CIO Business Intelligence

NOVEMBER 14, 2022

Decision support systems definition A decision support system (DSS) is an interactive information system that analyzes large volumes of data for informing business decisions. A DSS leverages a combination of raw data, documents, personal knowledge, and/or business models to help users make decisions. Analytics, Data Science

Data mining 144

Data mining Data-driven Statistics OLAP 144

DataRobot

AUGUST 5, 2021

In behavioral science this is known as the blemish frame , where a small negative provides a frame of comparison to much stronger positives, strengthening the positive messaging. AI and Uncertainty. Some people react to the uncertainty with fear and suspicion. People are unsure about AI because it’s new. AI you can trust.

Uncertainty 98

Uncertainty Sales Risk Statistics 98

CIO Business Intelligence

JUNE 28, 2022

Philosophers and economists may argue about the quality of the metaphor, but there’s no doubt that organizing and analyzing data is a vital endeavor for any enterprise looking to deliver on the promise of data-driven decision-making. And to do so, a solid data management strategy is key.

Management 138

Management Internet of Things Data-driven Statistics 138

O'Reilly on Data

FEBRUARY 11, 2020

The responses show a surfeit of concerns around data quality and some uncertainty about how best to address those concerns. Key survey results: The C-suite is engaged with data quality. His insight was a corrective to the collective bias of the Army’s Statistical Research Group (SRG). This last point is important.

Data Quality 363

Data Quality Metadata Data Governance Publishing 363

Data Science and Beyond

JANUARY 7, 2019

Bootstrap sampling techniques are very appealing, as they don’t require knowing much about statistics and opaque formulas. Instead, all one needs to do is resample the given data many times, and calculate the desired statistics. Don’t compare confidence intervals visually. Pitfall #1: Inaccurate confidence intervals.

Statistics 77

Statistics Uncertainty Testing Modeling 77

The Unofficial Google Data Science Blog

JULY 22, 2020

For example, imagine a fantasy football site is considering displaying advanced player statistics. A ramp-up strategy may mitigate the risk of upsetting the site’s loyal users who perhaps have strong preferences for the current statistics that are shown. One reason to do ramp-up is to mitigate the risk of never before seen arms.

Experimentation 96

Experimentation Statistics Testing Knowledge Discovery 96

The Unofficial Google Data Science Blog

AUGUST 27, 2019

But importance sampling in statistics is a variance reduction technique to improve the inference of the rate of rare events, and it seems natural to apply it to our prevalence estimation problem. Statistical Science. Statistics in Biopharmaceutical Research, 2010. [4] High Risk 10% 5% 33.3% How Many Strata? 7] Neyman, J.

Metrics 98

Metrics Statistics Uncertainty Optimization 98

Insight

MAY 15, 2020

In this time of terrifying uncertainty, some might focus on their own career journey over others. Wishlists are especially off-putting to women, who statistically will only apply to job opportunities if they meet 100% of the listed requirements, versus men applying when they meet 60%. Are you hiring in data science, AI, or engineering?

Uncertainty 76

Uncertainty Data Science Statistics Marketing 76

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. Crucially, it takes into account the uncertainty inherent in our experiments.

Experimentation 52

Experimentation Optimization Uncertainty Metrics 52

The Unofficial Google Data Science Blog

JULY 11, 2017

SCOTT Time series data are everywhere, but time series modeling is a fairly specialized area within statistics and data science. Introduction Time series data appear in a surprising number of applications, ranging from business, to the physical and social sciences, to health, medicine, and engineering.

Forecasting 66

Forecasting Statistics Modeling Software 66

The Unofficial Google Data Science Blog

APRIL 17, 2017

Quantification of forecast uncertainty via simulation-based prediction intervals. We conclude with an example of our forecasting routine applied to publicly available Turkish Electricity data. Prediction Intervals A statistical forecasting system should not lack uncertainty quantification. Forecasting data and methods".

Forecasting 83

Forecasting Modeling Statistics Uncertainty 83

Peter James Thomas

JUNE 20, 2019

Of course it can be argued that you can use statistics (and Google Trends in particular) to prove anything [1] , but I found the above figures striking. Here we come back to the upward trend in searches for Data Science. King was a wise King, but now he was gripped with uncertainty. The scope is worldwide.

Cost-Benefit 55

Cost-Benefit Data Warehouse Data Science Consulting 55

The Unofficial Google Data Science Blog

MARCH 13, 2017

By MUKUND SUNDARARAJAN, ANKUR TALY, QIQI YAN Editor's note: Causal inference is central to answering questions in science, engineering and business and hence the topic has received particular attention on this blog.

IT 70

IT Visualization Modeling Uncertainty 70

Peter James Thomas

AUGUST 12, 2018

I explore some similar themes in a section of Data Visualisation – A Scientific Treatment. Integrity of statistical estimates based on Data. Having spent 18 years working in various parts of the Insurance industry, statistical estimates being part of the standard set of metrics is pretty familiar to me [7].

Metrics 49

Metrics Statistics Data Quality Measurement 49

The Unofficial Google Data Science Blog

MARCH 31, 2016

We often use statistical models to summarize the variation in our data, and random effects models are well suited for this — they are a form of ANOVA after all. In the context of prediction problems, another benefit is that the models produce an estimate of the uncertainty in their predictions: the predictive posterior distribution.

Modeling 40

Modeling Statistics Advertising Testing 40

Domino Data Lab

JUNE 23, 2019

All you need to know, for now, is that machine learning is a field of artificial intelligence that uses statistical techniques to give computer systems the ability to learn based on data by being trained on past examples. They have the foundations of data infrastructure.

Management 110

Management Machine Learning Experimentation Metrics 110

IBM Big Data Hub

APRIL 18, 2024

Achieving these feats is accomplished through a combination of sophisticated algorithms, natural language processing (NLP) and computer science principles. LLMs like ChatGPT are trained on massive amounts of text data, allowing them to recognize patterns and statistical relationships within language.

Cost-Benefit 89

Cost-Benefit Manufacturing Modeling Interactive 89

The Unofficial Google Data Science Blog

OCTOBER 21, 2015

Our post describes how we arrived at recent changes to design principles for the Google search page, and thus highlights aspects of a data scientist’s role which involve practicing the scientific method. There has been debate as to whether the term “data science” is necessary. Some don’t see the point.

Slice and Dice 46

Slice and Dice Experimentation Data-driven Data Science 46

The Unofficial Google Data Science Blog

MAY 31, 2016

Statistical power is traditionally given in terms of a probability function, but often a more intuitive way of describing power is by stating the expected precision of our estimates. This is a quantity that is easily interpretable and summarizes nicely the statistical power of the experiment. In the U.S.,

Advertising 43

Advertising Testing Sales Statistics 43

Domino Data Lab

JULY 22, 2019

Paco Nathan presented, “Data Science, Past & Future” , at Rev. At Rev’s “ Data Science, Past & Future” , Paco Nathan covered contextual insight into some common impactful themes over the decades that also provided a “lens” help data scientists, researchers, and leaders consider the future.

Data Science 109

Data Science Machine Learning Data Governance Modeling 109

The Unofficial Google Data Science Blog

JANUARY 14, 2016

by AMIR NAJMI Running live experiments on large-scale online services (LSOS) is an important aspect of data science. Because individual observations have so little information, statistical significance remains important to assess. We must therefore maintain statistical rigor in quantifying experimental uncertainty.

Experimentation 42

Experimentation Statistics Metrics Measurement 42

The Unofficial Google Data Science Blog

JULY 18, 2023

Editor's note : The relationship between reliability and validity are somewhat analogous to that between the notions of statistical uncertainty and representational uncertainty introduced in an earlier post. But for more complicated metrics like xRR, our preference is to bootstrap when measuring uncertainty.

Measurement 52

Measurement Metrics Uncertainty Slice and Dice 52

Insight

FEBRUARY 19, 2020

Using variability in machine learning predictions as a proxy for risk can help studio executives and producers decide whether or not to green light a film project Photo by Kyle Smith on Unsplash Originally posted on Toward Data Science. Are you interested in working on high-impact projects and transitioning to a career in data?

Risk 67

Risk ROI Modeling Metrics 67

The Unofficial Google Data Science Blog

APRIL 16, 2019

On the other hand, fledgling products often have neither the statistical power to identify the effects of small incremental changes, nor the luxury to contemplate small improvements. The binomial confidence intervals we computed earlier may greatly underestimate the uncertainty in our inference.

Experimentation 69

Experimentation Sales Metrics Measurement 69

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 41

Experimentation Statistics Metrics Measurement 41

The Unofficial Google Data Science Blog

MARCH 24, 2025

See how much you agree with the authors view of the importance of these questions in assessing practical data science ability. Defining "Data Scientist" If you look through job listings at Google for data scientists , you will find a role called Data Scientist - Research (DS-R for short).

Statistics 58

Statistics Testing Interactive Sales 58