CIO Business Intelligence

NOVEMBER 7, 2022

However, the measure of success has been historically at odds with the number of projects said to be overrunning or underperforming, as Panorama has noted that organizations have lowered their standards of success. While we weren’t naïve to the risk of disruption to the business, the extent and magnitude was greater than we anticipated.”

Manufacturing 128

Manufacturing Cost-Benefit Sales Consulting 128

IBM Big Data Hub

AUGUST 9, 2023

In such scenarios, observability becomes crucial to trace requests across different services, measure latency and pinpoint performance bottlenecks. By adopting observability early on, these organizations can build a solid foundation for monitoring and troubleshooting, ensuring smoother growth and minimizing the risk of unexpected issues.

Metrics 61

Metrics Interactive Data-driven Sales 61

Domino Data Lab

JUNE 2, 2019

Trying to dissect a model to divine an interpretation of its results is a good way to throw away much of the crucial information – especially about non-automated inputs and decisions going into our workflows – that will be required to mitigate existential risk. Measure how these decisions vary across your population.

Data Science 110

Data Science Data-driven Machine Learning Modeling 110

AWS Big Data

APRIL 3, 2024

As data is refreshed and updated, changes can happen through upstream processes that put it at risk of not maintaining the intended quality. By analyzing the historical report snapshot, you can identify areas for improvement, implement changes, and measure the effectiveness of those changes.

Data Quality 103

Data Quality Visualization Metadata Metrics 103

The Unofficial Google Data Science Blog

APRIL 17, 2017

First, the system may not be understood, and even if it was understood it may be extremely difficult to measure the relationships that are assumed to govern its behavior. In the first plot, the raw weekly actuals (in red) are adjusted for a level change in September 2011 and an anomalous spike near October 2012.

Forecasting 83

Forecasting Modeling Statistics Uncertainty 83

AWS Big Data

JANUARY 22, 2024

It helped them understand the areas where they could improve their workloads and how to address common issues, with automated solutions, as well as how to measure the success, defining KPIs. Through the lenses of the tool, Acast was able to address better monitoring, cost optimization , performance, and security.

Data-driven 99

Data-driven Advertising Metadata Data Architecture 99

Cloudera

AUGUST 6, 2018

For example, researchers from Berkeley used mobile data to predict poverty and wealth of individuals or microregions in Rwanda at a time when measuring poverty in Africa remains a challenge. In 2012 , only 25 of the region’s 48 countries had conducted at least two surveys over the past decade to track poverty.

Machine Learning 50

Machine Learning Internet of Things Data-driven Cost-Benefit 50

Domino Data Lab

APRIL 3, 2019

Also, while surveying the literature two key drivers stood out: Risk management is the thin-edge-of-the-wedge ?for Network security mushrooms with VPNs, IDS , gateways, various bump-in-the-wire solutions, SIMS tying all the anti-intrusion measures within the perimeter together, and so on. for DG adoption in the enterprise.

Machine Learning 84

Machine Learning Data Governance Metadata Data Science 84

Domino Data Lab

AUGUST 8, 2019

Their approach is to bombard “organoid” mini brains living in vats with potential cancer meds, to measure the meds’ relative effects. The probabilistic nature changes the risks and process required. We face problems—crises—regarding risks involved with data and machine learning in production. Or something.

Data Science 71

Data Science Machine Learning Data Governance Statistics 71

The Unofficial Google Data Science Blog

AUGUST 27, 2019

The measurement may be biased if our samples are generated from a procedure that samples without replacement, such as reservoir sampling , especially if some items have disproportionate weight, i.e., $p(v_i) cdot n$ is large. High Risk 10% 5% 33.3% 5] Ray Chambers, Robert Clark (2012). 4] William Cochran (1977). 7] Neyman, J.

Metrics 98

Metrics Statistics Uncertainty Optimization 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., Taking measurements at parameter settings further from control parameter settings leads to a lower variance estimate of the slope of the line relating the metric to the parameter.

Experimentation 52

Experimentation Optimization Uncertainty Metrics 52

The Unofficial Google Data Science Blog

MAY 31, 2016

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. Let's take a look at larger groups of individuals whose aggregate behavior we can measure. days or weeks).

Advertising 43

Advertising Testing Sales Statistics 43

The Unofficial Google Data Science Blog

JUNE 30, 2016

A naïve comparison of the exposed and unexposed groups would produce an overly optimistic measurement of the effect of the ad, since the exposed group has a higher baseline likelihood of purchasing a pickup truck. 2012): 25-46. This is often referred to as the positivity assumption. Econometrica (1998): 315-331. Hainmueller, Jens.

Statistics 45

Statistics Optimization Modeling Experimentation 45

O'Reilly on Data

APRIL 23, 2024

By the end of 2012, it was up to 82%. There is a great deal of discussion about the risks and benefits of AI, but it is generally focused narrowly on the technical capabilities of AI tools and whether continued advances will eventually put AI beyond human control, leading to possible disaster. The market was maturing. I think not.

Advertising 334

Advertising Cost-Benefit Marketing Risk 334

Occam's Razor

OCTOBER 15, 2012

The cost of failure in the offline world is so high that even when the cost of failure is low (online), they don't want to take the smallest risk. The data was collected in the first part of 2012, between January and May for the Barometer and between January and February for the Enumeration. What you see is for 2012.

Advertising 137

Advertising Marketing Strategy Insurance 137

datapine

MAY 17, 2021

Studies say that more data has been generated in the last two years than in the entire history before and that since 2012 the industry has created around 13 million jobs around the world. The final aim of the data cleaning stage is to avoid the risks of working with misleading data that can damage your business.

Visualization 161

Visualization Data-driven Business Intelligence Metrics 161

The Unofficial Google Data Science Blog

OCTOBER 12, 2017

Of course, exploratory analysis of big unintentional data puts us squarely at risk for these types of mistakes. But this does not mean that the slice will continue to exhibit an extreme value on this measurement in the future. Controlling the Type I error necessarily comes at the expense of increasing the risk of a Type II error.

Experimentation 62

Experimentation Testing Statistics Metrics 62

Insight

DECEMBER 30, 2019

LeNet-5: A classic CNN architecture Since LeNet-5, there have been many different CNN architectures developed over the years, such as AlexNet (2012), GoogLeNet (2014), VGGNet (2014), and ResNet (2015). a circuit) and you measure the output signal, f(t) , given an exceedingly narrow input pulse (approximated as a Dirac delta function).

Visualization 72

Visualization Machine Learning Modeling IT 72

datapine

DECEMBER 28, 2021

These controlling measures are essential and should be part of any experiment or survey – unfortunately, that isn’t always the case. Drinking tea increases diabetes by 50%, and baldness raises the cardiovascular disease risk up to 70%! In 2012, the global mean temperature was measured at 58.2 They sure can.

Statistics 363

Statistics Advertising Visualization Data mining 363

CIO Business Intelligence

JANUARY 16, 2025

Ultimately, the intent, however, is generally at odds with measurably useful outcomes. The mega-vendor era By 2020, the basis of competition for what are now referred to as mega-vendors was interoperability, automation and intra-ecosystem participation and unlocking access to data to drive business capabilities, value and manage risk.

Enterprise 105

Enterprise Digital Transformation Scorecard Interactive 105

CIO Business Intelligence

APRIL 18, 2025

We struggled with our system becoming spaghetti-like due to repeated modifications, making it difficult to implement new ones or market-related measures, says Sugihara. In early 2012, they submitted an RFP to IT vendors, and ultimately chose Amadeus cloud service Altea after evaluating its effectiveness across other airlines.

Digital Transformation 59

Digital Transformation Data Processing IT Marketing 59