CIO Business Intelligence

SEPTEMBER 13, 2024

billion by 2030, according to statistics portal Statista, by virtue of the healthcare industry being under increasing attack. For Kevin Torres, trying to modernize patient care while balancing considerable cybersecurity risks at MemorialCare, the integrated nonprofit health system based in Southern California, is a major challenge.

Risk 128

Risk IT IoT Statistics 128

CIO Business Intelligence

AUGUST 21, 2024

“The flashpoint moment is that rather than being based on rules, statistics, and thresholds, now these systems are being imbued with the power of deep learning and deep reinforcement learning brought about by neural networks,” Mattmann says. Adding smarter AI also adds risk, of course. “At We do lose sleep on this,” he says.

Risk 136

Risk Insurance Cost-Benefit Software 136

Depict Data Studio

JULY 13, 2021

— Thank you to Ann Emery, Depict Data Studio, and her Simple Spreadsheets class for inviting us to talk to them about the use of statistics in nonprofit program evaluation! But then we realized that much of the time, statistics just don’t have much of a role in nonprofit work. Why Nonprofits Shouldn’t Use Statistics.

Statistics 98

Statistics Testing Recreation/Entertainment Experimentation 98

Smart Data Collective

DECEMBER 17, 2021

However, how we connect online can be both highly beneficial (such as fast 5G speeds) and expose us to risks that we were unaware of in the first place. How These Statistics Matter To Your Online Security. This exposure can lead to some serious risks for your customer’s information as well as your business.

Risk 124

Risk Statistics Sales Software 124

O'Reilly on Data

MAY 27, 2020

So the state calculates and publishes a “Risk Adjusted Mortality Ratio”—a comparison between the actual number of observed deaths and the number that would be statistically expected, on average, for patients medically similar to those each doctor actually operated on. Mass produced and farm-to-table. Credit scores.

Risk 361

Risk Testing Reporting Measurement 361

Sisense

SEPTEMBER 25, 2020

A data scientist must be skilled in many arts: math and statistics, computer science, and domain knowledge. Statistics and programming go hand in hand. Mastering statistical techniques and knowing how to implement them via a programming language are essential building blocks for advanced analytics. Linear regression.

Statistics 98

Statistics Predictive Modeling Modeling Machine Learning 98

O'Reilly on Data

MARCH 18, 2020

Whether it’s controlling for common risk factors—bias in model development, missing or poorly conditioned data, the tendency of models to degrade in production—or instantiating formal processes to promote data governance, adopters will have their work cut out for them as they work to establish reliable AI production lines. It ranks high (No.

Enterprise 347

Enterprise Deep Learning Data Governance Risk 347

DataRobot Blog

APRIL 29, 2022

This provides a great amount of benefit, but it also exposes institutions to greater risk and consequent exposure to operational losses. The stakes in managing model risk are at an all-time high, but luckily automated machine learning provides an effective way to reduce these risks.

Risk 111

Risk Modeling IT Risk Management 111

Laminar Security

JUNE 8, 2023

This widespread cloud transformation set the stage for great innovation and growth, but it has also significantly increased the associated risks and complexity of data security, especially the protection of sensitive data. If a business operates in the cloud, especially the public cloud, it will be subject to cloud data security risk.

Statistics 82

Statistics Risk Reporting Unstructured Data 82

O'Reilly on Data

JULY 15, 2019

After the 2008 financial crisis, the Federal Reserve issued a new set of guidelines governing models— SR 11-7 : Guidance on Model Risk Management. Note that the emphasis of SR 11-7 is on risk management.). Sources of model risk. Machine learning developers are beginning to look at an even broader set of risk factors.

Machine Learning 52

Machine Learning Management Enterprise Risk Management 52

datapine

JANUARY 24, 2021

You’ll want to be mindful of the level of measurement for your different variables, as this will affect the statistical techniques you will be able to apply in your analysis. There are basically 4 types of scales: *Statistics Level Measurement Table*. 5) Which statistical analysis techniques do you want to apply?

IT 317

IT Statistics KPI Data-driven 317

CIO Business Intelligence

FEBRUARY 9, 2023

Here are six revealing statistics that show how far the IT industry still has to go before it can truly become a level playing field. This is a disheartening statistic that won’t change without considerable work being done at the top. billion dollars each year because of inequitable and often unwelcoming work environments.

Statistics 116

Statistics IT Reporting Cost-Benefit 116

O'Reilly on Data

NOVEMBER 28, 2023

Unexpected outcomes, security, safety, fairness and bias, and privacy are the biggest risks for which adopters are testing. We’re not encouraging skepticism or fear, but companies should start AI products with a clear understanding of the risks, especially those risks that are specific to AI.

Enterprise 339

Enterprise Testing Modeling Reporting 339

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 31

Statistics Uncertainty Risk Marketing 31

O'Reilly on Data

APRIL 19, 2021

When we asked respondents with mature practices what risks they checked for, 71% said “unexpected outcomes or predictions.” A farming application that detects crop disease doesn’t have the same kind of risks as an application that’s approving or denying loans. Risks checked for during development.

Enterprise 364

Enterprise Risk Modeling Manufacturing 364

datapine

MARCH 25, 2022

While some experts try to underline that BA focuses, also, on predictive modeling and advanced statistics to evaluate what will happen in the future, BI is more focused on the present moment of data, making the decision based on current insights. But let’s see in more detail what experts say and how can we connect and differentiate the both.

Business Intelligence 264

Business Intelligence Analytics Statistics Dashboards 264

AWS Big Data

JANUARY 9, 2025

This simplifies data modification processes, which is crucial for ingesting and updating large volumes of market and trade data, quickly iterating on backtesting and reprocessing workflows, and maintaining detailed audit trails for risk and compliance requirements. At petabyte scale, Icebergs advantages become clear.

Metadata 110

Metadata Snapshot Cost-Benefit Optimization 110

O'Reilly on Data

DECEMBER 12, 2019

1] This includes C-suite executives, front-line data scientists, and risk, legal, and compliance personnel. These recommendations are based on our experience, both as a data scientist and as a lawyer, focused on managing the risks of deploying ML. 4] Fairwashing: The Risk of Rationalization , How Can We Fool LIME and SHAP?

Machine Learning 276

Machine Learning Modeling Testing Risk Management 276

CIO Business Intelligence

DECEMBER 9, 2024

Assuming a technology can capture these risks will fail like many knowledge management solutions did in the 90s by trying to achieve the impossible. In 1987, Nobel prize winning economist Robert Solow famously quipped, You can see the computer age everywhere but in the productivity statistics.

Enterprise 116

Enterprise ROI Cost-Benefit Measurement 116

CIO Business Intelligence

DECEMBER 3, 2024

But supporting a technology strategy that attempts to offset skills gaps by supplanting the need for those skills is also changing the fabric of IT careers — and the long-term prospects of those at risk of being automated out of work. In software development today, automated testing is already well established and accelerating.

IT 128

IT Testing Software Data-driven 128

O'Reilly on Data

FEBRUARY 18, 2020

There’s plenty of security risks for business executives, sysadmins, DBAs, developers, etc., True, it might seem difficult to reconcile R’s decline with strong interest in AI and ML, but consider two factors: first, ML and statistics are not the same thing, and, second, R is not, primarily, a developer-oriented language. to be wary of.

Data-driven 363

Data-driven Software Statistics Marketing 363

IBM Big Data Hub

DECEMBER 8, 2023

.” This same sentiment can be true when it comes to a successful risk mitigation plan. The only way for effective risk reduction is for an organization to use a step-by-step risk mitigation strategy to sort and manage risk, ensuring the organization has a business continuity plan in place for unexpected events.

Risk 67

Risk Strategy Risk Management Insurance 67

CIO Business Intelligence

OCTOBER 29, 2024

So much so that it cites the US Bureau of Labor Statistics which forecasts that nearly two million healthcare workers will be needed each year to keep up with domestic demand. This feature, according to the company, assumes importance as the US healthcare industry is currently facing an ongoing talent shortage.

Cost-Benefit 89

Cost-Benefit Statistics Enterprise Forecasting 89

O'Reilly on Data

MARCH 26, 2024

It’s ironic that, in this article, we didn’t reproduce the images from Marcus’ article because we didn’t want to risk violating copyright—a risk that Midjourney apparently ignores and perhaps a risk that even IEEE and the authors took on!) To see this, let’s consider another example, that of MegaFace.

Modeling 317

Modeling Machine Learning Risk Advertising 317

datapine

MARCH 31, 2022

According to the US Bureau of Labor Statistics, demand for qualified business intelligence analysts and managers is expected to soar to 14% by 2026, with the overall need for data professionals to climb to 28% by the same year. The Bureau of Labor Statistics also states that in 2015, the annual median salary for BI analysts was $81,320.

Business Intelligence 239

Business Intelligence Statistics Visualization Data-driven 239

Smart Data Collective

JUNE 4, 2021

Data science needs knowledge from a variety of fields including statistics, mathematics, programming, and transforming data. Mathematics, statistics, and programming are pillars of data science. In data science, use linear algebra for understanding the statistical graphs. It is the building block of statistics.

Data Science 133

Data Science Statistics Deep Learning Machine Learning 133

CIO Business Intelligence

FEBRUARY 10, 2023

Predictive analytics definition Predictive analytics is a category of data analytics aimed at making predictions about future outcomes based on historical data and analytics techniques such as statistical modeling and machine learning. Financial services: Develop credit risk models. from 2022 to 2028. Forecast financial market trends.

Predictive Analytics 145

Predictive Analytics Analytics Predictive Modeling Forecasting 145

DataRobot Blog

DECEMBER 6, 2022

Bureau of Labor Statistics predicts that the employment of data scientists will grow 36 percent by 2031, 1 much faster than the average for all occupations. Taking a Multi-Tiered Approach to Model Risk Management. Bureau of Labor Statistics. Data scientists are in demand: the U.S. See DataRobot in Action. Watch a demo.

Experimentation 145

Experimentation Forecasting Data-driven Machine Learning 145

O'Reilly on Data

JUNE 14, 2024

They are then able to take in prompts and produce outputs based on the statistical weights of the pretrained models of those corpora. And when a question goes beyond the limits of possible citations, the tool will simply reply “I don’t know” rather than risk hallucinating.

Metadata 293

Metadata Publishing Modeling Data-driven 293

O'Reilly on Data

MARCH 31, 2020

All you need to know for now is that machine learning uses statistical techniques to give computer systems the ability to “learn” by being trained on existing data. Those cutting-edge ideas are also attractive, both to managers who don’t understand the risks and to developers who want to try something that’s really challenging.

Management 363

Management Machine Learning Experimentation Metrics 363

CIO Business Intelligence

JUNE 7, 2022

The chief aim of data analytics is to apply statistical analysis and technologies on data to find trends and solve problems. Data analytics draws from a range of disciplines — including computer programming, mathematics, and statistics — to perform analysis on data in an effort to describe, predict, and improve performance.

Data Analytics 135

Data Analytics Diagnostic Analytics Management Analytics 135

O'Reilly on Data

JULY 28, 2020

This isn’t always simple, since it doesn’t just take into account technical risk; it also has to account for social risk and reputational damage. A product needs to balance the investment of resources against the risks of moving forward without a full understanding of the data landscape. arbitrary stemming, stop word removal.).

Marketing 364

Marketing Experimentation Metrics Testing 364

O'Reilly on Data

JUNE 18, 2024

During training, the AI model learns the statistical relationships between the words or images in its training set. Rather, they are a statistical representation of the probability, based on the training data, that one word will follow another, or in an image, that one pixel will be adjacent to another. We need to achieve both goals.

Advertising 363

Advertising Modeling Publishing Data Processing 363

CIO Business Intelligence

FEBRUARY 20, 2025

In addition, they can use statistical methods, algorithms and machine learning to more easily establish correlations and patterns, and thus make predictions about future developments and scenarios. It ensures that all relevant data and information is consolidated, evaluated and presented in a clear and concise form.

Big Data 84

Big Data Measurement Visualization Reporting 84

The Unofficial Google Data Science Blog

DECEMBER 4, 2019

With those stakes and the long forecast horizon, we do not rely on a single statistical model based on historical trends. It provides the occasion for deeper exploration of which inputs that can be influenced and which risks can be proactively managed. The alternative we use is the forecast triangulation framework described above.

Forecasting 115

Forecasting Data Science Statistics Uncertainty 115

CIO Business Intelligence

JUNE 25, 2024

Charles Dickens’ Tale of Two Cities contrasts London’s order and safety with the chaos and risk of Paris. Its performance might, like so many political polls, be within the boundaries of statistical noise — especially as it upped its 2023 investment in R&D to some $30B. And therein lies a cautionary tale for all CIOs.

Risk 118

Risk Risk Management Statistics Management 118

bridgei2i

MAY 27, 2021

A Masters in Quantitative Economics from the Indian Statistical Institute (ISI), Calcutta, Prithvijit founded BRIDGEi2i in May 2011. Pritam Kanti Paul, CTO and Co-Founder of BRIDGEi2i Analytics, is a Gold Medalist in his batch of Masters in Statistics at the Indian Statistical Institute Calcutta.

Statistics 98

Statistics Digital Transformation Data Processing Forecasting 98