CIO Business Intelligence

MARCH 23, 2023

As the world continues to experience economic uncertainty, IT leaders look to tighten budgets, consolidate tools and resources, and generally become more risk-averse when evaluating new investments. Provide decision makers with credible industry research, as well as your own team’s statistics to support your request. to just 2.2%

Scorecard 59

Scorecard Cost-Benefit ROI IT 59

CIO Business Intelligence

JUNE 28, 2022

And to do so, a solid data management strategy is key. 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. Good developers can catch some of these issues through validation.

Management 138

Management Internet of Things Statistics Data-driven 138

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 Follow a value-focused strategy. To address the challenges, the company has leveraged a combination of computer vision, neural networks, NLP, and fuzzy logic.

Digital Transformation 105

Digital Transformation Cost-Benefit Informatics Data mining 105

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. Surveys show a mixed executive outlook, indicating a level of uncertainty about what to expect. IT Leadership, IT Strategy

IT 138

IT Digital Transformation Reporting Finance 138

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 97

Cost-Benefit Modeling Manufacturing Interactive 97

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

APRIL 17, 2017

Quantification of forecast uncertainty via simulation-based prediction intervals. Prediction Intervals A statistical forecasting system should not lack uncertainty quantification. Journal of Official Statistics 6.1 Disaggregation of the time series into subseries and reconciliation of the subseries forecasts. 1990): 3. [3]

Forecasting 83

Forecasting Modeling Statistics Uncertainty 83

The Unofficial Google Data Science Blog

JULY 22, 2020

by ALEXANDER WAKIM Ramp-up and multi-armed bandits (MAB) are common strategies in online controlled experiments (OCE). These strategies involve changing assignment weights during an experiment. The first is a strategy called ramp-up and is advised by many experts in the field [1].

Experimentation 95

Experimentation Statistics Testing Strategy 95

Timo Elliott

JANUARY 18, 2021

Data has always been important for organizations, but now, faced with so much uncertainty, everybody is interested in having more. Our team have focused on “what’s the distinctive capability that Microsoft has and how can we help? Timo: I believe there’s been a massive explosion of interest in data.

IT 71

IT Dashboards Digital Transformation Data-driven 71

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. There are many strategies we can use to estimate this quantity, and we will discuss each option in detail. High Risk 10% 5% 33.3%

Metrics 98

Metrics Statistics Uncertainty Optimization 98

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. Executives bring a different, transcendent , perspective to bear in assessing data quality, particularly with respect to its impact on business operations and strategy.

Data Quality 304

Data Quality Metadata Data Governance Publishing 304

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

The Unofficial Google Data Science Blog

MARCH 31, 2016

We often use statistical models to summarize the variation in our data, and random effects models are well suited for this — they are a form of ANOVA after all. In the context of prediction problems, another benefit is that the models produce an estimate of the uncertainty in their predictions: the predictive posterior distribution.

Modeling 40

Modeling Statistics Advertising Testing 40

CIO Business Intelligence

FEBRUARY 12, 2024

2 Key challenges include a shortage of talent and skills (62%), unclear investment priorities (47%), and the lack of a strategy for responsible AI (42%), BCG found. Such bleak statistics suggest that indecision around how to proceed with genAI is paralyzing organizations and preventing them from developing strategies that will unlock value.

IT 137

IT Consulting Cost-Benefit Uncertainty 137

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

The Unofficial Google Data Science Blog

APRIL 16, 2019

On the other hand, fledgling products often have neither the statistical power to identify the effects of small incremental changes, nor the luxury to contemplate small improvements. This strategy only works if we have the ability to identify EDM users. If so, decision making is further simplified.

Experimentation 68

Experimentation Sales Metrics Measurement 68

Jen Stirrup

NOVEMBER 15, 2022

However, organizations can be supported by a synergistic approach by integrating systems thinking with the data strategy and technical perspective. The business teams are getting a value framework, which explains how the organization boils down the strategy into measures of success. Data strategy in a VUCA environment.

Data Science 145

Data Science Digital Transformation Data-driven Measurement 145

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. Mike had made the common error of equating a bad outcome with a bad decision.

Uncertainty 275

Uncertainty Testing Risk Reporting 275