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

JUNE 6, 2007

I am thrilled to say that my book Web Analytics: An Hour A Day has been published and is now widely available. Five Reasons Why You Should Buy Web Analytics: An Hour A Day: Web Analytics: An Hour A Day for the first time provides a Practitioner's in the trenches perspective on web analytics. There I said it.

Analytics 60

Analytics Metrics Marketing Dashboards 60

Occam's Razor

SEPTEMBER 15, 2014

They will need two different implementations, it is quite likely that you will end up with two sets of metrics (more people focused for mobile apps, more visit focused for sites). Remember, when someone says mobile analytics, first ask the clarifying question: Do you mean mobile application or mobile website ? Tag your mobile website.

Metrics 141

Metrics Reporting Analytics Marketing 141

Occam's Razor

JULY 18, 2011

You must use metrics that are unique to the medium. Ready for the best email marketing campaign metrics? So for our email campaign analysis let’s look at metrics using that framework. Optimal Acquisition Email Metrics. Most good email providers will do this automatically for whatever web analytics tool you use.

Metrics 137

Metrics Marketing Measurement Cost-Benefit 137

Rocket-Powered Data Science

JULY 6, 2021

2) MLOps became the expected norm in machine learning and data science projects. MLOps takes the modeling, algorithms, and data wrangling out of the experimental “one off” phase and moves the best models into deployment and sustained operational phase. This is critical in our massively data-sharing world and enterprises.

Machine Learning 279

Machine Learning Data-driven IoT Deep Learning 279

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

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

Occam's Razor

MAY 27, 2015

It is an investment in numerous report writers or data (puking) automation or hiring a small army in India or Philippines to do that, before investing in any smart Analyst. It is being hyper-conservative when it comes to creativity and experimentation because of quant-issues. Definitely buy my data books! :). < /A > ].

Marketing Analytics 153

Marketing Analytics Marketing Analytics Advertising 153

Occam's Razor

MARCH 23, 2010

There are more mistruths and F U D about Web analytics out there than I think is reasonable. Web Analytics, this beautiful child, was born just the other day in the midst of tumultuous times, quite literally, when everything changes every day. Here are ten web analytics ground truths… 1. Part of it fueled by Vendors.

Analytics 118

Analytics Consulting Metrics Experimentation 118

Domino Data Lab

FEBRUARY 4, 2019

We’ll unpack curiosity as a core attribute of effective data science, look at how that informs process for data science (in contrast to Agile, etc.), and dig into details about where science meets rhetoric in data science. That body of work has much to offer the practice of leading data science teams.

Data Science 66

Data Science Experimentation Data-driven Machine Learning 66

Occam's Razor

OCTOBER 19, 2010

What is the first thing you want when you think about web analytics? recommending tools for the complete web analytics 2.0 Disclosure:] I am the co-Founder of Market Motive Inc and the Analytics Evangelist for Google. Web Analytics 2.0. This blog post is about web analytics 2.0. Of course tools. Don't be.

Analytics 135

Analytics Testing Measurement Optimization 135

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.

Modeling 124

Modeling Metrics Data Quality Data-driven 124

Cloudera

JUNE 6, 2019

At the other end of the spectrum, the admin may instantiate a number of low-priority dev clusters – these clusters may often run at capacity, not require performance guarantees, but also provide more agility and flexibility for experimentation. Cloudera Manager 6.2 Learn more about Cloudera’s platform here.

Metadata 85

Metadata Data Lake Optimization Strategy 85

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

Occam's Razor

JANUARY 7, 2014

Every indicator we have is that companies are investing more in every facet of analytics. There is an analytics ladder of awesomeness each company needs to climb, and it just takes time. First… it is important to realize that big data's big imperative is driving big action. You know what your Return on Analytics is!

Data-driven 140

Data-driven Consulting Reporting Marketing 140

The Unofficial Google Data Science Blog

OCTOBER 12, 2017

by ERIC HOLLINGSWORTH A large part of the data we data scientists are asked to analyze was not collected with any specific analysis in mind, or perhaps any particular purpose at all. This post describes the analytical issues which arise in such a setting, and what the data scientist can do about them.

Experimentation 61

Experimentation Testing Statistics Metrics 61

Occam's Razor

APRIL 29, 2013

Marketer, is not spent with data you''ll fail to achieve professional success.]. Many used some data, but they unfortunately used silly data strategies/metrics. And silly simply because as soon as the strategy/success metric being obsessed about was mentioned, it was clear they would fail. You'll get fired.

Marketing 166

Marketing Metrics Advertising Measurement 166