The Unofficial Google Data Science Blog

DECEMBER 14, 2021

by AMIR NAJMI & MUKUND SUNDARARAJAN Data science is about decision making under uncertainty. Some of that uncertainty is the result of statistical inference, i.e., using a finite sample of observations for estimation. But there are other kinds of uncertainty, at least as important, that are not statistical in nature.

Uncertainty 56

Uncertainty Statistics Measurement Cost-Benefit 56

O'Reilly on Data

DECEMBER 9, 2019

Because of this trifecta of errors, we need dynamic models that quantify the uncertainty inherent in our financial estimates and predictions. Practitioners in all social sciences, especially financial economics, use confidence intervals to quantify the uncertainty in their estimates and predictions.

Statistics 31

Statistics Uncertainty Risk Marketing 31

The Unofficial Google Data Science Blog

JULY 22, 2020

Another reason to use ramp-up is to test if a website's infrastructure can handle deploying a new arm to all of its users. The website wants to make sure they have the infrastructure to handle the feature while testing if engagement increases enough to justify the infrastructure. We offer two examples where this may be the case.

Experimentation 96

Experimentation Statistics Testing Knowledge Discovery 96

CIO Business Intelligence

SEPTEMBER 19, 2023

How can enterprises attain these in the face of uncertainty? Rogers: This is one of two fundamental challenges of corporate innovation — managing innovation under high uncertainty and managing innovation far from the core — that I have studied in my work advising companies and try to tackle in my new book The Digital Transformation Roadmap.

Digital Transformation 130

Digital Transformation Uncertainty Testing Enterprise 130

Occam's Razor

APRIL 8, 2013

Sometimes, we escape the clutches of this sub optimal existence and do pick good metrics or engage in simple A/B testing. Testing out a new feature. Identify, hypothesize, test, react. But at the same time, they had to have a real test of an actual feature. You don’t need a beautiful beast to go out and test.

Metrics 157

Metrics KPI Analytics Key Performance Indicator 157

AWS Big Data

SEPTEMBER 5, 2024

Intuitively, for some extremely short user inputs, the vectors generated by dense vector models might have significant semantic uncertainty, where overlaying with a sparse vector model could be beneficial. Experimental data selection For retrieval evaluation, we used to use the datasets from BeIR. How to combine dense and sparse?

Metrics 110

Metrics Testing Experimentation Modeling 110

CIO Business Intelligence

SEPTEMBER 6, 2023

The use of AI-generated code is still in an experimental phase for many organizations due to numerous uncertainties such as its impact on security, data privacy, copyright, and more. Best practices and education Currently, there are no established best practices for leveraging AI in software development.

Software 119

Software Experimentation Risk Uncertainty 119

Mark Raskino

AUGUST 30, 2017

He was talking about something we call the ‘compound uncertainty’ that must be navigated when we want to test and introduce a real breakthrough digital business idea. You can connect social groups, economic groups and communities, which would be extraordinarily cumbersome and time-consuming in bigger societies”.

Testing 53

Testing Experimentation Uncertainty Risk 53

CIO Business Intelligence

JUNE 28, 2023

These circumstances have induced uncertainty across our entire business value chain,” says Venkat Gopalan, chief digital, data and technology officer, Belcorp. “As That, in turn, led to a slew of manual processes to make descriptive analysis of the test results. This allowed us to derive insights more easily.”

Digital Transformation 105

Digital Transformation Cost-Benefit Informatics Data mining 105

Domino Data Lab

JUNE 23, 2019

Skomoroch proposes that managing ML projects are challenging for organizations because shipping ML projects requires an experimental culture that fundamentally changes how many companies approach building and shipping software. Yet, this challenge is not insurmountable. for what is and isn’t possible) to address these challenges. Transcript.

Management 110

Management Machine Learning Experimentation Metrics 110

CIO Business Intelligence

OCTOBER 18, 2023

If anything, 2023 has proved to be a year of reckoning for businesses, and IT leaders in particular, as they attempt to come to grips with the disruptive potential of this technology — just as debates over the best path forward for AI have accelerated and regulatory uncertainty has cast a longer shadow over its outlook in the wake of these events.

Risk 141

Risk Manufacturing Enterprise Technology 141

The Unofficial Google Data Science Blog

APRIL 16, 2019

by MICHAEL FORTE Large-scale live experimentation is a big part of online product development. This means a small and growing product has to use experimentation differently and very carefully. This blog post is about experimentation in this regime. Such decisions involve an actual hypothesis test on specific metrics (e.g.

Experimentation 69

Experimentation Sales Metrics Measurement 69

The Unofficial Google Data Science Blog

JANUARY 14, 2016

Unlike experimentation in some other areas, LSOS experiments present a surprising challenge to statisticians — even though we operate in the realm of “big data”, the statistical uncertainty in our experiments can be substantial. We must therefore maintain statistical rigor in quantifying experimental uncertainty.

Experimentation 42

Experimentation Statistics Metrics Measurement 42

CIO Business Intelligence

JULY 31, 2023

As vendors add generative AI to their enterprise software offerings, and as employees test out the tech, CIOs must advise their colleagues on the pros and cons of gen AI’s use as well as the potential consequences of banning or limiting it. There’s a lot of uncertainty. People are thinking, ‘How is this going to affect my career?

Strategy 98

Strategy Risk Enterprise Risk Management 98

DataRobot Blog

OCTOBER 4, 2022

In the last few years, businesses have experienced disruptions and uncertainty on an unprecedented scale. However, hand-coding, testing, evaluating and deploying highly accurate models is a tedious and time-consuming process. Managing Through Socio-Economic Disruption.

Forecasting 59

Forecasting Sales Data-driven Modeling 59

IBM Big Data Hub

APRIL 18, 2024

While leaders have some reservations about the benefits of current AI, organizations are actively investing in gen AI deployment, significantly increasing budgets, expanding use cases, and transitioning projects from experimentation to production. The AGI would need to handle uncertainty and make decisions with incomplete information.

Cost-Benefit 89

Cost-Benefit Manufacturing Modeling Interactive 89

The Unofficial Google Data Science Blog

OCTOBER 21, 2015

The beliefs of this community are always evolving, and the process of thoughtfully generating, testing, refuting and accepting ideas looks a lot like Science. It is important to make clear distinctions among each of these, and to advance the state of knowledge through concerted observation, modeling and experimentation.

Slice and Dice 46

Slice and Dice Experimentation Data-driven Data Science 46

The Unofficial Google Data Science Blog

MAY 31, 2016

Similarly, we could test the effectiveness of a search ad compared to showing only organic search results. A geo experiment is an experiment where the experimental units are defined by geographic regions. Structure of a geo experiment A typical geo experiment consists of two distinct time periods: pretest and test.

Advertising 43

Advertising Testing Sales Statistics 43

CIO Business Intelligence

JANUARY 23, 2025

And while its beyond the scope of this article, the applicable knowledge gained through our hands-on experimentation with genAI was head and shoulders above simple web searches (e.g., He specializes in removing fear, uncertainty, and doubt from strategic decision-making through empirical data and market sensing.

Experimentation 116

Experimentation Uncertainty Data-driven Digital Transformation 116