DataRobot

JUNE 8, 2021

The proposed cognitive benefit of personas is that humans are better at thinking in narratives than abstract data about customers. Unlike personas, however, cluster analysis is data-driven. But despite the allure of a data-driven approach, cluster analysis is arbitrary. Personas are subjectively constructed.

Data-driven 98

Data-driven Testing Marketing Experimentation 98

The Unofficial Google Data Science Blog

APRIL 23, 2024

If $Y$ at that point is (statistically and practically) significantly better than our current operating point, and that point is deemed acceptable, we update the system parameters to this better value. Modeling live experiment data Data scientists at YouTube are rarely involved in the analysis of typical live traffic experiments.

Experimentation 52

Experimentation Optimization Uncertainty Metrics 52

Occam's Razor

AUGUST 22, 2011

Be data driven?" Six Rules For Creating A Data Driven Boss! Be data driven?" Slay The Analytics Data Quality Dragon & Win Your HiPPO's Love! Web Data Quality: A 6 Step Process To Evolve Your Mental Model. The Ultimate Web Analytics Data Reconciliation Checklist.

KPI 124

KPI Metrics Measurement ROI 124

Alation

SEPTEMBER 28, 2022

The term has been used a lot more of late, especially in the data analytics industry, as we’ve seen it expand over the past few years to keep pace with new regulations, like the GDPR and CCPA. In essence, DataOps is a practice that helps organizations manage and govern data more effectively. What exactly is DataOps ?

Cost-Benefit 52

Cost-Benefit Data Quality Manufacturing Testing 52

The Unofficial Google Data Science Blog

DECEMBER 14, 2021

by AMIR NAJMI & MUKUND SUNDARARAJAN Data science is about decision making under uncertainty. Some of that uncertainty is the result of statistical inference, i.e., using a finite sample of observations for estimation. But there are other kinds of uncertainty, at least as important, that are not statistical in nature.

Uncertainty 56

Uncertainty Statistics Measurement Cost-Benefit 56

Occam's Razor

SEPTEMBER 19, 2011

You need people with deep skills in Scientific Method , Design of Experiments , and Statistical Analysis. Then they isolated regions of the country (by city, zip, state, dma pick your fave) into test and control regions. People in the test regions will participate in our hypothesis testing. ask for a raise. None of that.

Measurement 119

Measurement Advertising Testing Marketing 119

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. Sometimes, we escape the clutches of this sub optimal existence and do pick good metrics or engage in simple A/B testing. Testing out a new feature.

Metrics 156

Metrics KPI Analytics Key Performance Indicator 156

Ontotext

APRIL 3, 2024

Joseph Hilger : People are starting to understand that knowledge graphs are not just a tool for storing data and information. I need something that defines what those entities are and can align them with the data.” The other use case where graphs are exploding is what Gartner calls a data fabric. What are your thoughts about it?

Technology 59

Technology Risk Digital Transformation Measurement 59

The Unofficial Google Data Science Blog

JUNE 30, 2016

By IVAN DIAZ & JOSEPH KELLY Determining the causal effects of an action—which we call treatment—on an outcome of interest is at the heart of many data analysis efforts. In an ideal world, experimentation through randomization of the treatment assignment allows the identification and consistent estimation of causal effects.

Statistics 44

Statistics Optimization Modeling Experimentation 44

O'Reilly on Data

MARCH 23, 2023

But Transformers have some other important advantages: Transformers don’t require training data to be labeled; that is, you don’t need metadata that specifies what each sentence in the training data means. Unlike labels, embeddings are learned from the training data, not produced by humans.

IT 261

IT Modeling Testing Risk 261

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

Occam's Razor

JUNE 6, 2007

Experimentation & Testing (A/B, Multivariate, you name it). What ideas to test first on your site? Benchmarking (exactly how you can do it), impactful actionable executive dashboards (what they should contain), creating a data driven organization. It is a book about Web Analytics 2.0. Clicks and outcomes.

Analytics 60

Analytics Metrics Marketing Dashboards 60

Domino Data Lab

MAY 8, 2019

For example, common practices for collecting data to build training datasets tend to throw away valuable information along the way. The lens of reductionism and an overemphasis on engineering becomes an Achilles heel for data science work. ML model interpretability and data visualization. back to the structure of the dataset.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

The Unofficial Google Data Science Blog

OCTOBER 21, 2015

by NIALL CARDIN, OMKAR MURALIDHARAN, and AMIR NAJMI When working with complex systems or phenomena, the data scientist must often operate with incomplete and provisional understanding, even as she works to advance the state of knowledge. There has been debate as to whether the term “data science” is necessary. Some don’t see the point.

Slice and Dice 45

Slice and Dice Experimentation Data-driven Data Science 45

Occam's Razor

MARCH 23, 2010

Part of it is fueled by a vocal minority genuinely upset that 10 years on we are still not a statistically powered bunch doing complicated analysis that is shifting paradigms. Having two tools guarantees you are going to be data collection, data processing and data reconciliation organization. This instant. Likely not.

Analytics 118

Analytics Consulting Metrics Experimentation 118

Domino Data Lab

AUGUST 26, 2019

Data scientists, machine learning (ML) researchers, and business stakeholders have a high-stakes investment in the predictive accuracy of models. Data scientists and researchers ascertain predictive accuracy of models using different techniques, methodologies, and settings, including model parameters and hyperparameters. Introduction.

Testing 79

Testing Modeling Machine Learning Metrics 79

Occam's Razor

JANUARY 9, 2012

Now here's another surprise: These rules/insights/mind shifts are not about data! Here's important context (before we get into the rules for revolutionaries)… The Fundamental Web Analytics Problem Is Not Data! Most companies are astonishingly blasé about data and possibilities of measurement.

Marketing 140

Marketing B2B Measurement Risk 140

IBM Big Data Hub

SEPTEMBER 25, 2023

The magic behind Uber’s data-driven success Uber, the ride-hailing giant, is a household name worldwide. But what most people don’t realize is that behind the scenes, Uber is not just a transportation service; it’s a data and analytics powerhouse. Consider the magnitude of Uber’s footprint.

OLAP 87

OLAP Data Lake Data-driven Snapshot 87