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

SEPTEMBER 15, 2014

Company UX leaders are happy to stink less by taking the sub-optimal path of responsive design, rather than create a mobile-unique experience (your customers tend to do different things on your desktop site than your mobile site!). In this post we will look mobile sites first, both data collection and analysis, and then mobile applications.

Metrics 141

Metrics Reporting Analytics Marketing 141

Occam's Razor

AUGUST 13, 2012

It is hard, it is time consuming, but it also allows you to test your hypotheses on possible optimal allocations, test them in the real world, find the best answers and be brilliant with your marketing spend mix. I can use that to hypothesize what an optimal budget allocation might look like.

Modeling 124

Modeling Metrics Data Quality Data-driven 124

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. Per our friends at Wikipedia, Programmatic encompasses an array of technologies that automate the buying, placement and optimization of media inventory. Programmatic platforms are a panacea.

Marketing Analytics 153

Marketing Analytics Marketing Analytics Advertising 153

The Unofficial Google Data Science Blog

OCTOBER 12, 2017

Implicitly, there was a prior belief about some interesting causal mechanism or an underlying hypothesis motivating the collection of the data. As computing and storage have made data collection cheaper and easier, we now gather data without this underlying motivation.

Experimentation 61

Experimentation Testing Statistics Metrics 61

Occam's Razor

JANUARY 7, 2014

First… it is important to realize that big data's big imperative is driving big action. Second… well there is no second, it is all about the big action and getting a big impact on your bottom-line from your big investment in analytics processes, consulting, people and tools.

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. Here's why… Real-time data is very expensive. It is expensive from a systems/platforms/data processing/data reporting perspective. PPS: I've mentioned one exception in the past.

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. Years and years of practice with R or "Big Data." Optimal Starting SCOTUS Starting Points. This reality powers my impostor syndrome, and (yet?)

Marketing 136

Marketing Analytics Machine Learning Strategy 136