Alation

MARCH 29, 2022

This led to the birth of separate systems for reporting: the enterprise data warehouse. For the first time, the focus of a system became business questions, where data was denormalized. The request model started to fray. But at its core, it’s about making data discoverable, trustworthy, and shareable.

Business Intelligence 96

Business Intelligence Reporting Data mining Data-driven 96

IBM Big Data Hub

JULY 6, 2023

It uses advanced tools to look at raw data, gather a data set, process it, and develop insights to create meaning. Areas making up the data science field include mining, statistics, data analytics, data modeling, machine learning modeling and programming.

Machine Learning 54

Machine Learning Data Science Statistics Deep Learning 54

Occam's Razor

APRIL 25, 2011

The issues of course include people and jaded mental models and bureaucracy and a lack of time and the missing desire to be great and org structures, and bosses. Not having a vibrant, engaging, non-pimpy blog. Having a vibrant blog does not mean not being on Twitter or Facebook (or every other place your customers congregate).

Marketing 126

Marketing Measurement Metrics Testing 126

Domino Data Lab

JULY 22, 2019

Paco Nathan presented, “Data Science, Past & Future” , at Rev. This blog post provides a concise session summary, a video, and a written transcript. data science’s emergence as an interdisciplinary field – from industry, not academia. The data governance, however, is still pretty much over on the data warehouse.

Data Science 109

Data Science Machine Learning Data Governance Modeling 109