Cloudera

MARCH 2, 2018

Bridgespan Group estimated in 2015 that only 6% of nonprofits use data to drive improvements in their work. Identify those most at risk or most affected by a problem more accurately by using predictive analytics. The model has been shown to be effective in preventing the screening-out of at-risk children.

Data-driven 42

Data-driven Prescriptive Analytics Descriptive Analytics Machine Learning 42

bridgei2i

MAY 26, 2020

It is even more essential now that supply chains are empowered with a high standard of data and analytics sophistication to be able to cost-effectively serve the company’s purpose and combat risks at the same time. You know, Chief Risk Officers, for example, will no longer be confined to the credit industry. Anushruti: Perfect.

Enterprise 131

Enterprise Management Forecasting Manufacturing 131

Rita Sallam

JULY 22, 2016

These intentions are also reflected in Gartner’s previous webinar poll results in March and in our 2015 market share results, which show a 63% growth in Modern BI market growth versus a 1.7% decline in traditional BI ( See: Market Share Analysis: Business Intelligence and Analytics Software, 2015 ).

Analytics 40

Analytics Business Intelligence Metadata Statistics 40

The Unofficial Google Data Science Blog

JUNE 30, 2016

Identification We now discuss formally the statistical problem of causal inference. We start by describing the problem using standard statistical notation. The field of statistical machine learning provides a solution to this problem, allowing exploration of larger spaces. For a random sample of units, indexed by $i = 1.

Statistics 44

Statistics Optimization Modeling Experimentation 44

Occam's Razor

JANUARY 9, 2012

I've discovered that if we can just get them to imagine a better existence, undertake serious risks, experiment with new better ideas, and spend money executing them… they will ask for more robust measurement! AND you can control for risk! You can literally control for risk should everything blow up in your face.

Marketing 140

Marketing B2B Measurement Risk 140

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. And we can keep repeating this approach, relying on intuition and luck.

Experimentation 52

Experimentation Optimization Uncertainty Metrics 52

Occam's Razor

MARCH 30, 2017

Yes, a silo but so much better than 2015. Take all the math class you can possibly take, including Calc I, Calc II, Calc III, Linear Algebra, Probability, and Statistics. people are at risk worldwide and you need a medical specialists to detect it – specialists who are not available in many parts of the world. 415 million (!)

Marketing Analytics 166

Marketing Analytics Marketing Analytics Deep Learning 166

The Unofficial Google Data Science Blog

JULY 22, 2020

One reason to do ramp-up is to mitigate the risk of never before seen arms. For example, imagine a fantasy football site is considering displaying advanced player statistics. A ramp-up strategy may mitigate the risk of upsetting the site’s loyal users who perhaps have strong preferences for the current statistics that are shown.

Experimentation 95

Experimentation Statistics Testing Strategy 95

Domino Data Lab

JANUARY 6, 2019

What are the projected risks for companies that fall behind for internal training in data science? Joel Grus (2015). Pedro Domingos (2015). How do options such as mentoring programs fit into this picture, both for organizations and for the individuals involved? In business terms, why does this matter ? Think Bayes.

Data Science 56

Data Science Machine Learning Reporting Visualization 56

CIO Business Intelligence

APRIL 24, 2023

Amanda Merola had zero technical background when she came to The Hartford in 2015, despite a natural interest in computers and a proclivity for problem-solving. Internal talent is gold, and we’re making sure our current employees find places to grow and modernize their skill sets.”

IT 107

IT Strategy Software Insurance 107

O'Reilly on Data

DECEMBER 9, 2019

We develop an ordinary least squares (OLS) linear regression model of equity returns using Statsmodels, a Python statistical package, to illustrate these three error types. CI theory was developed around 1937 by Jerzy Neyman, a mathematician and one of the principal architects of modern statistics.

Statistics 27

Statistics Uncertainty Risk Marketing 27

The Unofficial Google Data Science Blog

MAY 31, 2016

Statistical power is traditionally given in terms of a probability function, but often a more intuitive way of describing power is by stating the expected precision of our estimates. This is a quantity that is easily interpretable and summarizes nicely the statistical power of the experiment.

Advertising 42

Advertising Testing Sales Statistics 42

datapine

DECEMBER 28, 2021

1) What Is A Misleading Statistic? 2) Are Statistics Reliable? 3) Misleading Statistics Examples In Real Life. 4) How Can Statistics Be Misleading. 5) How To Avoid & Identify The Misuse Of Statistics? If all this is true, what is the problem with statistics? What Is A Misleading Statistic?

Statistics 363

Statistics Advertising Visualization Data mining 363

Andrew White

JANUARY 9, 2022

This was not statistic and we have not really explored this in any greater detail since. 2015) and What is Wrong with Interoperability (in healthcare)? There is a use case that does warrant starting with a catalog – that is closely related to data privacy risk. I suspect we should. This is flat wrong.

Analytics 64

Analytics Measurement Modeling Data-driven 64

Smart Data Collective

JUNE 26, 2019

Or, dark data could open your company to regulatory risks if you cannot retrieve requested information during an audit. Instead of being overly concerned about the business investment required for that software, think of the risks to your company if you continue to ignore your unclassified data and the blind spots it causes.

ROI 102

ROI Statistics Machine Learning Measurement 102

Domino Data Lab

JULY 9, 2019

You can sleep at night as a data scientician and you know you’re not building a random number generator, but the people from product, they don’t want to know just that you can predict who’s going to be at risk. However, in 2014, 2015 the editors were falling in love with Slack.

Data Science 112

Data Science Machine Learning Advertising Modeling 112

CIO Business Intelligence

MAY 7, 2024

Still, CIOs should not be too quick to consign the technologies and techniques touted during the honeymoon period (circa 2005-2015) of the Big Data Era to the dust bin of history. Big Data” is a critical area that runs the risk of being miscategorized as being either irrelevant — a thing of the past or lacking a worth-the-trouble upside.

Big Data 125

Big Data Statistics Machine Learning Data Science 125

CIO Business Intelligence

JULY 6, 2023

Spreading the news Telecom provider AT&T began trialing RPA in 2015 to decrease the number of repetitive tasks, such as order entry, for its service delivery group. Another benefit is greater risk management. Secure sponsorship.

Cost-Benefit 116

Cost-Benefit Technology Unstructured Data Testing 116

O'Reilly on Data

JUNE 16, 2020

We know, statistically, that doubling down on an 11 is a good (and common) strategy in blackjack. The quality of the decision is based on known information and an informed risk assessment, while chance involves hidden information and the stochasticity of the world. Risk, Probability, Impact, and Decisions.

Uncertainty 275

Uncertainty Testing Risk Reporting 275