Cloudera

JULY 15, 2021

In this session we explored what firms are doing to approach the uncertainty with more predictability. Across the industry, the pandemic caused a huge breakdown in model performance due to the change in macroeconomic conditions and government stimulus packages. Observe what the model has to offer even if not the intended output.

Risk 102

Risk Optimization Management Insurance 102

erwin

MAY 6, 2020

In these times of great uncertainty and massive disruption, is your enterprise data helping you drive better business outcomes? The COVID-19 pandemic has forced organizations to tactically adjust their business models, work practices and revenue projections for the short term.

Cost-Benefit 94

Cost-Benefit Data Governance Uncertainty Data-driven 94

DataRobot

JUNE 1, 2021

In an earlier post, I shared the four foundations of trusted performance in AI : data quality, accuracy, robustness and stability, and speed. Before you put a model into production, you may first need to clear compliance hurdles. Typically, these areas require the most attention: model development, implementation, and use.

Uncertainty 52

Uncertainty Machine Learning Advertising Risk 52

CIO Business Intelligence

APRIL 28, 2022

Right from the start, auxmoney leveraged cloud-enabled analytics for its unique risk models and digital processes to further its mission. However, many financial services companies still prefer to build their own data centers rather than leverage cloud solutions. We see this demonstrated in S-Bank , ranked No.

Analytics 98

Analytics Insurance Big Data Uncertainty 98

Timo Elliott

JANUARY 4, 2021

First, because uncertainty exploded. Business people want more data than ever. We’re no longer talking about tinkering at the margins of a stable business model. It’s now about using data for survival in the present and new business models in the future. But recently, there has been a surge in demand.

Dashboards 139

Dashboards Visualization Data-driven Forecasting 139

CIO Business Intelligence

JANUARY 4, 2023

Here are the shifts we noted: Delivery Models : Moving from an almost strictly on-premises systems approach (with some remote access services), HPC is embracing remote delivery models. Of course, data center challenges are also driving demand for these alternatives.

Cost-Benefit 57

Cost-Benefit Machine Learning Modeling Uncertainty 57

Octopai

APRIL 16, 2020

Some data teams working remotely are making the most of the situation with advanced metadata management tools that help them deliver faster and more accurately, ensuring business as usual, even during coronavirus. Smarter Business Intelligence is an Asset During Uncertainty.

Metadata 59

Metadata Business Intelligence Uncertainty Data Quality 59

CIO Business Intelligence

MARCH 29, 2022

Fortunately, learning-based projects typically use data collected for other purposes. . It’s also important to go back to raw data to ask new questions and train new models, particularly as the world is constantly changing. You’ve lost that opportunity if the data has been thrown away. . • You have data but don’t use it.

Cost-Benefit 52

Cost-Benefit Machine Learning Metadata Data Quality 52

DataKitchen

SEPTEMBER 21, 2023

Bridging the Gap: How ‘Data in Place’ and ‘Data in Use’ Define Complete Data Observability In a world where 97% of data engineers report burnout and crisis mode seems to be the default setting for data teams, a Zen-like calm feels like an unattainable dream. What is Data in Use?

Testing 169

Testing Data Quality Predictive Modeling Metrics 169

Darkhorse

AUGUST 26, 2016

You have devised a number of time-tested shortcuts to deal with uncertainty. keeps talking about his models as if they’re some kind of authority: “The model says so”. You’ll choose simpler models. You’ve surrounded yourself with people you trust and you weigh their advice before making major decisions.

Uncertainty 40

Uncertainty Data Quality Modeling Testing 40

O'Reilly on Data

OCTOBER 13, 2020

Similarly, in “ Building Machine Learning Powered Applications: Going from Idea to Product ,” Emmanuel Ameisen states: “Indeed, exposing a model to users in production comes with a set of challenges that mirrors the ones that come with debugging a model.”.

Management 244

Management Metrics Machine Learning Modeling 244

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

datapine

MAY 31, 2022

More and more CRM, marketing, and finance-related tools use SaaS business intelligence and technology, and even Adobe’s Creative Suite has adopted the model. Businesses can instead access extra processing through pay-as-you-go models from public providers.

Risk 237

Risk Cost-Benefit Business Intelligence Data-driven 237

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

IBM Big Data Hub

JUNE 22, 2023

Technology-enabled business process operations, the new BPO, can significantly create new value, improve data quality, free precious employee resources, and deliver higher customer satisfaction, but it requires a holistic approach. “Modern BPO is a creator of growth, differentiation and competitive advantage,” Manik says.

Consulting 78

Consulting Insurance Finance Uncertainty 78

Peter James Thomas

AUGUST 12, 2018

However, often the biggest stumbling block is a human one, getting people to buy in to the idea that the care and attention they pay to data capture will pay dividends later in the process. These and other areas are covered in greater detail in an older article, Using BI to drive improvements in data quality. million ± £0.5

Metrics 49

Metrics Statistics Data Quality Measurement 49

Domino Data Lab

JUNE 23, 2019

It’s harder for many folks in this role who don’t have any data or machine learning background and are thrown or thrust into shipping something like this. They lack probably what a lot of folks in this room have in terms of domain expertise around data, machine learning, and how these models work.

Management 110

Management Machine Learning Experimentation Metrics 110

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

Cloudera

OCTOBER 8, 2020

Businesses are now faced with more data, and from more sources, than ever before. But knowing what to do with that data, and how to do it, is another thing entirely. . Poor data quality costs upwards of $3.1 Ninety-five percent of businesses cite the need to manage unstructured data as a real problem.

Digital Transformation 109

Digital Transformation Experimentation Unstructured Data Uncertainty 109