erwin

JUNE 7, 2022

Accordingly, data modelers must embrace some new tricks when designing data warehouses and data marts. Data modeling for the cloud: good database design means “right size” and savings. Now to cover some data modeling basics that applies no matter whether on-premises or in the cloud. Data Modeling. Business Focus.

Data Warehouse 52

Data Warehouse Modeling Sales Data Lake 52

CIO Business Intelligence

AUGUST 18, 2023

For organizations seeking a collaborative win-win approach to outsourcing, the Vested sourcing business model is worth consideration. It is the product of nearly 20 years of research at the University of Tennessee, beginning with a deep-dive funded by the United States Air Force on outcome-based outsourcing in 2003.

Measurement 86

Measurement Modeling Risk Management 86

CIO Business Intelligence

MARCH 9, 2022

Based in Abu Dhabi and in operation since 2003, in recent years Etihad has used a data lake and a unified set of AI-driven analytics tools to optimise staffing, the handling of passengers, and responses to customer inquiries. Etihad wanted to deploy, schedule and automate their data models very rapidly. Talal Mufti. Martin Hammer.

Data Science 83

Data Science Data Lake Cost-Benefit Digital Transformation 83

BizAcuity

FEBRUARY 15, 2019

Sales Analytics in simple terms can be defined as the process used to identify, understand, predict and model sales trends and sales results and in this process of understanding of these trends helps its users in finding improvement points. Image Source: [link].

Sales 67

Sales Analytics ROI B2B 67

bridgei2i

MAY 26, 2020

You know the markets shake and the accompanying Swine Flu epidemic of 2015 and 2016, the Japanese tsunami and the Thailand floods in 2011 that shook up the high-tech value chain quite a bit, the great financial crisis and the accompanying H1N1 outbreak in 2008-2009, MERS and SARS before that in 2003.

Enterprise 131

Enterprise Management Forecasting Manufacturing 131

The Unofficial Google Data Science Blog

DECEMBER 4, 2019

by THOMAS OLAVSON Thomas leads a team at Google called "Operations Data Science" that helps Google scale its infrastructure capacity optimally. Others argue that there will still be a unique role for the data scientist to deal with ambiguous objectives, messy data, and knowing the limits of any given model.

Forecasting 113

Forecasting Data Science Statistics Uncertainty 113

The Unofficial Google Data Science Blog

NOVEMBER 4, 2015

But most common machine learning methods don’t give posteriors, and many don’t have explicit probability models. More precisely, our model is that $theta$ is drawn from a prior that depends on $t$, then $y$ comes from some known parametric family $f_theta$. Here, our items are query-ad pairs. Calculate posterior quantities of interest.

KDD 40

KDD Testing Machine Learning Modeling 40