CIO Business Intelligence

OCTOBER 26, 2023

While there is little doubt that companies have been cutting back on expenses generally in response to economic uncertainty, startups in particular have been feeling the pain of contracting budgets and reluctant investors. At this point in time, it needs to be asked whether such a rapid increase in the number of vendors is sustainable.

IT 128

IT Cost-Benefit Data-driven Manufacturing 128

CIO Business Intelligence

JANUARY 10, 2023

Because of economic uncertainty, about 40% of CIOs slowed hiring as 2022 wound down, and about 30% experienced hiring freezes. Cold: Poaching high performers Market uncertainties have made recruiting more difficult in surprising ways, says Dru Kirk, vice president of talent acquisition for Marqeta.

IT 131

IT Uncertainty Data Science Marketing 131

AWS Big Data

AUGUST 7, 2023

Forecasting acts as a planning tool to help enterprises prepare for the uncertainty that can occur in the future. Many businesses use different software tools to analyze historical data and past patterns to forecast future demand and trends to make more accurate financial, marketing, and operational decisions.

Forecasting 96

Forecasting Data Warehouse Machine Learning Modeling 96

Domino Data Lab

JUNE 2, 2019

I recall a “Data Drinkup Group” gathering at a pub in Palo Alto, circa 2012, where I overheard Pete Skomoroch talking with other data scientists about Kahneman’s work. Clearly, when we work with data and machine learning, we’re swimming in those waters of decision-making under uncertainty.

Data Science 110

Data Science Data-driven Machine Learning Modeling 110

The Unofficial Google Data Science Blog

AUGUST 27, 2019

The bucketing method also changes the importance sampling to a stratified sampling setting, and allows us to use binomial confidence intervals to estimate the uncertainty of our estimate (more on that later). 5] Ray Chambers, Robert Clark (2012). Whether or not we borrow strength from other scores also impacts the estimation.

Metrics 98

Metrics Statistics Uncertainty Optimization 98

The Unofficial Google Data Science Blog

APRIL 23, 2024

Crucially, it takes into account the uncertainty inherent in our experiments. There is also uncertainty related to our modeling choices — did we select the correct polynomial embedding function $f(x)$, or is the true relationship better described by a different polynomial embedding?

Experimentation 52

Experimentation Optimization Uncertainty Metrics 52

The Unofficial Google Data Science Blog

JULY 11, 2017

This model has stationary distribution $$mu_infty sim Nleft(0, frac{sigma^2_eta}{1 - rho^2}right),$$ which means that uncertainty grows to a finite asymptote, rather than infinity, in the distant future. For example $$ mu_{t+1} = rho mu_{t} + eta_t,$$ with $eta_t sim N(0, sigma^2_eta)$ and $|rho| < 1$. and Chib, S. Benoit, D. Carlin, J.

Forecasting 66

Forecasting Statistics Modeling Software 66