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. You should first identify potential compliance risks, with each additional step again tested against risks. That might mean a piece of data is an outlier. Is rain 40% likely?

Uncertainty 52

Uncertainty Machine Learning Advertising Risk 52

O'Reilly on Data

MARCH 31, 2020

Machine learning adds uncertainty. Underneath this uncertainty lies further uncertainty in the development process itself. There are strategies for dealing with all of this uncertainty–starting with the proverb from the early days of Agile: “ do the simplest thing that could possibly work.”

Management 332

Management Machine Learning Experimentation Metrics 332

Jet Global

JULY 5, 2023

The Impact of Market Uncertainty This year, Finance decision-makers are feeling pressure from both internal and external sources. With heightened scrutiny on organizations and leaders, organizations can’t afford such a high risk of error. And manual processes increase the likelihood of reporting mistakes.

Finance 52

Finance Uncertainty Reporting Marketing 52

DataRobot

JULY 16, 2021

They all serve to answer the question, “How well can my model make predictions based on data?” In performance, the trust dimensions are the following: Data quality — the performance of any machine learning model is intimately tied to the data it was trained on and validated against.

Machine Learning 98

Machine Learning Uncertainty Modeling Measurement 98

Anmut

OCTOBER 29, 2020

As a result, concerns of data governance and data quality were ignored. The direct consequence of bad quality data is misinformed decision making based on inaccurate information; the quality of the solutions is driven by the quality of the data.

Data Governance 52

Data Governance Data Collection Data-driven Statistics 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 176

Testing Data Quality Predictive Modeling Metrics 176

IBM Big Data Hub

SEPTEMBER 11, 2023

Government executives face several uncertainties as they embark on their journeys of modernization. and quality (how does this impact service delivery, business process and data quality?). frequency (how many occurrences?), time (how much time is lost?)

Data-driven 90

Data-driven IT Measurement Cost-Benefit 90

Domino Data Lab

JULY 22, 2019

What I’m trying to say is this evolution of system architecture, the hardware driving the software layers, and also, the whole landscape with regard to threats and risks, it changes things. You see these drivers involving risk and cost, but also opportunity. One is data quality, cleaning up data, the lack of labelled data.

Data Science 109

Data Science Machine Learning Data Governance Modeling 109

Jet Global

MARCH 13, 2020

In a Deloitte survey, 67 percent of the 1,048 executives who participated said they were uncomfortable accessing data using the tools they have available. They prefer to ask an accountant or someone from IT to retrieve data for them. Worse, those consequences are now more probable with data volumes rising rapidly.

Finance 64

Finance Snapshot Reporting Advertising 64

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.

Metrics 49

Metrics Statistics Data Quality Measurement 49

Domino Data Lab

JUNE 23, 2019

If you have a user facing product, the data that you had when you prototype the model may be very different from what you actually have in production. This really rewards companies with an experimental culture where they can take intelligent risks and they’re comfortable with those uncertainties.

Management 110

Management Machine Learning Experimentation Metrics 110

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 102

Digital Transformation Experimentation Unstructured Data Uncertainty 102