CIO Business Intelligence

SEPTEMBER 12, 2022

Prioritize time for experimentation. The team was given time to gather and clean data and experiment with machine learning models,’’ Crowe says. It requires bold bets and a willingness to persevere despite setbacks, criticism, and uncertainty,’’ wrote McKinsey senior partners Laura Furstenthal and Erik Roth in a recent blog post. “I

Experimentation 133

Experimentation Consulting Technology Machine Learning 133

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

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. Imagine a self-driving car piloted by an AGI. It might suggest a restaurant based on preferences and current popularity.

Cost-Benefit 96

Cost-Benefit Modeling Manufacturing Interactive 96

CIO Business Intelligence

APRIL 6, 2022

If anything, the past few years have shown us the levels of uncertainty we are facing. The race to embrace digital technologies to compete and stay relevant in emerging business models is compelling organizations to shift focus. Approaches like design thinking help empathize with real business problems and customer needs.

Experimentation 103

Experimentation Data Lake Uncertainty Enterprise 103

Cloudera

AUGUST 20, 2020

It’s been a year filled with disruption and uncertainty. The company’s advanced AI models can today detect suspicious transactions and rank these transactions with a score so that fraud investigation teams can best prioritise cases that require immediate mitigation — something that’s imperative as business team members work remotely.

Enterprise 123

Enterprise Experimentation Uncertainty Deep Learning 123

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

CIO Business Intelligence

JULY 31, 2023

How has, say, ChatGPT hit your business model?” Just look at the stats:Some 45% of 2,500 executives polled for a May 2023 report from research firm Gartner said the publicity around ChatGPT prompted them to increase their AI investments, 70% said their organization is already exploring gen AI, and 19% are in actual pilot or production mode.

Strategy 98

Strategy Risk Enterprise Risk Management 98

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

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. But it is not routine. So, how do we fix this problem?

Metrics 156

Metrics KPI Analytics Key Performance Indicator 156

The Unofficial Google Data Science Blog

JULY 22, 2020

Instead, we focus on the case where an experimenter has decided to run a full traffic ramp-up experiment and wants to use the data from all of the epochs in the analysis. When there are changing assignment weights and time-based confounders, this complication must be considered either in the analysis or the experimental design.

Experimentation 95

Experimentation Statistics Testing Strategy 95

CIO Business Intelligence

SEPTEMBER 19, 2023

How can enterprises attain these in the face of uncertainty? Rogers: This is one of two fundamental challenges of corporate innovation — managing innovation under high uncertainty and managing innovation far from the core — that I have studied in my work advising companies and try to tackle in my new book The Digital Transformation Roadmap.

Digital Transformation 130

Digital Transformation Uncertainty Testing Enterprise 130

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.”. Proper AI product monitoring is essential to this outcome. I/O validation.

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 competence results in reduced risk and uncertainty. Outputs from trained AI models include numbers (continuous or discrete), categories or classes (e.g.,

Cost-Benefit 293

Cost-Benefit Advertising ROI Machine Learning 293

CIO Business Intelligence

AUGUST 25, 2023

Where an internal capability does not already exist, and the case relies on a large language model (LLM), you will need to determine how you want to proceed: by training and fine-tuning an off-the-shelf model, like Morgan Stanley did with OpenAI; or by building your own, like Bloomberg did. Experiment. That’s the way you want it.

Strategy 98

Strategy Data Strategy Experimentation Uncertainty 98

Domino Data Lab

JUNE 23, 2019

Skomoroch proposes that managing ML projects are challenging for organizations because shipping ML projects requires an experimental culture that fundamentally changes how many companies approach building and shipping software. Yet, this challenge is not insurmountable. for what is and isn’t possible) to address these challenges.

Management 110

Management Machine Learning Experimentation Metrics 110

DataRobot Blog

OCTOBER 4, 2022

This has prompted AI/ML model owners to retrain their legacy models using data from the post-COVID era, while adapting to continually fluctuating market trends and thinking creatively about forecasting. In the last few years, businesses have experienced disruptions and uncertainty on an unprecedented scale.

Forecasting 59

Forecasting Sales Data-driven Modeling 59

The Unofficial Google Data Science Blog

APRIL 16, 2019

by MICHAEL FORTE Large-scale live experimentation is a big part of online product development. This means a small and growing product has to use experimentation differently and very carefully. This blog post is about experimentation in this regime. But these are not usually amenable to A/B experimentation.

Experimentation 68

Experimentation Sales Metrics Measurement 68

The Unofficial Google Data Science Blog

FEBRUARY 29, 2016

Despite a very large number of experimental units, the experiments conducted by LSOS cannot presume statistical significance of all effects they deem practically significant. The result is that experimenters can’t afford to be sloppy about quantifying uncertainty. At Google, we tend to refer to them as slices.

Experimentation 40

Experimentation Metrics Statistics Measurement 40

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

MAY 31, 2016

A geo experiment is an experiment where the experimental units are defined by geographic regions. We might be interested in comparing, for example, different subscription offers, different versions of terms and conditions, or different user interfaces. What does it take to estimate the impact of online exposure on user behavior?

Advertising 42

Advertising Testing Sales Statistics 42

O'Reilly on Data

SEPTEMBER 11, 2019

2 in frequency in proposal topics; a related term, “models,” is No. An ML-related topic, “models,” was No. For example, even though ML and ML-related concepts —a related term, “ML models,” (No. Our analysis of the Strata NY speaker proposals surfaced several notable findings: ML- and AI-related terms predominate.

IoT 20

IoT Big Data Data Warehouse Uncertainty 20

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. the power grid, a streaming music service, the human body, the weather).

Slice and Dice 45

Slice and Dice Experimentation Data-driven Data Science 45

Cloudera

OCTOBER 8, 2020

In fact, it was a painful process to overhaul their entire business model and they lost significant revenues over the course of two years. A disruptive mindset creates an environment that embraces constant experimentation and change. Stability during Uncertainty . Charlene is a titan in the field. trillion a year.

Digital Transformation 106

Digital Transformation Experimentation Unstructured Data Uncertainty 106