CIO Business Intelligence

MAY 7, 2024

Data is the cement that paves the AI value road. 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. CIOs and digital executives need to ensure that Big Data is getting the attention it deserves.

Big Data 125

Big Data Statistics Machine Learning Data Science 125

Sisense

SEPTEMBER 25, 2020

A data scientist must be skilled in many arts: math and statistics, computer science, and domain knowledge. No matter your skill, career level, or title, the ability to analyze, organize, and visualize data are vital skills in our world of quickly growing and ever-changing data. Linear regression.

Statistics 98

Statistics Predictive Modeling Modeling Machine Learning 98

The Unofficial Google Data Science Blog

DECEMBER 4, 2019

by THOMAS OLAVSON Thomas leads a team at Google called "Operations Data Science" that helps Google scale its infrastructure capacity optimally. With those stakes and the long forecast horizon, we do not rely on a single statistical model based on historical trends. Our team does a lot of forecasting.

Forecasting 113

Forecasting Data Science Statistics Uncertainty 113

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 54

Data Science Machine Learning Cost-Benefit Deep Learning 54

Domino Data Lab

JULY 22, 2019

Paco Nathan presented, “Data Science, Past & Future” , at Rev. At Rev’s “ Data Science, Past & Future” , Paco Nathan covered contextual insight into some common impactful themes over the decades that also provided a “lens” help data scientists, researchers, and leaders consider the future.

Data Science 109

Data Science Machine Learning Data Governance Modeling 109

Rocket-Powered Data Science

JULY 14, 2023

I recently saw an informal online survey that asked users what types of data (tabular; text; images; or “other”) are being used in their organization’s analytics applications. This was not a scientific or statistically robust survey, so the results are not necessarily reliable, but they are interesting and provocative.

Analytics 166

Analytics Internet of Things Prescriptive Analytics Data Science 166

CIO Business Intelligence

JUNE 7, 2022

It comprises the processes, tools and techniques of data analysis and management, including the collection, organization, and storage of data. The chief aim of data analytics is to apply statistical analysis and technologies on data to find trends and solve problems. It is frequently used for risk analysis.

Data Analytics 129

Data Analytics Diagnostic Analytics Management Analytics 129

DataRobot Blog

DECEMBER 6, 2022

Savvy data scientists are already applying artificial intelligence and machine learning to accelerate the scope and scale of data-driven decisions in strategic organizations. These data science teams are seeing tremendous results—millions of dollars saved, new customers acquired, and new innovations that create a competitive advantage.

Experimentation 145

Experimentation Forecasting Data-driven Machine Learning 145

Rocket-Powered Data Science

JULY 19, 2023

I recently saw an informal online survey that asked users which types of data (tabular, text, images, or “other”) are being used in their organization’s analytics applications. This was not a scientific or statistically robust survey, so the results are not necessarily reliable, but they are interesting and provocative.

Data-driven 188

Data-driven Enterprise Analytics Machine Learning 188

Domino Data Lab

JANUARY 24, 2019

In 2018 we saw the “data science platform” market rapidly crystallize into three distinct product segments. Over the last couple years, it would be hard to blame anyone for being overwhelmed looking at the data science platform market landscape. Proprietary (often GUI-driven) data science platforms.

Data Science 65

Data Science Marketing Data-driven Statistics 65

CIO Business Intelligence

MAY 9, 2024

Companies want candidates who can drive innovation, deliver meaningful business results, and work closely with other leaders to manage risks. And they must develop and upskill talent to ensure the workforce is well-versed in the innovation and risk associated with AI use. The same can be said for AI talent in general, Daly stresses.

Risk Management 134

Risk Management Risk Cost-Benefit Technology 134

Insight

FEBRUARY 19, 2020

Using variability in machine learning predictions as a proxy for risk can help studio executives and producers decide whether or not to green light a film project Photo by Kyle Smith on Unsplash Originally posted on Toward Data Science. and even set their risk tolerance. and even set their risk tolerance.

Risk 67

Risk ROI Modeling Metrics 67

Domino Data Lab

JULY 9, 2019

Chris Wiggins , Chief Data Scientist at The New York Times, presented “Data Science at the New York Times” at Rev. Wiggins also indicated that data science, data engineering, and data analysis are different groups at The New York Times. Data science. Session Summary.

Data Science 112

Data Science Machine Learning Advertising Modeling 112

CIO Business Intelligence

MARCH 14, 2024

Adopting and scaling AI Some of the NAO’s advice to government could also apply to enterprises, especially those with a similar aversion to risk and change. The NAO found that government bodies had flagged up a range of specific AI risks, including legal liability, the risk of inaccurate output, and security.

Testing 103

Testing Risk Digital Transformation Statistics 103

O'Reilly on Data

MAY 18, 2020

This article answers these questions, based on our combined experience as both a lawyer and a data scientist responding to cybersecurity incidents, crafting legal frameworks to manage the risks of AI, and building sophisticated interpretable models to mitigate risk. First and foremost is the tendency for AI to decay over time.

Risk 357

Risk Modeling Data Processing Software 357

IBM Big Data Hub

NOVEMBER 29, 2023

To build that trust and drive broad adoption, vendors of synthetic data generation tools will need to address two critical questions that many business leaders ask: Will synthetic data expose my business to additional data privacy risks? How accurately does synthetic data reflect my existing data?

Metrics 82

Metrics Machine Learning Risk Statistics 82

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

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 267

Enterprise Modeling Testing Reporting 267

datapine

AUGUST 29, 2022

Best for : the new intern who has no idea what data science even means. An excerpt from a rave review : “I would definitely recommend this book to everyone interested in learning about data from scratch and would say it is the finest resource available among all other Big Data Analytics books.”. Davenport.

Big Data 263

Big Data Data Analytics Analytics Data mining 263

O'Reilly on Data

MARCH 24, 2020

Why is high-quality and accessible data foundational? The assumed value of data is a myth leading to inflated valuations of start-ups capturing said data. Generating data with a pre-specified analysis plan and running that analysis is good. Re-analyzing existing data is often very bad.”

Machine Learning 343

Machine Learning Statistics Data Quality Modeling 343

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

DataRobot Blog

SEPTEMBER 1, 2022

Imagine yourself as a pilot operating aircraft through a thunderstorm; you have all the dashboards and automated systems that inform you about any risks. These and many other questions are now on top of the agenda of every data science team. You use this information to make decisions to navigate and land safely.

Metrics 145

Metrics Statistics Modeling Data Science 145

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. A background in (or a firm grasp of) data warehousing and mining. BI Data Scientist.

Business Intelligence 239

Business Intelligence Statistics Visualization Data-driven 239

Smart Data Collective

DECEMBER 21, 2022

The IT and cybersecurity sectors are heavily dependent on people with an expertise in data science. The Bureau of Labor Statistics estimates that there are nearly 106,000 employed data scientists in the United States. You have so many opportunities available to you when you get a degree in data science or a related field.

IT 111

IT Big Data Data Science Data-driven 111

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. There is a risk of injecting bias. There is no clear end state.

Statistics 106

Statistics Unstructured Data Data Science Visualization 106

Smart Data Collective

SEPTEMBER 19, 2022

Statistics show that 93% of customers will offer repeat business when they encounter a positive customer experience. Big data in fintech plays a vital role, providing crucial content that impacts service delivery. Through big data insights, financial institutions can offer personalized services as well as predict consumer behavior.

Big Data 129

Big Data ROI Measurement Risk 129

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 307

Enterprise Deep Learning Data Governance Risk 307

Rita Sallam

APRIL 2, 2023

We explored these questions and more at our Bake-Offs and Show Floor Showdowns at our Data and Analytics Summit in Orlando with 4,000 of our closest D&A friends and family. The first featured analytics and BI platform Gartner Magic Quadrant leaders while the other showcased high interest data science and machine learning platforms.

Optimization 52

Optimization Machine Learning Insurance Risk 52

O'Reilly on Data

MAY 21, 2019

As the number of data scientists and machine learning engineers grow within an organization, tools have to be standardized, models and features need to be shared, and automation starts getting introduced. 58% of survey respondents indicated they are building or evaluating data science platforms. Data results from a Twitter poll.

Machine Learning 234

Machine Learning Technology Deep Learning Data Science 234

IBM Big Data Hub

FEBRUARY 13, 2024

Like diligent students, these generative models soak up information and identify patterns, structures and relationships between data points, which is how they learn the grammar of poetry, artistic brushstrokes and musical melodies. Imagine each data point as a glowing orb placed on a vast, multi-dimensional landscape.

Enterprise 120

Enterprise Modeling Testing Data-driven 120

Smart Data Collective

APRIL 2, 2023

Data engineers who’ve previously worked in the financial or telecommunications sectors may find this to be a rewarding field to get into. Their skills would certainly be valued by managerial staff who need to have ready access to healthcare statistics at all hours. Today’s startup culture has streamlined this considerably.

Broadcasting 95

Broadcasting Marketing Technology Predictive Analytics 95

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. Managing Model Risk.

Modeling 64

Modeling IT Risk Management Deep Learning 64

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. Data, on the other hand, is not prejudiced in its approach, as opposed to human people.

Data Analytics 135

Data Analytics Analytics Big Data Key Performance Indicator 135

Birst BI

NOVEMBER 20, 2018

When you hear about Data Science, Big Data, Analytics, Artificial Intelligence, Machine Learning, or Deep Learning, you may end up feeling a bit confused about what these terms mean. A foundational data analysis tool is Statistics , and everyone intuitively applies it daily. So, what do these terms really mean?

Statistics 84

Statistics Machine Learning Deep Learning Optimization 84

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

CIO Business Intelligence

AUGUST 24, 2023

Gen AI has the potential to magnify existing risks around data privacy laws that govern how sensitive data is collected, used, shared, and stored. We’re getting bombarded with questions and inquiries from clients and potential clients about the risks of AI.” The risk is too high.” AI is a black box.

Uncertainty 97

Uncertainty Risk Testing Enterprise 97

Domino Data Lab

JULY 19, 2021

One of the primary challenges of any ML/AI project is transitioning it from the hands of data scientists in the develop phase of the data science lifecycle into the hands of engineers in the deploy phase. Where in the life cycle does data scientists’ involvement end? Data Engineer. DevOps Engineer. ML Architect.

Enterprise 85

Enterprise Data Science Testing Modeling 85