Occam's Razor

AUGUST 13, 2012

Yehoshua I've covered this topic in detail in this blog post: Multi-Channel Attribution: Definitions, Models and a Reality Check. I explain three different models (Online to Store, Across Multiple Devices, Across Digital Channels) and for each I've highlighted: 1. What's possible to measure.

Modeling 124

Modeling Metrics Data Quality Data-driven 124

Domino Data Lab

MAY 8, 2019

Paco Nathan’s latest article features several emerging threads adjacent to model interpretability. I’ve been out themespotting and this month’s article features several emerging threads adjacent to the interpretability of machine learning models. Machine learning model interpretability. Introduction. 2018-06-21).

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

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. Taking a pulse.

Data Science 66

Data Science Experimentation Data-driven Machine Learning 66

Cloudera

JUNE 6, 2019

When a mix of batch, interactive, and data serving workloads are added to the mix, the problem becomes nearly intractable. This duplication can become such a management nightmare that they may prefer to return to the original model and simply live with noisy neighbors. A Better Approach: Virtual Private Clusters.

Metadata 88

Metadata Data Lake Optimization Strategy 88

Occam's Razor

DECEMBER 12, 2016

You got me, I am ignoring all the data layer and custom stuff! But, at the end of the day presence of a Tag Manager communicates to me that the company is serious about data collection and data quality. Digital Marketing & Measurement Model. All that is great. That's it. Problematic, right? Management.

Analytics 141

Analytics Data-driven Strategy Consulting 141

AWS Big Data

JULY 3, 2023

We use Iceberg tags to label market data snapshots to avoid look-ahead bias in the research data lake, which will enable us to test various trade entry and exit scenarios and assess the respective profitability of each. Sell 1 (PVH, PVH) 2022-09-06 18321.729571 55.15 Sell 2 (INVH, INVH) 2022-09-06 27419.797094 38.20

Snapshot 85

Snapshot Data Lake Testing Strategy 85

Occam's Razor

SEPTEMBER 15, 2014

In this post we will look mobile sites first, both data collection and analysis, and then mobile applications. Media-Mix Modeling/Experimentation. Upsight (nee Kontagent) provides mobile app analytics, with a pinch of advanced segmentation (including sweet cohort analysis ) and big data mining thrown in for good measure.

Metrics 141

Metrics Reporting Analytics Marketing 141

Occam's Razor

MAY 27, 2015

(even if you've never visited the site) has access to tons of intent signals from you right now, tons of third-party cookies that litter your browser right now, and immense Big Data and algorithms. It is being hyper-conservative when it comes to creativity and experimentation because of quant-issues. Does Yahoo!

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. There is a lot of intent data on the web.

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." There is never a boring moment, there is never time when you can’t do something faster or smarter.

Marketing 136

Marketing Analytics Machine Learning Strategy 136