article thumbnail

Towards optimal experimentation in online systems

The Unofficial Google Data Science Blog

the weight given to Likes in our video recommendation algorithm) while $Y$ is a vector of outcome measures such as different metrics of user experience (e.g., Taking measurements at parameter settings further from control parameter settings leads to a lower variance estimate of the slope of the line relating the metric to the parameter.

article thumbnail

Business Strategies for Deploying Disruptive Tech: Generative AI and ChatGPT

Rocket-Powered Data Science

Third, any commitment to a disruptive technology (including data-intensive and AI implementations) must start with a business strategy. I suggest that the simplest business strategy starts with answering three basic questions: What? encouraging and rewarding) a culture of experimentation across the organization.

Strategy 289
Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

Robust Experimentation and Testing | Reasons for Failure!

Occam's Razor

Since you're reading a blog on advanced analytics, I'm going to assume that you have been exposed to the magical and amazing awesomeness of experimentation and testing. And yet, chances are you really don’t know anyone directly who uses experimentation as a part of their regular business practice. Wah wah wah waaah.

article thumbnail

Bringing an AI Product to Market

O'Reilly on Data

Without clarity in metrics, it’s impossible to do meaningful experimentation. AI PMs must ensure that experimentation occurs during three phases of the product lifecycle: Phase 1: Concept During the concept phase, it’s important to determine if it’s even possible for an AI product “ intervention ” to move an upstream business metric.

Marketing 362
article thumbnail

Get AI in the hands of your employees

CIO Business Intelligence

Stephen Franchetti, CIO of Samsara, a fleet management SaaS provider that went public in 2021, believes the only way to optimize your AI strategy (or any emerging technology strategy, in fact) is with a bottoms-up approach. We’ve seen an ongoing iteration of experimentation with a number of promising pilots in production,” he says.

KPI 96
article thumbnail

Do You Need a DataOps Dojo?

DataKitchen

Centralizing analytics helps the organization standardize enterprise-wide measurements and metrics. Central DataOps process measurement function with reports. A COE typically has a full-time staff that focuses on delivering value for customers in an experimentation-driven, iterative, result-oriented, customer-focused way.

Metrics 243
article thumbnail

10 digital transformation roadblocks — and 5 tips for overcoming them

CIO Business Intelligence

Develop a clear strategy: A clear strategy that outlines goals and objectives, timelines, and resources required is essential for digital transformation success. Foster a culture of innovation: Digital transformation requires innovation and experimentation, and thus a culture for embracing new technologies and ideas.