DataRobot

AUGUST 5, 2021

AI and Uncertainty. Some people react to the uncertainty with fear and suspicion. Recently published research addressed the question of “ When Does Uncertainty Matter?: Understanding the Impact of Predictive Uncertainty in ML Assisted Decision Making.”. People are unsure about AI because it’s new. AI you can trust.

Uncertainty 98

Uncertainty Sales Risk Statistics 98

CIO Business Intelligence

FEBRUARY 12, 2024

Such bleak statistics suggest that indecision around how to proceed with genAI is paralyzing organizations and preventing them from developing strategies that will unlock value. High-quality data will be the oil that makes your models hum. Some of this will hinge on what you want your model to do.

IT 137

IT Consulting Cost-Benefit Uncertainty 137

Cloudera

DECEMBER 3, 2021

Systems should be designed with bias, causality and uncertainty in mind. For example, training an interview screening model using education data often contains gender information. As discussed in this article , model design can also be a source of bias too. Model Drift. System Design. Human Judgement & Oversight.

Uncertainty 102

Uncertainty Measurement Metrics Risk 102

Rocket-Powered Data Science

JULY 19, 2023

This was not a scientific or statistically robust survey, so the results are not necessarily reliable, but they are interesting and provocative. The ease with which such structured data can be stored, understood, indexed, searched, accessed, and incorporated into business models could explain this high percentage.

Data-driven 188

Data-driven Enterprise Analytics Machine Learning 188

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

Smart Data Collective

SEPTEMBER 22, 2020

Statistics over time have proven that the firearms industry does exceptionally well under two conditions: right before a presidential election and during a national crisis. Certain AR-15 rifle and pistol models are also hard to find unless you’re willing to pay three times the usual price. Currently, the U.S.

Big Data 127

Big Data Marketing Sales Manufacturing 127

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. His insight was a corrective to the collective bias of the Army’s Statistical Research Group (SRG). Key survey results: The C-suite is engaged with data quality. The SRG could not imagine that it was missing data.

Data Quality 363

Data Quality Metadata Data Governance Publishing 363

CIO Business Intelligence

JUNE 28, 2022

More importantly, we also have statistical models that draw error bars that delineate the limits of our analysis. Good data scientists can also reduce some of this uncertainty through cleansing. People even spell their names differently from year to year, day to day, or even line to line on the same form.

Management 138

Management Internet of Things Statistics Data-driven 138

Jen Stirrup

NOVEMBER 15, 2022

From a data science perspective, it is possible to begin immediately by framing problems regarding machine learning or statistical issues. VUCA stands for volatility, uncertainty, complexity, and ambiguity; these terms could be relevant to many data-based projects. Data strategy in a VUCA environment. Data can be hard to predict.

Data Science 145

Data Science Digital Transformation Data-driven Measurement 145

Perceptual Edge

APRIL 21, 2019

While they might argue that their smooth model illustrates the relationship in a simpler manner, I would argue that it over-simplifies the relationship if they only report the model without also revealing the actual data on which it was based.

Visualization 76

Visualization Statistics Uncertainty Modeling 76

Sisense

OCTOBER 6, 2020

Most commonly, we think of data as numbers that show information such as sales figures, marketing data, payroll totals, financial statistics, and other data that can be counted and measured objectively. All descriptive statistics can be calculated using quantitative data. Digging into quantitative data. This is quantitative data.

Statistics 98

Statistics Unstructured Data Data-driven Visualization 98

CIO Business Intelligence

AUGUST 14, 2023

The collaboration between Huabao and SAP continued as plans for a new foundation to support corporate development and business model transformation gathered speed. Capitalizing on SAP’s in-memory database, the solution is renowned for meeting the exact challenges Huabao hoped to address navigating uncertainty and refining business results.

Key Performance Indicator 98

Key Performance Indicator Sales Manufacturing KPI 98

CIO Business Intelligence

JANUARY 2, 2024

CIOs are readying for another demanding year, anticipating that artificial intelligence, economic uncertainty, business demands, and expectations for ever-increasing levels of speed will all be in play for 2024. But at the end of the day, it boils down to statistics. Statistics can be very misleading.

IT 144

IT Cost-Benefit Statistics Consulting 144

CIO Business Intelligence

NOVEMBER 3, 2023

Women disproportionately affected by burnout For women, the statistics around burnout are even worse. You’ll need to consider increases in resources, mentoring, opportunities for advancement, as well as evaluating boundaries around work-life balance and ensuring that a healthy balance is reflected and modeled all the way to the top.

IT 133

IT Reporting Uncertainty Cost-Benefit 133

CIO Business Intelligence

JUNE 28, 2023

These circumstances have induced uncertainty across our entire business value chain,” says Venkat Gopalan, chief digital, data and technology officer, Belcorp. “As Belcorp operates under a direct sales model in 14 countries. Its brands include ésika, L’Bel, and Cyzone, and its products range from skincare and makeup to fragrances.

Digital Transformation 105

Digital Transformation Cost-Benefit Informatics Data mining 105

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. And sometimes even if it is not[1].)

Experimentation 52

Experimentation Optimization Uncertainty Metrics 52

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

IBM Big Data Hub

SEPTEMBER 28, 2023

An AI and data platform, such as watsonx, can help empower businesses to leverage foundation models and accelerate the pace of generative AI adoption across their organization. Business-targeted, IBM-developed foundation models built from sound data Business leaders charged with adopting generative AI need model flexibility and choice.

Testing 87

Testing Finance Enterprise Modeling 87

The Unofficial Google Data Science Blog

MARCH 31, 2016

KUEHNEL, and ALI NASIRI AMINI In this post, we give a brief introduction to random effects models, and discuss some of their uses. Through simulation we illustrate issues with model fitting techniques that depend on matrix factorization. Random effects models are a useful tool for both exploratory analyses and prediction problems.

Modeling 40

Modeling Statistics Advertising Testing 40

Occam's Razor

APRIL 8, 2013

Let's listen in as Alistair discusses the lean analytics model… The Lean Analytics Cycle is a simple, four-step process that shows you how to improve a part of your business. Another way to find the metric you want to change is to look at your business model. The business model also tells you what the metric should be.

Metrics 157

Metrics KPI Analytics Key Performance Indicator 157

The Unofficial Google Data Science Blog

APRIL 17, 2017

Selection and aggregation of forecasts from an ensemble of models to produce a final forecast. Quantification of forecast uncertainty via simulation-based prediction intervals. Calendaring was therefore an explicit feature of models within our framework, and we made considerable investment in maintaining detailed regional calendars.

Forecasting 83

Forecasting Modeling Statistics Uncertainty 83

Insight

APRIL 10, 2020

Their latest placement and salary statistics are: 88% of Insight Fellows accept a job offer in their chosen field within 6 months of finishing the Fellows Program, and the median time to accept a job offer is 8 weeks. We are proud of the results above, but are constantly iterating on our model to do even better.

Uncertainty 74

Uncertainty Statistics Machine Learning Publishing 74

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. This post describes the bsts software package, which makes it easy to fit some fairly sophisticated time series models with just a few lines of R code. by STEVEN L. Forecasting (e.g.

Forecasting 66

Forecasting Statistics Modeling Software 66

BI-Survey

MARCH 16, 2021

Markets and competition today are highly dynamic and complex, and the future is characterized by uncertainty – not least because of COVID-19. This uncertainty is currently at the forefront of everyone‘s minds. 75 percent of companies confirm that predictive models provide good forecasts for them, even in volatile markets.

Forecasting 52

Forecasting Management Uncertainty Predictive Modeling 52

BI-Survey

MARCH 16, 2021

Markets and competition today are highly dynamic and complex, and the future is characterized by uncertainty – not least because of COVID-19. This uncertainty is currently at the forefront of everyone‘s minds. 75 percent of companies confirm that predictive models provide good forecasts for them, even in volatile markets.

Forecasting 52

Forecasting Management Uncertainty Predictive Modeling 52

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? 16 (2): 101–133. [3]

Metrics 98

Metrics Statistics Uncertainty Optimization 98

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. When we shipped an algorithm update, when we improved the model, you would see a step function change in that growth.

Management 110

Management Machine Learning Experimentation Metrics 110

IBM Big Data Hub

APRIL 18, 2024

While these large language model (LLM) technologies might seem like it sometimes, it’s important to understand that they are not the thinking machines promised by science fiction. 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

BI-Survey

FEBRUARY 14, 2022

Overnight, the impact of uncertainty, dynamics and complexity on markets could no longer be ignored. Local events in an increasingly interconnected economy and uncertainties such as the climate crisis will continue to create high volatility and even chaos. The COVID-19 pandemic caught most companies unprepared. Think beyond finance.

Forecasting 52

Forecasting Uncertainty Measurement Cost-Benefit 52

Peter James Thomas

AUGUST 12, 2018

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]. The thing with statistical estimates is that they are never a single figure but a range. million ± £0.5

Statistics 49

Statistics Metrics Data Quality Measurement 49

The Unofficial Google Data Science Blog

MARCH 13, 2017

Typically, causal inference in data science is framed in probabilistic terms, where there is statistical uncertainty in the outcomes as well as model uncertainty about the true causal mechanism connecting inputs and outputs. We are all familiar with linear and logistic regression models.

IT 70

IT Visualization Modeling Uncertainty 70

Cloudera

MARCH 2, 2018

Banking, transportation, healthcare, retail, and real estate, all have seen the emergence of new business models fundamentally changing how customers use their services. The model integrates and analyses hundreds of data elements. The model has been shown to be effective in preventing the screening-out of at-risk children.

Data-driven 43

Data-driven Prescriptive Analytics Machine Learning Descriptive Analytics 43

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.

Advertising 43

Advertising Testing Sales Statistics 43

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. This is very much the work of a scientist.

Slice and Dice 46

Slice and Dice Experimentation Data-driven Data Science 46

O'Reilly on Data

DECEMBER 9, 2019

Recall from my previous blog post that all financial models are at the mercy of the Trinity of Errors , namely: errors in model specifications, errors in model parameter estimates, and errors resulting from the failure of a model to adapt to structural changes in its environment.

Statistics 31

Statistics Uncertainty Risk Marketing 31

Timo Elliott

MARCH 9, 2020

Researchers, of course, try to use sophisticated statistical techniques to get around these problems, and have attempted to provide their best estimates for outbreaks around the world. A more flexible way of attacking uncertainty is to look beyond specific models and instead benchmark against “other people like us.”

Uncertainty 126

Uncertainty Statistics Risk Reporting 126

Juice Analytics

APRIL 17, 2020

Forecasts are built by experts, using lots of assumptions, based on very complex and specifically applied statistical models. And forecast modelers know how not only to create those, but also how to interpret them. But when the public and politicians see a forecast, they we talk about them as if they’re a prediction.

Forecasting 101

Forecasting Snapshot Dashboards Uncertainty 101