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

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. 6] Debugging may focus on a variety of failure modes (i.e., Sensitivity analysis.

Machine Learning 276

Machine Learning Modeling Testing Risk Management 276

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 106

Metadata Snapshot Cost-Benefit Optimization 106

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

O'Reilly on Data

JULY 15, 2019

In recent posts, we described requisite foundational technologies needed to sustain machine learning practices within organizations, and specialized tools for model development, model governance, and model operations/testing/monitoring. Note that the emphasis of SR 11-7 is on risk management.). Sources of model risk.

Machine Learning 52

Machine Learning Management Enterprise Risk Management 52

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

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

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

MAY 21, 2019

A catalog or a database that lists models, including when they were tested, trained, and deployed. The technologies I’ve alluded to above—data governance, data lineage, model governance—are all going to be useful for helping manage these risks. There are real, not just theoretical, risks and considerations.

Machine Learning 279

Machine Learning Technology Deep Learning Data Science 279

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 Data-driven Modeling 293

O'Reilly on Data

MARCH 23, 2023

What is it, how does it work, what can it do, and what are the risks of using it? It’s by far the most convincing example of a conversation with a machine; it has certainly passed the Turing test. And it can look up an author and make statistical observations about their interests. But it is an amazing analytic engine.”

IT 346

IT Modeling Testing Risk 346

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. joined Flickr.

Modeling 317

Modeling Machine Learning Risk Advertising 317

datapine

JANUARY 6, 2022

More often than not, it involves the use of statistical modeling such as standard deviation, mean and median. Let’s quickly review the most common statistical terms: Mean: a mean represents a numerical average for a set of responses. Standard deviation: this is another statistical term commonly appearing in quantitative analysis.

Visualization 246

Visualization Dashboards Cost-Benefit Measurement 246

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

Smarten

FEBRUARY 19, 2025

Predictive analytics encompasses techniques like data mining, machine learning (ML) and predictive modeling techniques like time series forecasting, classification, association, correlation, clustering, hypothesis testing and descriptive statistics to analyze current and historical data and predict future events, results and business direction.

Predictive Analytics 64

Predictive Analytics Analytics Predictive Modeling Forecasting 64

datapine

NOVEMBER 27, 2019

This is one of the major trends chosen by Gartner in their 2020 Strategic Technology Trends report , combining AI with autonomous things and hyperautomation, and concentrating on the level of security in which AI risks of developing vulnerable points of attacks. Industries harness predictive analytics in different ways.

Business Intelligence 361

Business Intelligence Analytics Prescriptive Analytics Data Quality 361

DataKitchen

OCTOBER 5, 2022

Bad data reaches the customer because companies haven’t invested enough, or at all, in testing, automation, and monitoring. A survey of data engineers conducted by DataKitchen in 2022 revealed some shocking statistics. ” When the fix is fully tested and deployed to the production pipeline, Jason has time to reflect.

Testing 169

Testing Risk Data Processing Statistics 169

DataKitchen

MARCH 12, 2024

We kept adding tests over time; it has been several years since we’ve had any major glitches. Our vision was to create a flexible, state-of-the-art data infrastructure that would allow our analysts to transform the data rapidly with a very low risk of error. Data errors can cause compliance risks. That was amazing for the team.”

Metrics 117

Metrics Software Cost-Benefit Testing 117

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. If a database already exists, the available data must be tested and corrected. Subsequently, the reporting should be set up properly.

Big Data 84

Big Data Measurement Visualization Machine Learning 84

DataKitchen

NOVEMBER 18, 2022

As he thinks through the various journeys that data take in his company, Jason sees that his dashboard idea would require extracting or testing for events along the way. Data and tool tests. DataOps Observability must also store run data over time for root cause diagnosis and statistical process control analysis.

Testing 130

Testing Statistics Measurement Dashboards 130

Smart Data Collective

OCTOBER 8, 2021

Modern machine learning and back-testing; how quant hedge funds use it. Similarly, hedge funds often use modern machine learning and back-testing to analyze their quant models. Here, the models get tested using historical data to evaluate their profitability. And their risks before the organizations invest real money.

Machine Learning 132

Machine Learning Testing Strategy Modeling 132

CIO Business Intelligence

APRIL 12, 2024

Large banking firms are quietly testing AI tools under code names such as as Socrates that could one day make the need to hire thousands of college graduates at these firms obsolete, according to the report. But that’s just the tip of the iceberg for a future of AI organizational disruptions that remain to be seen, according to the firm.

Experimentation 143

Experimentation Reporting Testing Statistics 143

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. This has serious implications for software testing, versioning, deployment, and other core development processes. Machine learning adds uncertainty.

Management 363

Management Machine Learning Experimentation Metrics 363

Wayne Yaddow

MARCH 5, 2025

In this post, well see the fundamental procedures, tools, and techniques that data engineers, data scientists, and QA/testing teams use to ensure high-quality data as soon as its deployed. First, we look at how unit and integration tests uncover transformation errors at an early stage. Key Tools & Processes Testing frameworks (e.g.,

Testing 52

Testing Data Transformation Statistics Metadata 52

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. The CIO so-what test Given Apple’s status as company with the world’s second-highest market capitalization and second-highest overall profitability it’s hard to be too critical. And therein lies a cautionary tale for all CIOs.

Risk 118

Risk Risk Management Statistics Management 118

Domino Data Lab

JULY 28, 2021

Through a marriage of traditional statistics with fast-paced, code-first computer science doctrine and business acumen, data science teams can solve problems with more accuracy and precision than ever before, especially when combined with soft skills in creativity and communication. Math and Statistics Expertise.

Data Science 110

Data Science Statistics Predictive Analytics Recreation/Entertainment 110

AWS Big Data

NOVEMBER 17, 2023

Starting today, the Athena SQL engine uses a cost-based optimizer (CBO), a new feature that uses table and column statistics stored in the AWS Glue Data Catalog as part of the table’s metadata. By using these statistics, CBO improves query run plans and boosts the performance of queries run in Athena.

Optimization 124

Optimization Statistics Metadata Data Lake 124

Smart Data Collective

OCTOBER 8, 2023

It is an interdisciplinary field, combining computer science, statistics , mathematics, and business intelligence. Data Analysis The cleaned data is then analyzed using various statistical techniques and algorithms. This could involve identifying patterns and trends, testing hypotheses, or making predictions.

Data Analytics 125

Data Analytics Analytics Statistics Data Collection 125

Wayne Yaddow

FEBRUARY 11, 2025

Managing tests of complex data transformations when automated data testing tools lack important features? While numerous commercial and open-source tools facilitate standard data quality checks, they often fall short when addressing advanced or specialized testing requirements. High domain specificity: Many advanced checks (e.g.,

Testing 52

Testing Data Transformation Data Quality Statistics 52

DataKitchen

JULY 3, 2023

This innovative approach merges the agility of Agile Development, the stability of DevOps, and the meticulousness of Statistical Process Controls, orchestrating a dynamic, enriched, and nimble data ecosystem that is truly remarkable. They need help creating data tests and observing the entire Data Journey for success.

Testing 130

Testing Risk Data-driven Statistics 130

Smart Data Collective

SEPTEMBER 19, 2022

Statistics show that 93% of customers will offer repeat business when they encounter a positive customer experience. They can also anticipate industry trends, assess risks, and make strategic steps to elevate the customer experience. Improving Risk Assessment. Better UI/UX based on A/B testing. Improving Security.

Big Data 145

Big Data ROI Measurement Machine Learning 145

Smart Data Collective

AUGUST 10, 2021

As a result of the resolution of risks and the creation of hypotheses, data analysis assists businesses in generating sound business choices. The most significant benefit of statistical analysis is that it is completely impartial. Statistics allows an organisation to make choices based on the data that are available to them.

Data Analytics 137

Data Analytics Analytics Big Data Key Performance Indicator 137

Sisense

JANUARY 21, 2020

This is an example of Simpon’s paradox , a statistical phenomenon in which a trend that is present when data is put into groups reverses or disappears when the data is combined. It’s time to introduce a new statistical term. A new drug promising to reduce the risk of heart attack was tested with two groups.

Testing 104

Testing Data-driven Risk Statistics 104

Smart Data Collective

NOVEMBER 9, 2022

One of the biggest benefits is testing processes for optimal effectiveness. The main purpose of machine learning is to partially or completely replace manual testing. One example is using machine learning tools like Selenium to test web development processes. There are a number of great applications of machine learning.

Machine Learning 122

Machine Learning Testing Cost-Benefit Data-driven 122

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. For example, we may prefer one model to generate a range, but use a second scenario-based model to “stress test” the range. A single model may also not shed light on the uncertainty range we actually face.

Forecasting 115

Forecasting Data Science Statistics Uncertainty 115

CIO Business Intelligence

AUGUST 9, 2024

Synthetic data can be generated to reflect the same statistical characteristics as real data, but without revealing personally identifiable information, thereby complying with privacy-by – design regulations and other sensitive details. An example is Alpha Fold, widely used in structural biology and bioinformatics,” he says.

Risk 138

Risk Measurement Data Lake Data Collection 138

DataRobot Blog

MAY 26, 2022

To start with, SR 11-7 lays out the criticality of model validation in an effective model risk management practice: Model validation is the set of processes and activities intended to verify that models are performing as expected, in line with their design objectives and business uses.

Risk 52

Risk Modeling Metrics Business Objectives 52