O'Reilly on Data

SEPTEMBER 15, 2021

There was a lot of uncertainty about stability, particularly at smaller companies: Would the company’s business model continue to be effective? Economic uncertainty caused by the pandemic may be responsible for the declines in compensation. Average salary by tools for statistics or machine learning. What about Kafka? (See

Machine Learning 363

Machine Learning Statistics Reporting Consulting 363

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

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 186

Data-driven Enterprise Analytics Machine Learning 186

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 93

Testing Finance Enterprise Modeling 93

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 102

Digital Transformation Cost-Benefit Informatics Data mining 102

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

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 85

Forecasting Snapshot Dashboards Uncertainty 85

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, 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

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

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 95

Cost-Benefit Modeling Manufacturing Interactive 95

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 98

Big Data Marketing Sales Manufacturing 98

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

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

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

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

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

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 Descriptive Analytics Machine Learning 43

Domino Data Lab

JULY 22, 2019

how “the business executives who are seeing the value of data science and being model-informed, they are the ones who are doubling down on their bets now, and they’re investing a lot more money.” He was saying this doesn’t belong just in statistics. Key highlights from the session include. Transcript. Tukey did this paper.

Data Science 109

Data Science Machine Learning Data Governance Modeling 109

The Unofficial Google Data Science Blog

DECEMBER 4, 2019

Others argue that there will still be a unique role for the data scientist to deal with ambiguous objectives, messy data, and knowing the limits of any given model. This classification is based on the purpose, horizon, update frequency and uncertainty of the forecast.

Forecasting 113

Forecasting Data Science Statistics Uncertainty 113

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 156

Metrics KPI Analytics Key Performance Indicator 156

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 95

Experimentation Statistics Testing Strategy 95

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

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

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

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

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

Metrics 49

Metrics Statistics Data Quality Measurement 49

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 64

Forecasting Statistics Modeling Software 64

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 27

Statistics Uncertainty Risk Marketing 27

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 304

Data Quality Metadata Data Governance Publishing 304

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 Software 52

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 42

Advertising Testing Sales Statistics 42

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

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

Insight

FEBRUARY 19, 2020

My goal here is not to improve upon the current prediction algorithms but rather to describe a model I devised, called ReelRisk , that uses random resampling to generate a range of predictions which can then be used as a risk assessment tool to determine early on whether to fund a movie. The solid line shows the y=x line for comparison.

Risk 67

Risk ROI Modeling Metrics 67

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 68

IT Visualization Modeling Uncertainty 68