Occam's Razor

APRIL 8, 2013

We are far too enamored with data collection and reporting the standard metrics we love because others love them because someone else said they were nice so many years ago. Because they find interaction with others rewarding and compelling. Online, offline or nonline. Yet this structure rarely exists in companies.

Metrics 156

Metrics KPI Analytics Key Performance Indicator 156

Insight

MARCH 12, 2020

In this article, I will discuss the construction of the AIgent, from data collection to model assembly. Data Collection The AIgent leverages book synopses and book metadata. The latter is any type of external data that has been attached to a book? Instead, I built the AIgent. In other words, if 0.1%

Modeling 59

Modeling Metadata Publishing Sales 59

Occam's Razor

JUNE 6, 2007

Bonus: Interactive CD: Contains six podcasts, one video, two web analytics metrics definitions documents and five insightful powerpoint presentations. Experimentation & Testing (A/B, Multivariate, you name it). Bonus: Interactive CD. Immediately actionable web analytics (your biggest worries covered). Clicks and outcomes.

Analytics 60

Analytics Metrics Marketing Dashboards 60

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. The biggest time sink is often around data collection, labeling and cleaning.

Management 110

Management Machine Learning Experimentation Metrics 110

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. Or something.

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. If the impacted user is not changing anything in the environment from his/her perspective, then predictability is expected in both the performance and the stability of workloads.

Metadata 85

Metadata Data Lake Optimization Strategy 85

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

In this post we will look mobile sites first, both data collection and analysis, and then mobile applications. Media-Mix Modeling/Experimentation. For the first couple of interactions, give her/him that data. In my case the interactive elements which are useful are clearly displayed above. Almost nothing.

Metrics 141

Metrics Reporting Analytics Marketing 141

Occam's Razor

DECEMBER 12, 2016

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. AND, that if I have good ideas, they will get to market very quickly – making our engagement worth the current interaction and the continued success of new ideas after the engagement.

Analytics 141

Analytics Data-driven Strategy Consulting 141

Occam's Razor

AUGUST 13, 2012

That's simply because this model is unique to my business and my understand of our data. The nice thing is that my custom attribution model will give me a unique view of the conversion path on MY site (a new column to look at under "% Change from Last Interaction"). Understanding the user interaction model.

Modeling 124

Modeling Metrics Data Quality Data-driven 124

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.

Marketing Analytics 153

Marketing Analytics Marketing Analytics Advertising 153

Occam's Razor

MARCH 23, 2010

Having two tools guarantees you are going to be data collection, data processing and data reconciliation organization. If you don't have a robust experimentation program in your company you are going to die. Oh and when I say Experimentation I don't mean testing button sizes (BOO!). Likely not.

Analytics 118

Analytics Consulting Metrics Experimentation 118

datapine

JANUARY 31, 2022

It’s all about using data to get a clearer understanding of reality so that your company can make more strategically sound decisions (instead of relying only on gut instinct or corporate inertia). Ultimately, business intelligence and analytics are about much more than the technology used to gather and analyze data.

Business Intelligence 214

Business Intelligence Analytics Cost-Benefit ROI 214