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 361

Marketing Experimentation Metrics Testing 361

datapine

JANUARY 31, 2022

Using business intelligence and analytics effectively is the crucial difference between companies that succeed and companies that fail in the modern environment. Your Chance: Want to try a professional BI analytics software? This methodology of “test, look at the data, adjust” is at the heart and soul of business intelligence.

Business Intelligence 214

Business Intelligence Analytics Cost-Benefit ROI 214

O'Reilly on Data

MARCH 18, 2020

It seems as if the experimental AI projects of 2019 have borne fruit. By contrast, AI adopters are about one-third more likely to cite problems with missing or inconsistent data. The logic in this case partakes of garbage-in, garbage out : data scientists and ML engineers need quality data to train their models.

Enterprise 307

Enterprise Deep Learning Data Governance Risk 307

O'Reilly on Data

MARCH 31, 2020

Because it’s so different from traditional software development, where the risks are more or less well-known and predictable, AI rewards people and companies that are willing to take intelligent risks, and that have (or can develop) an experimental culture. These companies eventually moved beyond using data to inform product design decisions.

Management 327

Management Machine Learning Experimentation Metrics 327

Occam's Razor

DECEMBER 12, 2016

There is, almost literally, an unlimited number of things you could focus on to create a high impact data-influenced organization. And, as if unlimited is not enough, nearly every month your analytics vendors release new features, you discover new analytics solutions, and as your business is more successful (hurray!)

Analytics 141

Analytics Data-driven Strategy Consulting 141

Occam's Razor

AUGUST 13, 2012

A couple weeks back I'd requested the nice folks following me on Google+ and Facebook to submit their most important digital marketing and analytics questions. The questions reveal a bunch of things we used to worry about, and continue to, like data quality and creating data driven cultures. EU Cookies!)

Modeling 124

Modeling Metrics Data Quality Data-driven 124

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. Companies that succeed at machine learning tend to build on those existing analytics use cases.

Management 110

Management Machine Learning Experimentation Metrics 110