DataRobot Blog

MAY 26, 2022

To start with, SR 11-7 lays out the criticality of model validation in an effective model risk management practice: Model validation is the set of processes and activities intended to verify that models are performing as expected, in line with their design objectives and business uses.

Risk 52

Risk Modeling Metrics Business Objectives 52

Sisense

MAY 4, 2020

These statistics highlight how large the task is to extract, transform, and load data that will be ready for analysis. According to an Accenture study, 79 percent of enterprise executives say that not embracing Big Data will cause companies to lose competitive position and risk extinction. Organizations must adapt or die.

Big Data 78

Big Data Data-driven Snapshot Unstructured Data 78

bridgei2i

MAY 26, 2020

It is even more essential now that supply chains are empowered with a high standard of data and analytics sophistication to be able to cost-effectively serve the company’s purpose and combat risks at the same time. And I’m sure our listeners will benefit from this. Anushruti: Perfect. Love the simile, by the way.

Enterprise 131

Enterprise Management Forecasting Manufacturing 131

The Unofficial Google Data Science Blog

OCTOBER 12, 2017

1]" Statistics, as a discipline, was largely developed in a small data world. More people than ever are using statistical analysis packages and dashboards, explicitly or more often implicitly, to develop and test hypotheses. This question is statistical or methodological in nature. Know what matters.

Experimentation 61

Experimentation Testing Statistics Metrics 61

Occam's Razor

JUNE 4, 2018

I mean developing and inserting a subtle collection of gentle nudges that can help increase the conversion rate by a statistically significant amount. Present a yummy spreadsheet that quantifies the cost of inaction , how much money you’ll lose by not delivering a 25% improvement every week. I don’t mean: BUY IT NOW OR ELSE!

Strategy 124

Strategy Cost-Benefit Testing Sales 124

Domino Data Lab

JANUARY 6, 2019

What are the projected risks for companies that fall behind for internal training in data science? Guess who benefits most by that? For another perspective: ask anyone who’s ever maintained a popular open source project about the benefits of publishing and peer review w.r.t. Downey (2011). Provide references. Think Stats.

Data Science 56

Data Science Machine Learning Reporting Visualization 56