertainty and representational uncertainty introduced in an earlier post. But for more complicated metrics like xRR, our preference is to bootstrap when measuring uncertainty.

Measurement 52

Measurement Metrics Uncertainty Slice and Dice 52