IBM Big Data Hub

MARCH 7, 2023

From the moment of birth to discharge, healthcare professionals can collect so much data about an infant’s vitals—for instance, heartbeat frequency or every rise and drop in blood oxygen level. The worldwide statistics on premature births are staggering— the University of Oxford estimates that neonatal sepsis causes 2.5

Risk 52

Risk Technology Informatics Predictive Modeling 52

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.

Predictive Analytics 145

Predictive Analytics Analytics Predictive Modeling Forecasting 145

FineReport

MARCH 24, 2024

Data analysts contribute value to organizations by uncovering trends, patterns, and insights through data gathering, cleaning, and statistical analysis. They identify and interpret trends in complex datasets, optimize statistical results, and maintain databases while devising new data collection processes.

Statistics 52

Statistics Data mining Visualization Reporting 52

datapine

JANUARY 6, 2022

Qualitative data, as it is widely open to interpretation, must be “coded” so as to facilitate the grouping and labeling of data into identifiable themes. Quantitative analysis refers to a set of processes by which numerical data is analyzed. It is the sum of the values divided by the number of values within the data set.

Visualization 246

Visualization Dashboards Cost-Benefit Measurement 246

FineReport

APRIL 15, 2020

Unlike state-run health care systems in countries like South Korea and China, in most countries, each state or area manages that health data differently, creating disparate data sets, which increases the difficulty of statistics and allocate resources. Five Data Collection Forms. Download template.

Visualization 52

Visualization Dashboards Statistics Data Collection 52

Smart Data Collective

MAY 8, 2022

Let’s not forget that big data and AI can also automate about 80% of the physical work required from human beings, 70% of the data processing, and more than 60% of the data collection tasks. From the statistics shown, this means that both AI and big data have the potential to affect how we work in the workplace.

Big Data 106

Big Data Data Analytics Analytics Cost-Benefit 106

IBM Big Data Hub

SEPTEMBER 28, 2023

This process is designed to help mitigate risks so that model outputs can be deployed responsibly with the assistance of watsonx.data and watsonx.governance (coming soon). Building transparency into IBM-developed AI models To date, many available AI models lack information about data provenance, testing and safety or performance parameters.

Testing 95

Testing Finance Enterprise Modeling 95

IBM Big Data Hub

APRIL 22, 2024

The US City of Atlanta , for example, uses IBM data collection, machine learning and AI to monitor public transit tunnel ventilation systems and predict potential failures that could put passengers at risk. This will help advance progress by optimizing resources used.

IoT 86

IoT Internet of Things Cost-Benefit Reporting 86

AWS Big Data

MAY 16, 2023

In this first post of the series, we show you how data collected from smart sensors is used for building automated dashboards using QuickSight to help distribution network engineers manage, maintain and troubleshoot smart sensors and perform advanced analytics to support business decision making.

Dashboards 78

Dashboards Statistics Data Collection Business Intelligence 78

Anmut

OCTOBER 29, 2020

Although COVID-19 tracking data is highly complex and is subject to many data quality issues, it is still better to release good-enough data to inform decision making, rather than to take the risk of losing more lives without using any data. COVID-19 exposes shortcomings in data management.

Data Governance 52

Data Governance Data Collection Data-driven Statistics 52

FineReport

APRIL 15, 2020

Davis Wright Tremaine advised that employers should take the following considerations into account to determine if work from home may be possible: Operational requirements; Security of work data; Technological capabilities and equipment necessary to perform job duties; Productivity; and. And it is convenient for HRs to update statistics.

Visualization 52

Visualization Statistics Dashboards Data Collection 52

Sisense

SEPTEMBER 3, 2020

Data scientists usually build models for data-driven decisions asking challenging questions that only complex calculations can try to answer and creating new solutions where necessary. Programming and statistics are two fundamental technical skills for data analysts, as well as data wrangling and data visualization.

Statistics 97

Statistics Metrics Visualization Finance 97

BizAcuity

JULY 7, 2020

With limited seating and slot machine on the floor, casinos are ensuring that the risk of the spread of the virus is contained. With respect to developments or changes in inbound markets, gaming data, player statistics, economic recovery speed, and more, adjust and reiterate core strategies. This too shall pass.

Revenue Optimization 98

Revenue Optimization Measurement Interactive Marketing 98

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. Conclusion.

Marketing 362

Marketing Experimentation Metrics Testing 362

CIO Business Intelligence

FEBRUARY 13, 2023

The name references the Greek letter sigma, which is a statistical symbol that represents a standard deviation. The process aims to bring data and statistics into the mesh to help objectively identify errors and defects that will impact quality. Six Sigma was trademarked by Motorola in 1993.

Management 88

Management Statistics Manufacturing Measurement 88

CDW Research Hub

NOVEMBER 6, 2020

Every year companies invest resources on data collection, and employees spend countless hours mulling over that data to make informed business decisions like how to market to their target audience or where to expand the business. Will just any data protection solution get the job done? Know your security risks.

Scorecard 40

Scorecard Risk Business Driver Strategy 40

Smart Data Collective

MAY 4, 2023

Big data has been discussed by business leaders since the 1990s. It refers to datasets too large for normal statistical methods. Professionals have found ways to use big data to transform businesses. This helps to protect sensitive data from prying eyes and reduces the risk of data breaches and cyber attacks.

Data mining 79

Data mining Big Data Data-driven Measurement 79

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. After training, the system can make predictions (or deliver other results) based on data it hasn’t seen before. Machine learning adds uncertainty.

Management 332

Management Machine Learning Experimentation Metrics 332

Smart Data Collective

DECEMBER 8, 2020

The driving force behind this trend is mostly down to the rising concern people have over their online privacy and the unregulated data collection that has been going on silently in the background for many years. This is obviously more of an issue for some people than others, depending on where you live in the world.

Big Data 113

Big Data Data-driven Software Statistics 113

IBM Big Data Hub

AUGUST 7, 2023

In an ideal scenario, they would be able to, with relative and consistent accuracy, predict performance of clinical trial sites that are at risk of not meeting their recruitment expectations. Ultimately, enabling real-time monitoring of site activities and enrollment progress could prompt timely mitigation actions ahead of time.

Optimization 61

Optimization Forecasting Data-driven Strategy 61

Smart Data Collective

JUNE 15, 2022

Accurate client data collection and analysis are critical to maximizing all of these activities. When customers visit your website, they generate data points that may be mined for important insights about what works and what doesn’t on your site. Better Understand Customer Demographics.

Advertising 98

Advertising Cost-Benefit ROI Sales 98

IBM Big Data Hub

AUGUST 11, 2023

Machine learning (ML), a subset of artificial intelligence (AI), is an important piece of data-driven innovation. Machine learning engineers take massive datasets and use statistical methods to create algorithms that are trained to find patterns and uncover key insights in data mining projects.

Data Science 59

Data Science Machine Learning Cost-Benefit Deep Learning 59

The Unofficial Google Data Science Blog

OCTOBER 31, 2016

Generally, if the relative amount of data in a slice is the same across your two groups, you can safely make a comparison. Consider practical significance With a large volume of data, it can be tempting to focus solely on statistical significance or to hone in on the details of every bit of data.

Metrics 107

Metrics Measurement Statistics Data Collection 107

The Unofficial Google Data Science Blog

APRIL 17, 2017

They can arise from data collection errors or other unlikely-to-repeat causes such as an outage somewhere on the Internet. If unaccounted for, these data points can have an adverse impact on forecast accuracy by disrupting seasonality, holiday, or trend estimation.

Forecasting 83

Forecasting Modeling Statistics Uncertainty 83

Occam's Razor

MARCH 30, 2017

We are needed today because data collection is hard. Most humans employed by companies were unable to access data – not intelligent enough or trained enough or simply time pressures. Sidebar: If you don’t know these three phrases, please watch my short talk: A Big Data Imperative: Driving Big Action.]. 415 million (!)

Marketing Analytics 166

Marketing Analytics Marketing Analytics Deep Learning 166

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.

Enterprise 311

Enterprise Deep Learning Data Governance Risk 311

Domino Data Lab

JULY 22, 2019

He was saying this doesn’t belong just in statistics. He also really informed a lot of the early thinking about data visualization. It involved a lot of interesting work on something new that was data management. To some extent, academia still struggles a lot with how to stick data science into some sort of discipline.

Data Science 109

Data Science Machine Learning Data Governance Modeling 109

Smart Data Collective

AUGUST 4, 2022

According to statistics, an astonishing 62% of managers are reluctant to talk to their employees about anything, while one in five business leaders feel uncomfortable when it comes to recognizing employees’ achievements. The authors state that data analytics saves managers time and reduces the risk of inadvertent bias.

Data-driven 129

Data-driven Metrics Software Data Collection 129

Domino Data Lab

JANUARY 21, 2021

Producing insights from raw data is a time-consuming process. Predictive modeling efforts rely on dataset profiles , whether consisting of summary statistics or descriptive charts. The Importance of Exploratory Analytics in the Data Science Lifecycle. Exploratory analysis is a critical component of the data science lifecycle.

Statistics 106

Statistics Unstructured Data Data Science Predictive Modeling 106

Smart Data Collective

FEBRUARY 14, 2023

Features of Video Game Data Analytics Since we have touched on such important actors in the game dev field, it will be in the right place to remind us of what they do. After all, analytics is not just looking at statistics and reading player reviews.

Big Data 118

Big Data Data Analytics Analytics Testing 118

erwin

APRIL 21, 2020

One of the biggest lessons we’re learning from the global COVID-19 pandemic is the importance of data, specifically using a data catalog to comply, collaborate and innovate to crisis-proof our businesses. So one of the biggest lessons we’re learning from COVID-19 is the need for data collection, management and governance.

Measurement 97

Measurement Sales Data Governance Metadata 97

erwin

APRIL 21, 2020

One of the biggest lessons we’re learning from the global COVID-19 pandemic is the importance of data, specifically using a data catalog to comply, collaborate and innovate to crisis-proof our businesses. So one of the biggest lessons we’re learning from COVID-19 is the need for data collection, management and governance.

Measurement 96

Measurement Sales Data Governance Metadata 96

DataRobot Blog

DECEMBER 1, 2022

Artificial intelligence (AI) can help improve the response rate on your coupon offers by letting you consider the unique characteristics and wide array of data collected online and offline of each customer and presenting them with the most attractive offers. How Can AI Target the Right Prospects with Sharper Personalization?

Machine Learning 52

Machine Learning Marketing Modeling Statistics 52

Domino Data Lab

MAY 8, 2019

The lens of reductionism and an overemphasis on engineering becomes an Achilles heel for data science work. Instead, consider a “full stack” tracing from the point of data collection all the way out through inference. He talked about the risks of reductionism and Descartes’ questionable legacy. Ergo, less interpretable.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

datapine

AUGUST 29, 2022

The book has three main ideas: The biggest risk your company faces is investing a lot of time and resources into building something that the market doesn’t want. Most people are aware that companies collect our GPS locale, text messages, credit card purchases, social media posts, Google search history, etc.,

Big Data 263

Big Data Data Analytics Analytics Data mining 263

AWS Big Data

JULY 3, 2023

Backtesting is a process used in quantitative finance to evaluate trading strategies using historical data. This helps traders determine the potential profitability of a strategy and identify any risks associated with it, enabling them to optimize it for better performance.

Snapshot 87

Snapshot Data Lake Testing Strategy 87

O'Reilly on Data

OCTOBER 18, 2022

Data can be wrong. When we use AI in security applications, the risks become even more direct. 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. Predictions can be wrong. System designs can be wrong.

Risk Management 291

Risk Management Risk Testing Measurement 291