Smart Data Collective

MAY 16, 2022

One of the problems is that they don’t manage their data well. How Companies Can Manage their Data Better. The process of managing data can be quite daunting and complicated. Data management is a set of processes and policies that organizations use to collect, store and share data.

Management 127

Management Data mining Data Warehouse Data Collection 127

Domino Data Lab

AUGUST 9, 2021

For a model-driven enterprise, having access to the appropriate tools can mean the difference between operating at a loss with a string of late projects lingering ahead of you or exceeding productivity and profitability forecasts. What Are Modeling Tools? Importance of Modeling Tools. Types of Modeling Tools.

Modeling 111

Modeling Deep Learning Machine Learning Statistics 111

Smart Data Collective

MARCH 8, 2021

Nowadays, managers across industries rely on information systems such as CRMs to improve their business processes. Within the industry, the management of data allows T&L businesses to take productivity, efficiency, and safety to a whole new level. Fuel Management. The domain of logistics is no stranger to innovations either.

Big Data 143

Big Data Management Predictive Analytics Statistics 143

The Unofficial Google Data Science Blog

DECEMBER 14, 2021

Some of that uncertainty is the result of statistical inference, i.e., using a finite sample of observations for estimation. But there are other kinds of uncertainty, at least as important, that are not statistical in nature. Among these, only statistical uncertainty has formal recognition.

Uncertainty 56

Uncertainty Statistics Measurement Cost-Benefit 56

CIO Business Intelligence

SEPTEMBER 7, 2023

That’s because the current generation of AI is already very good at two things needed in supply chain management. Studies have shown that stat forecasting, where statistical techniques are used to extrapolate from historic data, consistently outperforms consensus methods. And machine intelligence does even better than stat forecasting.

Management 137

Management Forecasting Manufacturing Cost-Benefit 137

CIO Business Intelligence

JUNE 28, 2022

And to do so, a solid data management strategy is key. Encompassing data governance, data ops, data warehousing, data engineering, data analytics, data science , and more, data management, when done right, can provide businesses in every industry a competitive edge. Unstructured data is difficult to analyze.

Management 132

Management Internet of Things Statistics Data-driven 132

DataRobot Blog

APRIL 29, 2022

With the big data revolution of recent years, predictive models are being rapidly integrated into more and more business processes. When business decisions are made based on bad models, the consequences can be severe. As machine learning advances globally, we can only expect the focus on model risk to continue to increase.

Risk 111

Risk Modeling IT Risk Management 111

O'Reilly on Data

DECEMBER 12, 2019

Not least is the broadening realization that ML models can fail. And that’s why model debugging, the art and science of understanding and fixing problems in ML models, is so critical to the future of ML. Because all ML models make mistakes, everyone who cares about ML should also care about model debugging. [1]

Machine Learning 187

Machine Learning Modeling Testing Risk Management 187

AWS Big Data

MARCH 11, 2024

When running Apache Flink applications on Amazon Managed Service for Apache Flink , you have the unique benefit of taking advantage of its serverless nature. With Managed Service for Apache Flink, you can add and remove compute with the click of a button. Each KPU also comes with 50 GB of storage attached to the application.

Management 99

Management Snapshot Metrics Cost-Benefit 99

O'Reilly on Data

MARCH 20, 2019

Apply fair and private models, white-hat and forensic model debugging, and common sense to protect machine learning models from malicious actors. Like many others, I’ve known for some time that machine learning models themselves could pose security risks. This is like a denial-of-service (DOS) attack on your model itself.

Modeling 219

Modeling Machine Learning Predictive Modeling Consulting 219

CIO Business Intelligence

FEBRUARY 14, 2023

We know that the Contact Center-as-a-Service (CCaaS) market is growing; an increasing number of companies are choosing this flexible model to support their CX operations, and this will continue through 2023. A hybrid cloud environment allows them to leverage an innovation model that safeguards the stability of their existing operations.

Statistics 89

Statistics Cost-Benefit Enterprise Modeling 89

CIO Business Intelligence

JANUARY 26, 2023

Cloud cost managers are the solution. See Azure Cost Management , Google Cloud Cost Management , and AWS Cloud Financial Management tools for the big three clouds. Once your cloud commitment gets bigger, independent cost management tools start to become attractive.

Management 113

Management Forecasting Dashboards Optimization 113

DataRobot Blog

MAY 26, 2022

Last time , we discussed the steps that a modeler must pay attention to when building out ML models to be utilized within the financial institution. In summary, to ensure that they have built a robust model, modelers must make certain that they have designed the model in a way that is backed by research and industry-adopted practices.

Risk 52

Risk Modeling Metrics Business Objectives 52

O'Reilly on Data

OCTOBER 18, 2022

What if a named entity recognition (NER) system, based on a cutting-edge large language model (LLM), fails for Chinese, Cyrillic, or Arabic text? As AI technologies are adopted more broadly in security and other high-risk applications, we’ll all need to know more about AI audit and risk management. being the only law firm of the bunch.

Risk Management 287

Risk Management Risk Testing Measurement 287

datapine

SEPTEMBER 29, 2022

1) What Is Data Quality Management? However, with all good things comes many challenges and businesses often struggle with managing their information in the correct way. Enters data quality management. What Is Data Quality Management (DQM)? Why Do You Need Data Quality Management? Table of Contents.

Data Quality 241

Data Quality Metrics Data-driven Management 241

Alation

JUNE 2, 2022

In part one of this series, I discussed how data management challenges have evolved and how data governance and security have to play in such challenges, with an eye to cloud migration and drift over time. Data management is not yet a solved problem, but modern data management is leagues ahead of prior approaches.

Modeling 52

Modeling Data Governance Statistics Unstructured Data 52

CIO Business Intelligence

MAY 7, 2024

Rethinking our thoughts on data When confronting a long-lived substantive problem — for example, why aren’t we getting full value from data — that is not immediately life threatening, I suggest conducting a mental model audit. Big Data, Business IT Alignment, Data Management How are we thinking about this problem? Data is data.

Big Data 125

Big Data Statistics Machine Learning Data Science 125

CIO Business Intelligence

OCTOBER 13, 2023

The data architect is responsible for visualizing and designing an organization’s enterprise data management framework. Application data architect: The application data architect designs and implements data models for specific software applications. They often report to data infrastructure and data science leads.

Data Architecture 127

Data Architecture Data Warehouse Statistics Visualization 127

Domino Data Lab

AUGUST 10, 2021

Models are the central output of data science, and they have tremendous power to transform companies, industries, and society. At the center of every machine learning or artificial intelligence application is the ML/AI model that is built with data, algorithms and code. The process of creating models is called modeling.

Modeling 64

Modeling IT Risk Management Deep Learning 64

CIO Business Intelligence

SEPTEMBER 29, 2022

It’s a hybrid work model that Kiwis have embraced and it is here to stay. But the productive benefits of working from home also bring a more complex IT environment to manage. Joe Locandro says Fletcher Building’s management team and its board are briefed monthly on cyber statistics, activities and events.

Management 109

Management Risk Statistics Data-driven 109

CIO Business Intelligence

JULY 5, 2022

Business analytics is the practical application of statistical analysis and technologies on business data to identify and anticipate trends and predict business outcomes. Data analytics is used across disciplines to find trends and solve problems using data mining , data cleansing, data transformation, data modeling, and more.

Business Analytics 126

Business Analytics Prescriptive Analytics Data mining Diagnostic Analytics 126

CIO Business Intelligence

FEBRUARY 13, 2023

Six Sigma is a quality management methodology used to help businesses improve current processes, products, or services by discovering and eliminating defects. The name references the Greek letter sigma, which is a statistical symbol that represents a standard deviation. What is Six Sigma? Six Sigma was trademarked by Motorola in 1993.

Management 76

Management Statistics Manufacturing Measurement 76

CIO Business Intelligence

JANUARY 19, 2024

The US Bureau of Labor Statistics (BLS) forecasts employment of data scientists will grow 35% from 2022 to 2032, with about 17,000 openings projected on average each year. Companies are increasingly eager to hire data professionals who can make sense of the wide array of data the business collects.

Data Science 125

Data Science Machine Learning Predictive Modeling Experimentation 125

CIO Business Intelligence

NOVEMBER 14, 2022

A DSS supports the management, operations, and planning levels of an organization in making better decisions by assessing the significance of uncertainties and the tradeoffs involved in making one decision over another. These systems help managers monitor performance indicators. Model-driven DSS. ERP dashboards.

Data mining 130

Data mining Data-driven Statistics OLAP 130

O'Reilly on Data

FEBRUARY 16, 2023

It might be easy to dismiss these stories as anecdotal at best, fraudulent at worst, but I’ve seen many reports from beta testers who managed to duplicate them. Large language models like ChatGPT and Google’s LaMDA aren’t designed to give correct results. So it’s not surprising that things are wrong.

Testing 208

Testing Statistics Modeling Optimization 208

CIO Business Intelligence

OCTOBER 26, 2023

Through use of data analytics, data visualization, and data modeling techniques and technologies, BI analysts can identify trends that can help other departments, managers, and executives make business decisions to modernize and improve processes in the organization. What does a business intelligence analyst do?

Business Intelligence 125

Business Intelligence Data-driven Statistics Data Warehouse 125

O'Reilly on Data

NOVEMBER 28, 2023

And everyone has opinions about how these language models and art generation programs are going to change the nature of work, usher in the singularity, or perhaps even doom the human race. 16% of respondents working with AI are using open source models. A few have even tried out Bard or Claude, or run LLaMA 1 on their laptop.

Enterprise 267

Enterprise Modeling Testing Reporting 267

DataRobot

JUNE 16, 2021

In several earlier blog posts, we have focused on what we at DataRobot call the AI production gap , which refers to the gap that makes it difficult to transition models from the data science teams who develop them to the IT and DevOps teams who are responsible for deploying and monitoring them in production. The MLOps Management Agent.

Management 52

Management Modeling Statistics Data Science 52

CIO Business Intelligence

JANUARY 26, 2024

Carnegie Mellon University The Machine Learning Department of the School of Computer Science at Carnegie Mellon University was founded in 2006 and grew out of the Center for Automated Learning and Discovery (CALD), itself created in 1997 as an interdisciplinary group of researchers with interests in statistics and machine learning.

Machine Learning 100

Machine Learning Statistics Recreation/Entertainment Data Science 100

Jet Global

APRIL 5, 2021

Enterprises actively use financial modeling to guide their financial planning and strategic decision-making. Financial models offer data-driven, quantitative analysis that tells you where your company stands and where it’s heading. That being said, one model can’t do it all. What Is Financial Modeling? Risk management.

Modeling 98

Modeling Forecasting Metrics Finance 98

DataRobot Blog

DECEMBER 6, 2022

Other organizations are just discovering how to apply AI to accelerate experimentation time frames and find the best models to produce results. 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. Read the blog. Read the blog.

Experimentation 145

Experimentation Forecasting Data-driven Machine Learning 145

Domino Data Lab

JUNE 23, 2019

Pete Skomoroch presented “ Product Management for AI ” at Rev. Pete Skomoroch ’s “ Product Management for AI ”session at Rev provided a “crash course” on what product managers and leaders need to know about shipping machine learning (ML) projects and how to navigate key challenges. Session Summary. It is similar to R&D.

Management 110

Management Machine Learning Experimentation Metrics 110

O'Reilly on Data

MARCH 24, 2020

In a world focused on buzzword-driven models and algorithms, you’d be forgiven for forgetting about the unreasonable importance of data preparation and quality: your models are only as good as the data you feed them. The model and the data specification become more important than the code.

Machine Learning 343

Machine Learning Statistics Data Quality Modeling 343

O'Reilly on Data

MARCH 26, 2024

TL;DR LLMs and other GenAI models can reproduce significant chunks of training data. Researchers are finding more and more ways to extract training data from ChatGPT and other models. And the space is moving quickly: SORA , OpenAI’s text-to-video model, is yet to be released and has already taken the world by storm.

Modeling 271

Modeling Machine Learning Risk Advertising 271

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. This beats projections for almost all other occupations.

Business Intelligence 239

Business Intelligence Statistics Visualization Data-driven 239

IBM Big Data Hub

FEBRUARY 13, 2024

This data is fed into generational models, and there are a few to choose from, each developed to excel at a specific task. Generative adversarial networks (GANs) or variational autoencoders (VAEs) are used for images, videos, 3D models and music. Autoregressive models or large language models (LLMs) are used for text and language.

Enterprise 121

Enterprise Modeling Testing Data-driven 121

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. Models can be designed, for instance, to discover relationships between various behavior factors.

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Predictive Analytics Analytics Predictive Modeling Forecasting 140