Flipboard

MARCH 13, 2023

Presented by BMC Poor data quality costs organizations an average $12.9 Organizations are beginning to recognize that not only does it have a direct impact on revenue over the long term, but poor data quality also increases the complexity of data ecosystems, and directly impacts the … million a year.

Data Quality 101

Data Quality Big Data Machine Learning 101

Flipboard

JUNE 5, 2023

True data quality simplification requires transformation of both code and data, because the two are inextricably linked. Code sprawl and data siloing both imply bad habits that should be the exception, rather than the norm.

Data Quality 80

Data Quality Big Data Data Science Machine Learning 80

Pickl AI

DECEMBER 4, 2023

In the realm of Data Intelligence, the blog demystifies its significance, components, and distinctions from Data Information, Artificial Intelligence, and Data Analysis. Data Intelligence emerges as the indispensable force steering businesses towards informed and strategic decision-making. These insights?

Data Analysis 52

Data Analysis Data Quality Artificial Intelligence Artificial Intelligence 52

Flipboard

JUNE 15, 2023

Data Engineers: We look into Data Engineering, which combines three core practices around Data Management, Software Engineering, and I&O. This focuses …

Data Quality 96

Data Quality Software Engineer Big Data Machine Learning 96

IBM Journey to AI blog

MAY 9, 2024

Understanding data governance in healthcare The need for a strong data governance framework is undeniable in any highly-regulated industry, but the healthcare industry is unique because it collects and processes massive amounts of personal data to make informed decisions about patient care. The consequence?

ETL 185

ETL Data Quality Automation Metadata 185

Marktechpost

MAY 7, 2024

The burgeoning expansion of the data landscape, propelled by the Internet of Things (IoT), presents a pressing challenge: ensuring data quality amidst the deluge of information. However, the quality of that data is paramount, especially given the escalating reliance on Machine Learning (ML) across various industries.

Large Language Models 122

Large Language Models Automation Data Quality ML 122

Marktechpost

NOVEMBER 19, 2023

One possible solution is finding subsets of data inside incredibly huge datasets to address the issues raised. These subsets should contain all the diversity and information in the original dataset but be easier to handle during processing. The quality of the information should be prioritized over gathering enormous volumes of data.

Machine Learning 93

Machine Learning Deep Learning Data Quality AI Researcher 93

Unite.AI

JANUARY 29, 2024

This article explores the implications of this challenge and advocates for a data-centric approach in AI development to effectively combat misinformation. Understanding the Misinformation Challenge in Generative AI The abundance of digital information has transformed how we learn, communicate, and interact.

Generative AI 204

Generative AI Data Quality AI AI 204

IBM Journey to AI blog

DECEMBER 16, 2022

So, instead of wandering the aisles in hopes you’ll stumble across the book, you can walk straight to it and get the information you want much faster. An enterprise data catalog does all that a library inventory system does – namely streamlining data discovery and access across data sources – and a lot more.

Metadata 130

Metadata Data Quality Data Discovery Data Scientist 130

AI News

NOVEMBER 21, 2023

Addressing this gap will require a multi-faceted approach including grappling with issues related to data quality and ensuring that AI systems are built on reliable, unbiased, and representative datasets. Companies have struggled with data quality and data hygiene.

Big Data 236

Big Data Data Quality Generative AI Large Language Models 236

IBM Journey to AI blog

JULY 13, 2023

When we talk about data integrity, we’re referring to the overarching completeness, accuracy, consistency, accessibility, and security of an organization’s data. Together, these factors determine the reliability of the organization’s data. Data quality Data quality is essentially the measure of data integrity.

Data Quality 170

Data Quality Data Integration Metadata Automation 170

Unite.AI

DECEMBER 23, 2023

They also inform companies whether their APM strategy is being executed as originally planned. Maintaining Data Quality: The execution of any APM strategy can only be as good as the source data used to make conclusions about what needs to be done. This can lead to lower production and, as a result, lower revenue.

Data Quality 182

Data Quality Big Data Artificial Intelligence Artificial Intelligence 182

Unite.AI

JANUARY 18, 2024

LVMs are a new category of AI models specifically designed for analyzing and interpreting visual information, such as images and videos, on a large scale, with impressive accuracy. Moreover, LVMs enable insightful analytics by extracting and synthesizing information from diverse visual data sources, including images, videos, and text.

Computer Vision 264

Computer Vision OpenAI Artificial Intelligence Artificial Intelligence 264

Unite.AI

MAY 15, 2024

This isn’t just about having a place to store data; it’s about creating an organized, efficient system capable of managing and processing vast amounts of information as the project’s complexity grows. Teams should be continually updating their databases to reflect the most current information.

Large Language Models 130

Large Language Models Generative AI Data Quality AI 130

Unite.AI

OCTOBER 12, 2023

You're talking about signals, whether it's audio, images or video; understanding how we communicate and what our senses perceive, and how to mathematically represent that information in a way that allows us to leverage that knowledge to create and improve technology. The vehicle for my PhD was the bandwidth extension of narrowband speech.

Machine Learning 278

Machine Learning Deep Learning Conversational AI Data Quality 278

Unite.AI

FEBRUARY 7, 2024

They are huge, complex, and data-hungry. They also need a lot of data to learn from, which can raise data quality, privacy, and ethics issues. In addition, LLMOps provides techniques to improve the data quality, diversity, and relevance and the data ethics, fairness, and accountability of LLMs.

Machine Learning 294

Machine Learning Large Language Models LLM BERT 294

IBM Journey to AI blog

NOVEMBER 29, 2023

Structured synthetic data types are quantitative and includes tabular data, such as numbers or values, while unstructured synthetic data types are qualitative and includes text, images, and video. While a synthetic dataset may achieve high accuracy, it could compromise privacy by including too much of the original data.

Data Scientist 220

Data Scientist Machine Learning AI Modeling Generative AI 220

Unite.AI

APRIL 23, 2024

Aiimi's tech allows companies to find and make sense of their data – bringing together what can be a sprawling mass of data, documents, and digital information – helping teams access information instantly. The more data businesses accumulated and stored in different systems, the worse issues became.

Insight Engine 130

Insight Engine Auto-complete AI Tools AI Modeling 130

Pickl AI

OCTOBER 11, 2023

Data Observability and Data Quality are two key aspects of data management. The focus of this blog is going to be on Data Observability tools and their key framework. The growing landscape of technology has motivated organizations to adopt newer ways to harness the power of data. What is Data Observability?

Data Quality 52

Data Quality Metadata Automation Data Integration 52

Marktechpost

MARCH 1, 2024

Large multimodal models (LMMs) have the potential to revolutionize how machines interact with human languages and visual information, offering more intuitive and natural ways for machines to understand our world. The challenge in multimodal learning involves accurately interpreting and synthesizing information from textual and visual inputs.

Machine Learning 134

Machine Learning LLM Artificial Intelligence Artificial Intelligence 134

IBM Journey to AI blog

NOVEMBER 21, 2023

Apache Kafka transfers data without validating the information in the messages. It does not have any visibility of what kind of data are being sent and received, or what data types it might contain. A schema registry is essentially an agreement of the structure of your data within your Kafka environment.

Data Quality 266

Data Quality Metadata 266

Flipboard

DECEMBER 27, 2023

Presented by SQream The challenges of AI compound as it hurtles forward: demands of data preparation, large data sets and data quality, the time sink of long-running queries, batch processes and more. In this VB Spotlight, William Benton, principal product architect at NVIDIA, and others explain how …

Generative AI 155

Generative AI Data Quality Explainability AI 155

Marktechpost

JANUARY 1, 2024

Researchers from Microsoft have introduced a novel approach to generate diverse, high-quality instruction data from open-source code, thereby improving the effectiveness of instruction tuning and the generalization ability of fine-tuned models.

Large Language Models 128

Large Language Models Data Quality LLM AI Researcher 128

Unite.AI

JANUARY 9, 2024

Building a strong data foundation. Building a robust data foundation is critical, as the underlying data model with proper metadata, data quality, and governance is key to enabling AI to achieve peak efficiencies.

AI 290

AI AI Metadata AI Tools 290

Marktechpost

JANUARY 12, 2024

To address this challenge, a team of researchers from DeepSeek AI has released a study about scaling laws in detail and providing information about the scaling dynamics of large-scale models, especially in the popular open-source 7B and 67B configurations.

Large Language Models 112

Large Language Models LLM AI Modeling Data Quality 112

IBM Journey to AI blog

JULY 25, 2023

In reviewing IBM’s capabilities in ESG Reporting and Data Management, the Verdantix report notes that IBM has strengths in: Data quality control and enhancement, including “easy-to-understand reports to better analyze data quality.

ESG 153

ESG Data Quality Machine Learning Automation 153

Unite.AI

MARCH 4, 2024

Knowledge workers use their specialized skills, expertise, and creativity to generate, process, and communicate information. Knowledge workers are confused regarding AI due to exposure to conflicting and contradictory information and uncertainty about its impact on their professional lives. Why Are Knowledge Workers Confused About AI?

AI 162

AI AI Explainability Algorithm 162

Pickl AI

AUGUST 31, 2023

Introduction The presence of large volumes of data within organisations requires effective sorting and analysing ensuring that decision-making is highly credible. Almost all organisations nowadays make informed decisions by leveraging data and analysing the market effectively. What is Data Profiling in ETL?

ETL 52

ETL Data Quality Data Integration Metadata 52

IBM Journey to AI blog

FEBRUARY 28, 2024

This helps reduce errors to improve data quality and response times to questions, which improves customer and supplier satisfaction. Blockchain Information is an invaluable business asset. For example, the IBM Sterling Transaction Manager automates manual transactions between businesses and suppliers.

Automation 262

Automation Big Data Data Quality Data Analysis 262

Aiiot Talk

APRIL 30, 2024

IoT sensors continuously monitor a machine’s operational variables, providing real-time information regarding its performance. Inaccurate data leads to false inspection results with potentially devastating repercussions. Ethical considerations : Data privacy and algorithm bias are ethical issues of general AI adoption.

Automation 130

Automation Algorithm AI AI 130

IBM Journey to AI blog

APRIL 10, 2023

Everyone would be using the same data set to make informed decisions which may range from goal setting to prioritizing investments in sustainability. Data fabric can help model, integrate and query data sources, build data pipelines, integrate data in near real-time, and run AI-driven applications.

ESG 235

ESG Metadata AI AI 235

Marktechpost

OCTOBER 1, 2023

This is because, whereas the size of the convolutional kernel constrains convolutional neural networks (CNNs) and can only extract local information, self-attention can remove global information from the picture, delivering adequate and meaningful visual characteristics.

Convolutional Neural Networks 134

Convolutional Neural Networks Neural Network Data Quality AI Researcher 134

IBM Journey to AI blog

MAY 8, 2023

Technological risk—data confidentiality The chief technological risk is the matter of data confidentiality. Insurance AI users must be aware that input data quality limitations have insurance implications, potentially reducing actuarial analytic model accuracy. Above all, put in a robust AI governance program.

AI 184

AI AI Algorithm Generative AI 184

Marktechpost

SEPTEMBER 14, 2023

It required the expertise of individuals proficient in various languages, as the research team carefully inspected and assessed data quality across linguistic boundaries. This hands-on approach ensured the dataset met the highest quality standards. The researchers also documented their auditing process thoroughly.

AI Researcher 131

AI Researcher AI Research Natural Language Processing NLP 131

Unite.AI

JULY 31, 2023

The Importance of Quality Data Clean data serves as the foundation for any successful AI application. AI algorithms learn from data; they identify patterns, make decisions, and generate predictions based on the information they're fed. Consequently, the quality of this training data is paramount.

Data Quality 130

Data Quality Algorithm AI AI 130

Unite.AI

AUGUST 25, 2023

Generative AI models can be tuned to enterprises’ standard operating procedures, business processes, workflows, and software documentation and then can respond to user queries with contextualized and relevant information. In other words, you cannot use a generally trained model.

Generative AI 245

Generative AI AI AI ESG 245

IBM Journey to AI blog

AUGUST 23, 2023

So, if we are training a LLM on proprietary data about an enterprise’s customers, we can run into situations where the consumption of that model could be used to leak sensitive information. In-model learning data Many simple AI models have a training phase and then a deployment phase during which training is paused.

Large Language Models 141

Large Language Models Data Discovery LLM AI 141