IBM Big Data Hub

MAY 17, 2024

” Given the statistics—82% of surveyed respondents in a 2023 Statista study cited managing cloud spend as a significant challenge—it’s a legitimate concern. Teams are comfortable with experimentation and skilled in using data to inform business decisions. Why move to cloud?

Modeling 96

Modeling Cost-Benefit Optimization Digital Transformation 96

The Unofficial Google Data Science Blog

JANUARY 14, 2016

Unlike experimentation in some other areas, LSOS experiments present a surprising challenge to statisticians — even though we operate in the realm of “big data”, the statistical uncertainty in our experiments can be substantial. We must therefore maintain statistical rigor in quantifying experimental uncertainty.

Experimentation 41

Experimentation Statistics Metrics Measurement 41

CIO Business Intelligence

JUNE 11, 2024

A 1958 Harvard Business Review article coined the term information technology, focusing their definition on rapidly processing large amounts of information, using statistical and mathematical methods in decision-making, and simulating higher order thinking through applications.

IT 136

IT Digital Transformation Broadcasting Business Objectives 136

The Unofficial Google Data Science Blog

APRIL 16, 2019

by MICHAEL FORTE Large-scale live experimentation is a big part of online product development. This means a small and growing product has to use experimentation differently and very carefully. This blog post is about experimentation in this regime. Such decisions involve an actual hypothesis test on specific metrics (e.g.

Experimentation 68

Experimentation Sales Metrics Measurement 68

CIO Business Intelligence

AUGUST 21, 2024

The flashpoint moment is that rather than being based on rules, statistics, and thresholds, now these systems are being imbued with the power of deep learning and deep reinforcement learning brought about by neural networks,” Mattmann says. But multiagent AI systems are still in the experimental stages, or used in very limited ways.

Risk 136

Risk Insurance Cost-Benefit Software 136

DataRobot Blog

JULY 28, 2022

In addition to the accuracy of the models we built, we had to consider business metrics, cost, interpretability, and suitability for ongoing operations. Initially, the customer tried modeling using statistical methods to create typical features, such as moving averages, but the model metrics (R-square) was only 0.5

Data Science 72

Data Science Machine Learning Statistics Modeling 72

CIO Business Intelligence

MARCH 21, 2022

According to William Chen, Data Science Manager at Quora , the top five skills for data scientists include a mix of hard and soft skills: Programming: The “most fundamental of a data scientist’s skill set,” programming improves your statistics skills, helps you “analyze large datasets,” and gives you the ability to create your own tools, Chen says.

Unstructured Data 137

Unstructured Data Data Analytics Analytics Data Science 137

Occam's Razor

MAY 22, 2006

This post is a primer on the delightful world of testing and experimentation (A/B, Multivariate, and a new term from me: Experience Testing). Experimentation and testing help us figure out we are wrong, quickly and repeatedly and if you think about it that is a great thing for our customers, and for our employers. Counter claims?

Experimentation 103

Experimentation Testing Optimization Measurement 103

Octopai

OCTOBER 26, 2022

When DataOps principles are implemented within an organization, you see an increase in collaboration, experimentation, deployment speed and data quality. Continuous pipeline monitoring with SPC (statistical process control). Continuous DataOps metrics testing checks data’s validity, completeness and integrity at input and output.

Data Quality 52

Data Quality Data Analytics Analytics Manufacturing 52

The Unofficial Google Data Science Blog

FEBRUARY 29, 2016

In this post we explore why some standard statistical techniques to reduce variance are often ineffective in this “data-rich, information-poor” realm. Despite a very large number of experimental units, the experiments conducted by LSOS cannot presume statistical significance of all effects they deem practically significant.

Experimentation 40

Experimentation Statistics Metrics Measurement 40

Occam's Razor

MARCH 23, 2021

This is very hard to do, we now have a proven seven-step experimentation process, with one of the coolest algorithms to pick matched-markets (normally the kiss of death of any large-scale geo experiment). The benchmark for the beautiful metric AVOC is 15.3%. What does the diminishing returns curve look like for TV GRPs for our company?

Analytics 133

Analytics Scorecard Strategy Testing 133

Sisense

JANUARY 21, 2020

One of the simplest ways to start exploring your data is to aggregate the metrics you are interested in by their relevant dimensions. This is an example of Simpon’s paradox , a statistical phenomenon in which a trend that is present when data is put into groups reverses or disappears when the data is combined.

Testing 104

Testing Risk Data-driven Statistics 104

Occam's Razor

AUGUST 22, 2011

" ~ Web Metrics: "What is a KPI? " + Standard Metrics Revisited Series. "Engagement" Is Not A Metric, It's An Excuse. Defining a "Master Metric", + a Framework to Gain a Competitive Advantage in Web Analytics. Build A Great Web Experimentation & Testing Program.

KPI 125

KPI Metrics Measurement ROI 125

Domino Data Lab

DECEMBER 4, 2020

Having calculated AUC/AUMC, we can further derive a number of useful metrics like: Total clearance of the drug from plasma. Domino Lab supports both interactive and batch experimentation with all popular IDEs and notebooks (Jupyter, RStudio, SAS, Zeppelin, etc.). The area under the first moment curve would respectively be. cl_f = NCA.cl(pain_nca)

Metrics 59

Metrics Data Science Knowledge Discovery Measurement 59

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. Another pattern that I’ve seen in good PMs is that they’re very metric-driven.

Management 110

Management Machine Learning Experimentation Metrics 110

Occam's Razor

JULY 24, 2018

work (collection, processing, reporting, analysis), processes, org structure, governance models, last-mile gaps , metrics ladders of awesomeness , and… so… much… more. Remember, tools, work, processes, org structure, governance models, last-mile gaps, metrics ladders of awesomeness, and… so… much… more. The Implications of Complexity.

Analytics 95

Analytics Metrics Strategy Measurement 95

The Unofficial Google Data Science Blog

JANUARY 16, 2018

Experimentation on networks A/B testing is a standard method of measuring the effect of changes by randomizing samples into different treatment groups. However, the downside of using a larger unit of randomization is that we lose experimental power. Consider the case where experiment metrics are evaluated at the per-user level.

Testing 58

Testing Experimentation Measurement Modeling 58

O'Reilly on Data

APRIL 19, 2021

We’ll look at this later, but being able to reproduce experimental results is critical to any science, and it’s a well-known problem in AI. But is application deployment the right metric for maturity? It’s more concerning that workflow reproducibility (3%) is in second-to-last place. Bottlenecks to AI adoption. Maturity by Continent.

Enterprise 358

Enterprise Risk Modeling Manufacturing 358

Corinium

APRIL 25, 2019

Life insurance needs accurate data on consumer health, age and other metrics of risk. For example auto insurance companies offering to capture real-time driving statistics from policy-holders’ cars to encourage and reward safe driving. And more recently, we have also seen innovation with IOT (Internet Of Things).

Insurance 150

Insurance Risk IoT Cost-Benefit 150

The Unofficial Google Data Science Blog

JULY 31, 2016

To figure this out, let's consider an appropriate experimental design. In other words, the teacher is our second kind of unit, the unit of experimentation. This type of experimental design is known as a group-randomized or cluster-randomized trial. When analyzing the outcome measure (e.g.,

Experimentation 42

Experimentation Testing Measurement Metrics 42

Occam's Razor

NOVEMBER 17, 2008

There is a tendency to think experimentation and testing is optional. So you don't have to worry about integrations with analytics tools, you don't have to worry about rushing to get a PhD in Statistics to interpret results and what not. So as my tiny gift for you here are five experimentation and testing ideas for you.

Testing 113

Testing Experimentation Risk Optimization 113

The Unofficial Google Data Science Blog

MARCH 22, 2018

But what if users don't immediately uptake the new experimental version? Background At Google, experimentation is an invaluable tool for making decisions and inference about new products and features. by DANIEL PERCIVAL Randomized experiments are invaluable in making product decisions, including on mobile apps.

Experimentation 62

Experimentation Measurement Modeling Statistics 62

The Unofficial Google Data Science Blog

MAY 31, 2016

A geo experiment is an experiment where the experimental units are defined by geographic regions. This means it is possible to specify exactly in which geos an ad campaign will be served – and to observe the ad spend and the response metric at the geo level. They are non-overlapping geo-targetable regions. by turning campaigns off).

Advertising 42

Advertising Testing Sales Statistics 42

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. Because every tool uses its own sweet metrics definitions, cookie rules, session start and end rules and so much more. Part of it fueled by some Consultants.

Analytics 119

Analytics Consulting Metrics Experimentation 119

Business Over Broadway

MAY 22, 2019

According to Gartner, companies need to adopt these practices: build culture of collaboration and experimentation; start with a 3-way partnership among executives leading digital initiative, line of business and IT. Also, loyalty leaders infuse analytics into CX programs, including machine learning, data science and data integration.

Machine Learning 40

Machine Learning Technology Digital Transformation Data Science 40

The Unofficial Google Data Science Blog

JUNE 30, 2016

In an ideal world, experimentation through randomization of the treatment assignment allows the identification and consistent estimation of causal effects. Identification We now discuss formally the statistical problem of causal inference. We start by describing the problem using standard statistical notation.

Statistics 44

Statistics Optimization Modeling Experimentation 44

Domino Data Lab

AUGUST 22, 2019

Although it’s not perfect, [Note: These are statistical approximations, of course!] We need to take a brief break from natural language-specific content here to introduce a metric that will come in handy in the next section of the chapter, when we will evaluate the performance of deep learning NLP models. Example 11.6 0.85 = 0.15.

Deep Learning 111

Deep Learning Modeling Metrics Testing 111

Domino Data Lab

AUGUST 26, 2019

To support the iterative and experimental nature of industry work, Domino reached out to Addison-Wesley Professional (AWP) for appropriate permissions to excerpt the “Tuning Hyperparameters and Pipelines” from the book, Machine Learning with Python for Everyone by Mark E. algorithm leaf_size metric metric_params n_jobs n_neighbors p weights.

Testing 79

Testing Modeling Machine Learning Metrics 79

Domino Data Lab

MAY 8, 2019

If your “performance” metrics are focused on predictive power, then you’ll probably end up with more complex models, and consequently less interpretable ones. They also require advanced skills in statistics, experimental design, causal inference, and so on – more than most data science teams will have.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

The Unofficial Google Data Science Blog

OCTOBER 21, 2015

It is important to make clear distinctions among each of these, and to advance the state of knowledge through concerted observation, modeling and experimentation. Note also that this account does not involve ambiguity due to statistical uncertainty. We sliced and diced the experimental data in many many ways.

Slice and Dice 45

Slice and Dice Experimentation Data-driven Data Science 45

Occam's Razor

JANUARY 9, 2012

If you are doing lame stuff, why try harder in an analytics context by asking for Economic Value or Visitor Loyalty or Conversation Rate or a thousand other super powerful and insightful metrics ? AND you can have analysis of your risk in almost real time to get an early read and in a few days with statistical significance!

Marketing 142

Marketing B2B Measurement Risk 142

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). Immediately actionable web analytics (your biggest worries covered). It is a book about Web Analytics 2.0.

Analytics 60

Analytics Metrics Marketing Dashboards 60

Darkhorse

NOVEMBER 8, 2016

Strip plots in the flesh: NPR looks at school funding The LA Times on pop music’s evolution Eurostat’s key European statistics Jittered Strip Plot. Choosing the right number of bins can have an impact on how any of these charts look, but a bit of experimentation usually leads to a reasonable answer.

Visualization 71

Visualization Experimentation Measurement Statistics 71

Insight

MARCH 12, 2020

The most powerful approach for the first task is to use a ‘language model’ (LM), i.e. a statistical model of natural language. After some experimentation, I landed on a strategy I’ll call ‘warm encoding’: if greater than 1% of tags were in a particular class, I encoded the book as belonging to that class, non-exclusively.

Modeling 59

Modeling Metadata Publishing Sales 59

The Unofficial Google Data Science Blog

OCTOBER 12, 2017

1]" Statistics, as a discipline, was largely developed in a small data world. With more features come more potential post hoc hypotheses about what is driving metrics of interest, and more opportunity for exploratory analysis. Data was expensive to gather, and therefore decisions to collect data were generally well-considered.

Experimentation 61

Experimentation Testing Statistics Metrics 61

IBM Big Data Hub

SEPTEMBER 25, 2023

Presto provides a long list of functions, operators, and expressions as part of its open source offering, including standard functions, maps, arrays, mathematical, and statistical functions. Data Exploration and Innovation: The flexibility of Presto has encouraged data exploration and experimentation at Uber.

OLAP 86

OLAP Data Lake Data-driven Snapshot 86