Jedox

SEPTEMBER 3, 2020

The unprecedented uncertainty forced companies to make critical decisions within compressed time frames. Many pre-crisis business assumptions and planning models became outmoded overnight. Using these drivers as an overlay to stress-test models add robustness to forecasting and can identify exposure and risks to long-term stability.

Uncertainty 81

Uncertainty Forecasting Digital Transformation Risk 81

The Unofficial Google Data Science Blog

DECEMBER 14, 2021

by AMIR NAJMI & MUKUND SUNDARARAJAN Data science is about decision making under uncertainty. Some of that uncertainty is the result of statistical inference, i.e., using a finite sample of observations for estimation. But there are other kinds of uncertainty, at least as important, that are not statistical in nature.

Uncertainty 56

Uncertainty Statistics Measurement Cost-Benefit 56

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. Most experts categorize it as a powerful, but narrow AI model. A key trend is the adoption of multiple models in production.

Cost-Benefit 95

Cost-Benefit Modeling Manufacturing Interactive 95

DataRobot

APRIL 5, 2021

The uncertainty in her reply piqued my interest. Some claim that fully interpretable models are the solution, that we should constrain the complexity of algorithms so that a layperson can understand them. Some participants were advised of potential model shortcomings due to specific missing inputs.

Uncertainty 96

Uncertainty Machine Learning Modeling Reporting 96

DataRobot

JUNE 1, 2021

Before you put a model into production, you may first need to clear compliance hurdles. You should first identify potential compliance risks, with each additional step again tested against risks. Typically, these areas require the most attention: model development, implementation, and use. Knowing When to Trust a Model.

Uncertainty 52

Uncertainty Machine Learning Advertising Risk 52

Cloudera

DECEMBER 11, 2020

Insurance and finance are two industries that rely on measuring risk with historical data models. To facilitate risk modeling in this new normal, agility and flexibility is required. Data Variety. This will only become more important as we move into 2021 and a post-pandemic new normal.

Insurance 95

Insurance Digital Transformation Unstructured Data Machine Learning 95

CIO Business Intelligence

OCTOBER 18, 2023

If anything, 2023 has proved to be a year of reckoning for businesses, and IT leaders in particular, as they attempt to come to grips with the disruptive potential of this technology — just as debates over the best path forward for AI have accelerated and regulatory uncertainty has cast a longer shadow over its outlook in the wake of these events.

Risk 141

Risk Manufacturing Enterprise Technology 141

CIO Business Intelligence

JULY 7, 2023

This module validates your ability to measure, assess, and develop the Service Desk practice capability using the ITIL Maturity Model. You’ll be tested on a situation of your choosing, so the material will be personal to your experience.

Cost-Benefit 98

Cost-Benefit Strategy Management Uncertainty 98

Occam's Razor

APRIL 8, 2013

Sometimes, we escape the clutches of this sub optimal existence and do pick good metrics or engage in simple A/B testing. 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. Testing out a new feature.

Metrics 156

Metrics KPI Analytics Key Performance Indicator 156

The Unofficial Google Data Science Blog

JULY 18, 2023

E ven after we account for disagreement, human ratings may not measure exactly what we want to measure. Researchers and practitioners have been using human-labeled data for many years, trying to understand all sorts of abstract concepts that we could not measure otherwise. That’s the focus of this blog post.

Measurement 52

Measurement Metrics Uncertainty Slice and Dice 52

DataKitchen

SEPTEMBER 21, 2023

The uncertainty of not knowing where data issues will crop up next and the tiresome game of ‘who’s to blame’ when pinpointing the failure. In the context of Data in Place, validating data quality automatically with Business Domain Tests is imperative for ensuring the trustworthiness of your data assets.

Testing 176

Testing Data Quality Predictive Modeling Metrics 176

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. This type of data is often collected through less rigid, measurable means than quantitative data. This is quantitative data.

Statistics 98

Statistics Unstructured Data Data-driven Visualization 98

The Unofficial Google Data Science Blog

APRIL 23, 2024

the weight given to Likes in our video recommendation algorithm) while $Y$ is a vector of outcome measures such as different metrics of user experience (e.g., Crucially, it takes into account the uncertainty inherent in our experiments. Figure 2: Spreading measurements out makes estimates of model (slope of line) more accurate.

Experimentation 52

Experimentation Optimization Uncertainty Metrics 52

Jet Global

SEPTEMBER 10, 2020

Living through periods of rapid upheaval and uncertainty, like the recent pandemic, forces us to adapt quickly to new working practices. One area that often goes overlooked is the value that can be achieved from the application of consolidated KPIs to measure major indicators.

Forecasting 52

Forecasting Finance Uncertainty Sales 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

O'Reilly on Data

OCTOBER 13, 2020

Similarly, in “ Building Machine Learning Powered Applications: Going from Idea to Product ,” Emmanuel Ameisen states: “Indeed, exposing a model to users in production comes with a set of challenges that mirrors the ones that come with debugging a model.”. Debugging AI Products.

Management 244

Management Metrics Machine Learning Modeling 244

O'Reilly on Data

SEPTEMBER 15, 2020

In my book, I introduce the Technical Maturity Model: I define technical maturity as a combination of three factors at a given point of time. Technical sophistication: Sophistication measures a team’s ability to use advanced tools and techniques (e.g., Technical competence results in reduced risk and uncertainty.

Cost-Benefit 293

Cost-Benefit Advertising ROI Machine Learning 293

The Unofficial Google Data Science Blog

APRIL 19, 2021

by LEE RICHARDSON & TAYLOR POSPISIL Calibrated models make probabilistic predictions that match real world probabilities. To explain, let’s borrow a quote from Nate Silver’s The Signal and the Noise : One of the most important tests of a forecast — I would argue that it is the single most important one — is called calibration.

Modeling 122

Modeling IT Metrics Testing 122

CIO Business Intelligence

JUNE 8, 2022

Digital disruption, global pandemic, geopolitical crises, economic uncertainty — volatility has thrown into question time-honored beliefs about how best to lead IT. Thriving amid uncertainty means staying flexible, he argues. . The coming months are a leadership test for CIOs, and it’s a pass/fail grade.”. Keep calm and lead on.

IT 133

IT Cost-Benefit Uncertainty Digital Transformation 133

The Unofficial Google Data Science Blog

JULY 22, 2020

Another reason to use ramp-up is to test if a website's infrastructure can handle deploying a new arm to all of its users. The website wants to make sure they have the infrastructure to handle the feature while testing if engagement increases enough to justify the infrastructure. We offer two examples where this may be the case.

Experimentation 95

Experimentation Statistics Testing Strategy 95

CIO Business Intelligence

APRIL 28, 2024

However, new energy is restricted by weather and climate, which means extreme weather conditions and unpredictable external environments bring an element of uncertainty to new energy sources. It was the solution of choice to achieve an observable, measurable, adjustable, controllable and traceable low-voltage side.

Interactive 94

Interactive Uncertainty Data Collection IoT 94

Domino Data Lab

JUNE 23, 2019

As a result, Skomoroch advocates getting “designers and data scientists, machine learning folks together and using real data and prototyping and testing” as quickly as possible. These measurement-obsessed companies have an advantage when it comes to AI. It is similar to R&D. Transcript. Hi, I’m Peter Skomoroch.

Management 110

Management Machine Learning Experimentation Metrics 110

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

The Unofficial Google Data Science Blog

MAY 31, 2016

Similarly, we could test the effectiveness of a search ad compared to showing only organic search results. It is important that we can measure the effect of these offline conversions as well. Panel studies make it possible to measure user behavior along with the exposure to ads and other online elements. days or weeks).

Advertising 42

Advertising Testing Sales Statistics 42

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. BARC Recommendations.

Forecasting 52

Forecasting Uncertainty Measurement Software 52

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.

Risk 67

Risk ROI Modeling Metrics 67

Data Science and Beyond

JANUARY 7, 2019

Therefore, bootstrapping has been promoted as an easy way of modelling uncertainty to hackers who don’t have much statistical knowledge. Confidence intervals are a common way of quantifying the uncertainty in an estimate of a population parameter. Don’t compare confidence intervals visually.

Statistics 77

Statistics Uncertainty Testing Modeling 77

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

The Unofficial Google Data Science Blog

APRIL 16, 2019

Modern products often don't start with set-in-stone business models because starting and scaling costs are low. Such decisions involve an actual hypothesis test on specific metrics (e.g. The metrics to measure the impact of the change might not yet be established. Wonders of the internet (and VC funding)!

Experimentation 68

Experimentation Sales Metrics Measurement 68

The Unofficial Google Data Science Blog

OCTOBER 21, 2015

The beliefs of this community are always evolving, and the process of thoughtfully generating, testing, refuting and accepting ideas looks a lot like Science. It is important to make clear distinctions among each of these, and to advance the state of knowledge through concerted observation, modeling and experimentation.

Slice and Dice 45

Slice and Dice Experimentation Data-driven Data Science 45

datapine

DECEMBER 28, 2021

With the rise of advanced technology and globalized operations, statistical analyses grant businesses an insight into solving the extreme uncertainties of the market. These controlling measures are essential and should be part of any experiment or survey – unfortunately, that isn’t always the case. degrees Fahrenheit.

Statistics 363

Statistics Advertising Visualization Data mining 363