Rocket-Powered Data Science

JULY 19, 2023

The results showed that (among those surveyed) approximately 90% of enterprise analytics applications are being built on tabular data. 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

Domino Data Lab

FEBRUARY 20, 2019

Eugene Mandel , Head of Product at Superconductive Health , recently dropped by Domino HQ to candidly discuss cross-team collaboration within data science. Eugene Mandel , Head of Product at Superconductive Health , recently dropped by Domino HQ to discuss cross-team collaboration within data science.

Data Science 76

Data Science Testing Software Uncertainty 76

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

Domino Data Lab

MAY 15, 2019

This Domino Data Science Field Note covers Pete Skomoroch ’s recent Strata London talk. Over the years, I have listened to data scientists and machine learning (ML) researchers relay various pain points and challenges that impede their work. Addressing the Uncertainty that ML Adds to Product Roadmaps.

Machine Learning 111

Machine Learning Management Experimentation Uncertainty 111

CIO Business Intelligence

NOVEMBER 14, 2022

A DSS supports the management, operations, and planning levels of an organization in making better decisions by assessing the significance of uncertainties and the tradeoffs involved in making one decision over another. According to Gartner, the goal is to design, model, align, execute, monitor, and tune decision models and processes.

Data mining 134

Data mining Data-driven Statistics OLAP 134

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

Jet Global

NOVEMBER 20, 2023

Unlock insights from ERP Data to Deliver Actionable Insights Let’s face it. With the volatility of the market and increasing uncertainties that arise within your business, you need actionable insights to contend with competitors buoyed by digital transformation efforts. Or at least that is what users of these solutions believed.

Business Intelligence 52

Business Intelligence IT Operational Reporting Reporting 52

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 135

Management Internet of Things Statistics Data-driven 135

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 88

Cost-Benefit Modeling Manufacturing Interactive 88

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. Would your job still be there in a year? Salaries by Gender. Think about it.”

Machine Learning 363

Machine Learning Statistics Reporting Consulting 363

CIO Business Intelligence

APRIL 28, 2022

Right from the start, auxmoney leveraged cloud-enabled analytics for its unique risk models and digital processes to further its mission. However, many financial services companies still prefer to build their own data centers rather than leverage cloud solutions. We see this demonstrated in S-Bank , ranked No.

Analytics 88

Analytics Insurance Big Data Uncertainty 88

Cloudera

AUGUST 20, 2020

It’s been a year filled with disruption and uncertainty. Businesses had to literally switch operations, and enable better collaboration and access to data in an instant — while streamlining processes to accommodate a whole new way of doing things. One day we were all going to the office, and the next we were working from home.

Enterprise 120

Enterprise Experimentation Uncertainty Deep Learning 120

AWS Big Data

AUGUST 7, 2023

Many businesses use different software tools to analyze historical data and past patterns to forecast future demand and trends to make more accurate financial, marketing, and operational decisions. Forecasting acts as a planning tool to help enterprises prepare for the uncertainty that can occur in the future.

Forecasting 80

Forecasting Data Warehouse Machine Learning Modeling 80

CIO Business Intelligence

JANUARY 4, 2023

Here are the shifts we noted: Delivery Models : Moving from an almost strictly on-premises systems approach (with some remote access services), HPC is embracing remote delivery models. Of course, data center challenges are also driving demand for these alternatives.

Cost-Benefit 59

Cost-Benefit Machine Learning Modeling Uncertainty 59

Cloudera

OCTOBER 27, 2020

Our goal is to take the incredible data science and machine learning research developments we see emerging from academia and large industrial labs, and bridge the gap to products and processes that are useful to practitioners working across industries. At Cloudera Fast Forward we work to make the recently possible useful.

Deep Learning 59

Deep Learning Machine Learning Forecasting Uncertainty 59

Sisense

SEPTEMBER 9, 2019

If you lack a data science team, integrating BigSquid with your open-platform BI tool is a powerful way to achieve the horsepower of data science while maintaining the ease of use that the average business user requires. Then we ran Kraken’s machine learning and predictive modeling engine to get the results.

Predictive Analytics 75

Predictive Analytics Predictive Modeling Forecasting Data-driven 75

bridgei2i

JUNE 3, 2020

Consumers feel threatened by the prolonged uncertainty, not having had to deal with anything like it, in their lives. Difficulties in forecasting & planning: Pre-COVID forecasts are no longer valid as the pandemic has entirely disrupted the market and enterprises would need to work on new models to predict KPIs.

Manufacturing 52

Manufacturing Forecasting Uncertainty Digital Transformation 52

The Unofficial Google Data Science Blog

APRIL 23, 2024

Crucially, it takes into account the uncertainty inherent in our experiments. Experiments, Parameters and Models At Youtube, the relationships between system parameters and metrics often seem simple — straight-line models sometimes fit our data well. It is a big picture approach, worthy of your consideration.

Experimentation 52

Experimentation Optimization Uncertainty Metrics 52

CIO Business Intelligence

DECEMBER 20, 2022

In the coming year, having a good read on customer needs will be crucial as many organizations battle resource constraints, challenging economic conditions, and continuing uncertainty when it comes to planning.

Uncertainty 90

Uncertainty Digital Transformation Optimization Enterprise 90

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. We must therefore maintain statistical rigor in quantifying experimental uncertainty. In this post we explore how and why we can be “ data-rich but information-poor ”.

Experimentation 41

Experimentation Statistics Metrics Measurement 41

Jen Stirrup

DECEMBER 14, 2018

I’d hoped for some downtime at this time of year, but I’ve picked up a crucial data science project which needs to be delivered over Christmas so there isn’t much downtime. Markets, and businesses, do not like uncertainty. I was a Data Ambassador for DataKind this year. I am not going to share here.

Consulting 56

Consulting Business Intelligence Data Science Marketing 56

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. We conclude with an example of our forecasting routine applied to publicly available Turkish Electricity data.

Forecasting 83

Forecasting Modeling Statistics Uncertainty 83

The Unofficial Google Data Science Blog

DECEMBER 4, 2019

by THOMAS OLAVSON Thomas leads a team at Google called "Operations Data Science" that helps Google scale its infrastructure capacity optimally. 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.

Forecasting 113

Forecasting Data Science Statistics Uncertainty 113

Domino Data Lab

JUNE 2, 2019

Co-chair Paco Nathan provides highlights of Rev 2 , a data science leaders summit. We held Rev 2 May 23-24 in NYC, as the place where “data science leaders and their teams come to learn from each other.” If you lead a data science team/org, DM me and I’ll send you an invite to data-head.slack.com ”.

Data-driven 110

Data-driven Data Science Machine Learning Modeling 110

The Unofficial Google Data Science Blog

APRIL 19, 2021

by LEE RICHARDSON & TAYLOR POSPISIL Calibrated models make probabilistic predictions that match real world probabilities. Calibration applies in many applications, and hence the practicing data scientist must understand this useful tool. Why calibration matters What are the consequences of miscalibrated models? a sigmoid).

Modeling 122

Modeling IT Metrics Testing 122

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.”.

Management 244

Management Metrics Machine Learning Modeling 244

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

The Unofficial Google Data Science Blog

JULY 22, 2020

For this reason we don’t report uncertainty measures or statistical significance in the results of the simulation. In practice, one may want to use more complex models to make these estimates. For example, one may want to use a model that can pool the epoch estimates with each other via hierarchical modeling (a.k.a.

Experimentation 95

Experimentation Statistics Testing Strategy 95

Cloudera

NOVEMBER 18, 2021

By adopting a custom developed application based on the Cloudera ecosystem, Carrefour has combined the legacy systems into one platform which provides access to customer data in a single data lake. Learn more about the Cloudera Data Impact Awards and see past winners!

Data Lake 73

Data Lake Cost-Benefit Digital Transformation Risk 73

CIO Business Intelligence

JUNE 15, 2022

However, this model created more functional silos, process handoffs, and operational complexity,” Keshavan explains. So, in early 2021, Voya’s IT groups began transitioning back to a decentralized model, aligning value streams and bringing technology shared services work closer together to reduce process delays and complexity.

IT 145

IT Digital Transformation Technology Strategy 145

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

Domino Data Lab

JUNE 23, 2019

It’s harder for many folks in this role who don’t have any data or machine learning background and are thrown or thrust into shipping something like this. They lack probably what a lot of folks in this room have in terms of domain expertise around data, machine learning, and how these models work.

Management 110

Management Machine Learning Experimentation Metrics 110

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

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. Hollywood is a $10 billion-a-year industry, and movies range from huge hits to box office bombs.

Risk 67

Risk ROI Modeling Metrics 67

Sisense

APRIL 13, 2020

If data science is your jam, there are so many exciting new developments in AI, machine learning, natural language processing, and graph databases that you could keep busy reading for days on end. For me, the takeaway is the need to prepare those critical things to face uncertainty in the current market.”.

Uncertainty 83

Uncertainty Marketing Digital Transformation Forecasting 83

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

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. Most organizations should take formal steps to condition and improve their data, such as creating dedicated data quality teams.

Data Quality 304

Data Quality Metadata Data Governance Publishing 304