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

AWS Big Data

JULY 3, 2023

Major market indexes, such as S&P 500, are subject to periodic inclusions and exclusions for reasons beyond the scope of this post (for an example, refer to CoStar Group, Invitation Homes Set to Join S&P 500; Others to Join S&P 100, S&P MidCap 400, and S&P SmallCap 600 ).

Snapshot 83

Snapshot Data Lake Testing Strategy 83

Occam's Razor

JUNE 6, 2007

Experimentation & Testing (A/B, Multivariate, you name it). What ideas to test first on your site? The book is for CEO's and Marketer's and Search Specialists and Web Designers and User Researchers and Website Administrators and Website Owners and HiPPO's and of course Web Analysts. Clicks and outcomes.

Analytics 60

Analytics Metrics Marketing Dashboards 60

Occam's Razor

JULY 18, 2011

To not have it as an active part of your marketing portfolio is sub-optimal. The only requirement is that your mental model (and indeed, company culture) should be solidly rooted in permission marketing. Embrace permission marketing and email will be a surprising and loyal BFF. Every fiber of your being. That is okay.

Metrics 137

Metrics Marketing Measurement Cost-Benefit 137

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. Without large amounts of labeled training data solving most AI problems is not possible. Transcript.

Management 110

Management Machine Learning Experimentation Metrics 110

DataRobot Blog

OCTOBER 4, 2022

The shift in consumer habits and geopolitical crises have rendered data patterns collected pre-COVID obsolete. This has prompted AI/ML model owners to retrain their legacy models using data from the post-COVID era, while adapting to continually fluctuating market trends and thinking creatively about forecasting.

Forecasting 59

Forecasting Sales Data-driven Modeling 59

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

SEPTEMBER 15, 2014

If you look at the mobile marketing strategies, you will see they don't reflect this shift to mobile. In this post we will look mobile sites first, both data collection and analysis, and then mobile applications. Media-Mix Modeling/Experimentation. The only reason good old digital is beating TV is mobile.

Metrics 141

Metrics Reporting Analytics Marketing 141

Occam's Razor

MARCH 23, 2010

Having two tools guarantees you are going to be data collection, data processing and data reconciliation organization. In case of WebTrends replace those terms with Marketing Intelligence / Visitor Data Mart etc. Likely not. Omniture cannot save you. Only you can save yourself.

Analytics 118

Analytics Consulting Metrics Experimentation 118

Cloudera

JUNE 6, 2019

We sometimes refer to this as splitting “dev/test” from “production” workloads, but we can generalize the approach by referring to the overall priority of the workload for the business. Lakshmi Randall is Director of Product Marketing at Cloudera, the enterprise data cloud company. 3) By workload priority.

Metadata 86

Metadata Data Lake Strategy Optimization 86

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

Occam's Razor

OCTOBER 19, 2010

Disclosure:] I am the co-Founder of Market Motive Inc and the Analytics Evangelist for Google. None of these tools vendors have any relationship with Market Motive either. If you can show ROI on a DW it would be a good use of your money to go with Omniture Discover, WebTrends Data Mart, Coremetrics Explore. Three tools.

Analytics 135

Analytics Testing Measurement Optimization 135

datapine

JANUARY 31, 2022

For example, in regards to marketing, traditional advertising methods of spending large amounts of money on TV, radio, and print ads without measuring ROI aren’t working like they used to. Everything is being tested, and then the campaigns that succeed get more money put into them, while the others aren’t repeated.

Business Intelligence 214

Business Intelligence Analytics Cost-Benefit ROI 214

Occam's Razor

JANUARY 7, 2014

4: The Analytics/Marketing skills in your Analysis Ninjas is 70/30. #3. The organization functions off a clearly defined Digital Marketing & Measurement Model. #1. Digital Analyst, Web Analysis Guru, Digital Marketing Analyst, so on and so forth. More on the Digital Marketing & Measurement Model, DMMM, in #2 below.).

Data-driven 140

Data-driven Consulting Reporting Marketing 140

Occam's Razor

APRIL 29, 2013

It turns out that Marketers, especially Digital Marketers, make really silly mistakes when it comes to data. Small data. In the last couple months I've spent a lot of time with senior level marketers on three different continents. Some absolutely did not use data to do their digital jobs.

Marketing 166

Marketing Metrics Advertising Measurement 166

Occam's Razor

OCTOBER 12, 2017

The tiny downside of this is that our parents likely never had to invest as much in constant education, experimentation and self-driven investment in core skills. Like this blog, it will be particularly relevant for those who are in digital analytics and digital marketing. None focusses on digital marketing or analytics.

Marketing 136

Marketing Analytics Machine Learning Strategy 136