Smart Data Collective

FEBRUARY 23, 2022

Many companies are going to have to revamp their entire business models in order to deal with the new technological changes brought on by advances in the IoT. We have many examples of companies that refuse to previous changes in the market and eventually collapse because of their persistent attachment to an old-fashioned business model.

IoT 128

IoT Internet of Things Data-driven Uncertainty 128

Jedox

OCTOBER 8, 2020

We are currently operating in an environment with a very high (if not the highest ever) level of VUCA, (Volatility, Uncertainty, Complexity, Ambiguity). The way you mitigate uncertainty is with planning, planning, and more planning. The oil collapse of 2014 is another example of the importance of scenario planning.

Uncertainty 75

Uncertainty Cost-Benefit Unstructured Data Data-driven 75

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. Amazon Redshift ML makes it easy for data analysts and database developers to create, train, and apply machine learning (ML) models using familiar SQL commands in Amazon Redshift. You pay only the associated Forecast costs.

Forecasting 98

Forecasting Data Warehouse Machine Learning Modeling 98

The Unofficial Google Data Science Blog

JULY 11, 2017

SCOTT Time series data are everywhere, but time series modeling is a fairly specialized area within statistics and data science. This post describes the bsts software package, which makes it easy to fit some fairly sophisticated time series models with just a few lines of R code. by STEVEN L. Forecasting (e.g.

Forecasting 66

Forecasting Statistics Modeling Software 66

The Unofficial Google Data Science Blog

APRIL 17, 2017

Selection and aggregation of forecasts from an ensemble of models to produce a final forecast. Quantification of forecast uncertainty via simulation-based prediction intervals. Calendaring was therefore an explicit feature of models within our framework, and we made considerable investment in maintaining detailed regional calendars.

Forecasting 83

Forecasting Modeling Statistics Uncertainty 83

The Unofficial Google Data Science Blog

APRIL 23, 2024

Crucially, it takes into account the uncertainty inherent in our experiments. Experiments, Parameters and Models At Youtube, the relationships between system parameters and metrics often seem simple — straight-line models sometimes fit our data well. It is a big picture approach, worthy of your consideration.

Experimentation 52

Experimentation Optimization Uncertainty Metrics 52

The Unofficial Google Data Science Blog

MARCH 13, 2017

Typically, causal inference in data science is framed in probabilistic terms, where there is statistical uncertainty in the outcomes as well as model uncertainty about the true causal mechanism connecting inputs and outputs. We are all familiar with linear and logistic regression models.

IT 70

IT Visualization Modeling Uncertainty 70