O'Reilly on Data

MARCH 31, 2020

All you need to know for now is that machine learning uses statistical techniques to give computer systems the ability to “learn” by being trained on existing data. Machine learning adds uncertainty. Underneath this uncertainty lies further uncertainty in the development process itself.

Management 332

Management Machine Learning Experimentation Metrics 332

CIO Business Intelligence

NOVEMBER 3, 2023

This overall lack of engagement leads to a sense of detachment, increasing the risk that many in your department may quit or completely check-out in their role. Women disproportionately affected by burnout For women, the statistics around burnout are even worse.

IT 133

IT Reporting Uncertainty Cost-Benefit 133

IBM Big Data Hub

SEPTEMBER 28, 2023

This process is designed to help mitigate risks so that model outputs can be deployed responsibly with the assistance of watsonx.data and watsonx.governance (coming soon). For many businesses and organizations, this can introduce uncertainties that slow adoption of generative AI, particularly in highly regulated industries.

Testing 94

Testing Finance Enterprise Modeling 94

CIO Business Intelligence

NOVEMBER 27, 2023

Cybersecurity risks This one is no surprise, given the scary statistics on the growing number of cyberattacks, the rate of successful attacks, and the increasingly high consequences of being breached. They’re wondering how AI technologies, such as ChatGPT and generative AI in general, will increase risks.

IT 137

IT Digital Transformation Reporting Finance 137

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 Finally, our goal is to diminish consumer risk evaluation periods by 80% without compromising the safety of our products.” This allowed us to derive insights more easily.”

Digital Transformation 104

Digital Transformation Cost-Benefit Informatics Data mining 104

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

IBM Big Data Hub

APRIL 18, 2024

LLMs like ChatGPT are trained on massive amounts of text data, allowing them to recognize patterns and statistical relationships within language. The AGI would need to handle uncertainty and make decisions with incomplete information. NLP techniques help them parse the nuances of human language, including grammar, syntax and context.

Cost-Benefit 95

Cost-Benefit Modeling Manufacturing Interactive 95

Anmut

OCTOBER 29, 2020

Although COVID-19 tracking data is highly complex and is subject to many data quality issues, it is still better to release good-enough data to inform decision making, rather than to take the risk of losing more lives without using any data. In the case of the pandemic, the purpose of COVID-19 data is to inform and support better decisions.

Data Governance 52

Data Governance Data Collection Data-driven Statistics 52

Cloudera

MARCH 2, 2018

For example, applying machine learning to wind forecasting is expected to reduce uncertainty in wind energy production by more than 45% and will allow utilities to integrate wind more easily with traditional forms of power supply. Identify those most at risk or most affected by a problem more accurately by using predictive analytics.

Data-driven 43

Data-driven Prescriptive Analytics Descriptive Analytics Machine Learning 43

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 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

APRIL 17, 2017

Quantification of forecast uncertainty via simulation-based prediction intervals. Such a model risks conflating important aspects, notably the growth trend, with other less critical aspects. In other words, there is an asymmetry of risk-reward when there exists the possibility of misspecifying the weights in $X_C$.

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. King was a wise King, but now he was gripped with uncertainty. For example in 20 Risks that Beset Data Programmes. . [7]. And a more competent Chief Risk Officer. .

Cost-Benefit 55

Cost-Benefit Data Warehouse Consulting Data Science 55

Domino Data Lab

JULY 22, 2019

He was saying this doesn’t belong just in statistics. It involved a lot of work with applied math, some depth in statistics and visualization, and also a lot of communication skills. You see these drivers involving risk and cost, but also opportunity. They’re being told they have to embrace uncertainty.

Data Science 109

Data Science Machine Learning Data Governance Modeling 109

The Unofficial Google Data Science Blog

DECEMBER 4, 2019

This classification is based on the purpose, horizon, update frequency and uncertainty of the forecast. With those stakes and the long forecast horizon, we do not rely on a single statistical model based on historical trends. A single model may also not shed light on the uncertainty range we actually face.

Forecasting 113

Forecasting Data Science Statistics Uncertainty 113

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. and even set their risk tolerance. and even set their risk tolerance.

Risk 67

Risk ROI Modeling Metrics 67

Jet Global

MAY 16, 2023

By connecting solutions across the insightsoftware portfolio, organizations can now choose the capabilities they need for effective reporting, controllership, and budgeting and planning, while improving productivity, user experience, and reducing implementation risk. Good things happen when you’re well connected.

Uncertainty 52

Uncertainty Finance Cost-Benefit Reporting 52

The Unofficial Google Data Science Blog

JULY 22, 2020

One reason to do ramp-up is to mitigate the risk of never before seen arms. 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.

Experimentation 95

Experimentation Statistics Testing Strategy 95

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. High Risk 10% 5% 33.3% Statistical Science. Statistics in Biopharmaceutical Research, 2010. [4] How Many Strata?

Metrics 98

Metrics Statistics Uncertainty Optimization 98

O'Reilly on Data

DECEMBER 9, 2019

Because of this trifecta of errors, we need dynamic models that quantify the uncertainty inherent in our financial estimates and predictions. Practitioners in all social sciences, especially financial economics, use confidence intervals to quantify the uncertainty in their estimates and predictions.

Statistics 27

Statistics Uncertainty Risk Marketing 27

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

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. Simulations are the basis for the well-founded analysis and evaluation of alternative actions, opportunities and risks.

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. Simulations are the basis for the well-founded analysis and evaluation of alternative actions, opportunities and risks.

Forecasting 52

Forecasting Management Uncertainty Predictive Modeling 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. In the U.S.,

Advertising 42

Advertising Testing Sales Statistics 42

datapine

DECEMBER 28, 2021

1) What Is A Misleading Statistic? 2) Are Statistics Reliable? 3) Misleading Statistics Examples In Real Life. 4) How Can Statistics Be Misleading. 5) How To Avoid & Identify The Misuse Of Statistics? If all this is true, what is the problem with statistics? What Is A Misleading Statistic?

Statistics 363

Statistics Advertising Visualization Data mining 363

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. I assume a good number of people here have a fair amount of background there.

Management 110

Management Machine Learning Experimentation Metrics 110

CIO Business Intelligence

FEBRUARY 12, 2024

4 Additionally, while 63% have guardrails in place to use AI safely, these organizations worry about its role in misinformation, ethical bias and job loss among other risks, Wavestone found. Even as organizations plan to boost spending on genAI in 2024. The playbook marries organizational readiness with governance and iterative development.

IT 136

IT Consulting Cost-Benefit Uncertainty 136

Jet Global

OCTOBER 18, 2021

Factory shutdowns, shipping bottlenecks, and shortages of raw materials have led to substantial uncertainty for businesses seeking to address the vicissitudes of supply-side availability. Statistical demand forecasting may use complex formulas and algorithms to extrapolate future demand based on past history.

Forecasting 52

Forecasting Recreation/Entertainment Sales Finance 52

O'Reilly on Data

JUNE 16, 2020

We know, statistically, that doubling down on an 11 is a good (and common) strategy in blackjack. But when making a decision under uncertainty about the future, two things dictate the outcome: (1) the quality of the decision and (2) chance. Consider risk not only in terms of likelihood but also in terms of the impact of your decisions.

Uncertainty 275

Uncertainty Testing Risk Reporting 275