The Unofficial Google Data Science Blog

OCTOBER 7, 2015

by HENNING HOHNHOLD, DEIRDRE O'BRIEN, and DIANE TANG In this post we discuss the challenges in measuring and modeling the long-term effect of ads on user behavior. We describe experiment designs which have proven effective for us and discuss the subtleties of trying to generalize the results via modeling.

Modeling 61

Modeling Experimentation Knowledge Discovery Testing 61

Paul Blogs on BI

MARCH 25, 2020

We are really missing data and so the metrics are incomplete and, as such, we should ask ourselves the question: “Are we measuring the right things?”. Some universities and institutions have built out predictive models based on this data which are even more likely to be erroneous. Stay healthy.

Business Intelligence 51

Business Intelligence Measurement Metrics Cost-Benefit 51

Octopai

DECEMBER 11, 2022

Banks didn’t accurately assess their credit and operational risk and hold enough capital reserves, leading to the Great Recession of 2008-2009. The banking system’s inability to deal with the Great Recession of 2008-2009 led to the passage of regulations designed to make banks more responsible in preparing for financial risk.

Insurance 52

Insurance Risk Metadata Reporting 52

DataKitchen

MAY 10, 2021

Since 2008, teams working for our founding team and our customers have delivered 100s of millions of data sets, dashboards, and models with almost no errors. Best practices include continuous monitoring of machine learning models for degradations in accuracy. . Week after week, it is measured with a million rows.

Testing 214

Testing Manufacturing Data Quality Statistics 214

Smart Data Collective

APRIL 5, 2022

We have been publishing content on data analytics since 2008, but surprising new discoveries in big data are still made every year. The model quality may suffer because too much sunlight may add a lot of reflections to the images the drone is capturing, which in turn reduces data quality. Well, that’s incorrect.

Data Collection 107

Data Collection Data Quality Big Data Data-driven 107

Sisense

MARCH 24, 2020

Twelve years ago, in the throes of the 2008 economic recession, British Airways was cutting costs across the organization. With smart dashboards and KPIs , the BraunAbility team is able to tie discount data from marketing platforms to sales results in order to more effectively measure the impact of any discount. Insights over instinct.

Analytics 94

Analytics Sales Forecasting Business Intelligence 94

Jet Global

MARCH 5, 2019

AI, colloquially, is used to refer to a number of computer-powered business decision drivers, including automation (not AI), data modeling (not AI), and reporting and analytics (also not AI). But are those tools powered by artificial intelligence? So what can AI actually do?

Business Intelligence 75

Business Intelligence OLAP Visualization Reporting 75

Domino Data Lab

MARCH 27, 2019

Select all rows from the 2008 election. votes_2008 <- filter(presidentialElections, year == 2008). The preceding filter() statement is equivalent to extracting the rows using the following base R syntax: # Select all rows from the 2008 election. votes_2008 <- presidentialElections[presidentialElections$year == 2008, ].

Statistics 85

Statistics Data Science Visualization IT 85

Cloudera

JUNE 23, 2021

Evolving your analytics and risk models to accommodate climate change inputs and regulations beyond weather-related natural disasters is increasingly important. Insurers will have to adjust their models frequently to match local markets. . Climate change modeling will challenge organizations to think and act differently.

Insurance 52

Insurance Risk Modeling Risk Management 52

Domino Data Lab

MAY 20, 2021

In this article we discuss why fitting models on imbalanced datasets is problematic, and how class imbalance is typically addressed. This renders measures like classification accuracy meaningless. The use of multiple measurements in taxonomic problems. A weighted nearest neighbor algorithm for learning with symbolic features.

Machine Learning 136

Machine Learning Metrics Data mining Knowledge Discovery 136

The Unofficial Google Data Science Blog

APRIL 17, 2017

Selection and aggregation of forecasts from an ensemble of models to produce a final forecast. Calendaring was therefore an explicit feature of models within our framework, and we made considerable investment in maintaining detailed regional calendars. Adjustments for effects: holiday, seasonality, and day-of-week effects.

Forecasting 83

Forecasting Modeling Statistics Uncertainty 83

O'Reilly on Data

JULY 15, 2019

In recent posts, we described requisite foundational technologies needed to sustain machine learning practices within organizations, and specialized tools for model development, model governance, and model operations/testing/monitoring. Sources of model risk. Model risk management. Image by Ben Lorica.

Machine Learning 37

Machine Learning Management Enterprise Risk Management 37

Occam's Razor

OCTOBER 14, 2014

[See step four in the process for creating your Digital Marketing and Measurement Model.]. should be 1,356,000), you've set a clear line in the sand as to what performance will be declared a success or a failure at the end of the measurement time period. See Page 269. :). So how can you use your own data?

Key Performance Indicator 133

Key Performance Indicator Metrics Reporting Marketing 133

Domino Data Lab

AUGUST 8, 2019

Meanwhile, many organizations also struggle with “late in the pipeline issues” on model deployment in production and related compliance. Their approach is to bombard “organoid” mini brains living in vats with potential cancer meds, to measure the meds’ relative effects. 2008 – Financial crisis : scientists flee Wall St.

Data Science 71

Data Science Machine Learning Data Governance Statistics 71