O'Reilly on Data

JULY 28, 2020

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

Marketing Experimentation Metrics Testing 362

CIO Business Intelligence

MARCH 21, 2022

As such, a data scientist must have enough business domain expertise to translate company or departmental goals into data-based deliverables such as prediction engines, pattern detection analysis, optimization algorithms, and the like. Data scientists can help with this process.

Unstructured Data 131

Unstructured Data Data Analytics Analytics Structured Data 131

O'Reilly on Data

MAY 14, 2020

AI PMs should enter feature development and experimentation phases only after deciding what problem they want to solve as precisely as possible, and placing the problem into one of these categories. Experimentation: It’s just not possible to create a product by building, evaluating, and deploying a single model.

Management 363

Management Experimentation B2B Machine Learning 363

Occam's Razor

MAY 27, 2015

Here are the digital myths that are leading us down a profoundly sub-optimal path: 1. A data-first strategy is a winning formula. Programmatic advertising is all the rage. Per our friends at Wikipedia, Programmatic encompasses an array of technologies that automate the buying, placement and optimization of media inventory.

Marketing Analytics 153

Marketing Analytics Marketing Analytics Advertising 153

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.

Management 110

Management Machine Learning Experimentation Metrics 110

Domino Data Lab

MAY 8, 2019

The lens of reductionism and an overemphasis on engineering becomes an Achilles heel for data science work. Instead, consider a “full stack” tracing from the point of data collection all the way out through inference. Keep in mind that data science is fundamentally interdisciplinary. Let’s look through some antidotes.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

Occam's Razor

DECEMBER 12, 2016

I was asked a few weeks back: " What companies should we proactively help with analytics, for free, so that they can make smarter data-influenced decisions ?" You got me, I am ignoring all the data layer and custom stuff! Even if you follow the 10/90 rule, it is important to focus our time and resources optimally.

Analytics 141

Analytics Data-driven Strategy Consulting 141