IBM Journey to AI blog

JUNE 12, 2023

Companies rely heavily on data and analytics to find and retain talent, drive engagement, improve productivity and more across enterprise talent management. However, analytics are only as good as the quality of the data, which must be error-free, trustworthy and transparent. What is data quality? million each year.

Data Quality 187

Data Quality Automation Data Ingestion Data Platform 187

AI News

DECEMBER 9, 2024

The best way to overcome this hurdle is to go back to data basics. Organisations need to build a strong data governance strategy from the ground up, with rigorous controls that enforce data quality and integrity. Define clear business value Cost is on the list of AI barriers, as always.

Explainability 289

Explainability AI AI LLM 289

Unite.AI

DECEMBER 17, 2024

Financial institutions are in fact starting to deploy AI in anti-financial crime (AFC) efforts – to monitor transactions, generate suspicious activity reports, automate fraud detection and more. Human judgment required for holistic view Adoption of AI cant give way to complacency with automated systems.

Automation 246

Automation AI Explainability AI 246

IBM Journey to AI blog

AUGUST 2, 2023

However, analytics are only as good as the quality of the data, which aims to be error-free, trustworthy, and transparent. According to a Gartner report , poor data quality costs organizations an average of USD $12.9 What is data quality? Data quality is critical for data governance.

Data Quality 184

Data Quality Data Scientist Automation Data Platform 184

Flipboard

NOVEMBER 14, 2023

Modern data quality practices leverage advanced technologies, automation, and machine learning to handle diverse data sources, ensure real-time processing, and foster collaboration across stakeholders.

Data Quality 144

Data Quality Machine Learning Automation Big Data 144

IBM Journey to AI blog

JANUARY 5, 2023

Poor data quality is one of the top barriers faced by organizations aspiring to be more data-driven. Ill-timed business decisions and misinformed business processes, missed revenue opportunities, failed business initiatives and complex data systems can all stem from data quality issues.

Data Quality 130

Data Quality Metadata Big Data ETL 130

Unite.AI

NOVEMBER 13, 2024

As multi-cloud environments become more complex, observability must adapt to handle diverse data sources and infrastructures. Over the next few years, we anticipate AI and machine learning playing a key role in advancing observability capabilities, particularly through predictive analytics and automated anomaly detection.

Machine Learning 146

Machine Learning Large Language Models Data Quality Automation 146

Unite.AI

DECEMBER 27, 2024

Its not a choice between better data or better models. The future of AI demands both, but it starts with the data. Why Data Quality Matters More Than Ever According to one survey, 48% of businesses use big data , but a much lower number manage to use it successfully. Why is this the case?

Data Quality 130

Data Quality Data Drift Explainability Big Data 130

Unite.AI

MARCH 10, 2025

In marketing and customer experience, AI-driven capabilities are already enabling hyper-personalized product recommendations, automated tailored communications and dynamic promotions. Data Quality: The Foundational Strength of Business-driven AI The success of AI-powered transformation depends on high-quality, well-structured data.

AI Modeling 131

AI Modeling Generative AI AI Strategy Automation 131

IBM Journey to AI blog

JUNE 22, 2023

Manik, VP and senior partner for IBM Consulting, outlined a massive opportunity to strategically redesign the client’s finance operations and payment processing by leveraging AI, data analytics, metrics and automation. The results can be apparent quickly.

Automation 239

Automation AI AI Data Quality 239

Unite.AI

JULY 31, 2023

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. AI's Role in Improving Data Quality While the problem of data quality may seem daunting, there is hope.

Data Quality 130

Data Quality Algorithm Automation AI 130

Unite.AI

JANUARY 30, 2025

Can you explain the core concept and what motivated you to tackle this specific challenge in AI and data analytics? illumex pioneered Generative Semantic Fabric – a platform that automates the creation of human and machine-readable organizational context and reasoning. Even defining it back then was a tough task.

Automation 146

Automation Metadata Explainability Data Scientist 146

Aiiot Talk

MAY 25, 2023

However, bad data can have the opposite effect—clouding your judgment and leading to missteps and errors. Learn more about the importance of data quality and how to ensure you maintain reliable data quality for your organization. Why Is Ensuring Data Quality Important?

Data Quality 130

Data Quality Data Integration Robotics Artificial Intelligence 130

AI News

SEPTEMBER 27, 2024

BMC Software’s director of solutions marketing, Basil Faruqui, discusses the importance of DataOps, data orchestration, and the role of AI in optimising complex workflow automation for business success. Second, is data quality and accessibility, the quality of the data is critical.

AI Strategy 302

AI Strategy Automation Data Quality Big Data 302

Unite.AI

MARCH 14, 2025

Add in common issues like poor data quality, scalability limits, and integration headaches, and its easy to see why so many GenAI PoCs fail to move forward. Use techniques like LLM-as-a-judge or LLM-as-Juries to automate (semi-automate) evaluation.

Automation 112

Automation Data Quality LLM Generative AI 112

Unite.AI

FEBRUARY 17, 2025

A financial crime investigator who once received large volumes of suspicious activity alerts requiring tedious investigation work manually gathering data across systems in order to weed out false positives and draft Suspicious Activity Reports (SARs) on the others.

Automation 146

Automation Responsible AI Generative AI AI Modeling 146

Unite.AI

OCTOBER 22, 2024

With over 1,775 executives surveyed across 33 countries, the report uncovers how AI, automation, and sustainability are transforming the landscape of quality assurance. This shift marks a pivotal moment in the industry, with AI set to revolutionize various aspects of QE, from test automation to data quality management.

Automation 130

Automation Data Quality AI Automation AI 130

AI News

NOVEMBER 21, 2023

One of its key advantages lies in driving automation, with the prospect of automating up to 40 percent of the average workday—leading to significant productivity gains for businesses. Companies have struggled with data quality and data hygiene. So that’s a key area of focus,” explains O’Sullivan.

Big Data 337

Big Data Data Quality Generative AI Explainability 337

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 133

Large Language Models Automation Data Quality ML 133

IBM Journey to AI blog

FEBRUARY 28, 2024

Here are four smart technologies modernizing strategic sourcing processes today: Automation Business process automation (also considered a type of business process outsourcing ) is pervasive across industries, minimizing manual tasks in accounting, human resources, IT and more. Blockchain Information is an invaluable business asset.

Automation 291

Automation Big Data Data Quality Data Analysis 291

IBM Journey to AI blog

MAY 9, 2023

Over the past decade, deep learning arose from a seismic collision of data availability and sheer compute power, enabling a host of impressive AI capabilities. But we’ve faced a paradoxical challenge: automation is labor intensive. ” These large models have lowered the cost and labor involved in automation.

Data Platform 347

Data Platform Automation AI AI 347

Unite.AI

SEPTEMBER 30, 2024

AI quality assurance (QA) uses artificial intelligence to streamline and automate different parts of the software testing process. Machine learning models analyze historical data to detect high-risk areas, prioritize test cases, and optimize test coverage. Automated QA surpasses manual testing by offering up to 90% accuracy.

Automation 315

Automation AI AI DevOps 315

AI News

SEPTEMBER 24, 2024

Automation can revolutionise how we carry out inspection and maintenance of offshore wind farms, helping to reduce both costs and timelines.” ” Beyond improved efficiency, Beam’s technology elevates the quality of inspection data and facilitates the creation of 3D reconstructions of assets alongside visual data.

Robotics 334

Robotics Big Data AI AI 334

Marktechpost

JULY 11, 2024

This agentic framework automates the creation of diverse and high-quality synthetic data using raw data sources like text documents and code files as seeds. These benchmarks indicate the substantial advancements made possible by AgentInstruct in synthetic data generation.

Data Quality 112

Data Quality Large Language Models AI Modeling Automation 112

Unite.AI

JUNE 17, 2024

Pascal Bornet is a pioneer in Intelligent Automation (IA) and the author of the best-seller book “ Intelligent Automation.” He is regularly ranked as one of the top 10 global experts in Artificial Intelligence and Automation. It's true that the specter of job losses due to AI automation is a real fear for many.

Automation 146

Automation Artificial Intelligence Artificial Intelligence Generative AI 146

Unite.AI

NOVEMBER 20, 2024

With daily advancements in machine learning , natural language processing , and automation, many of these companies identify as “cutting-edge,” but struggle to stand out. As of 2024, there are approximately 70,000 AI companies worldwide, contributing to a global AI market value of nearly $200 billion.

Deep Learning 146

Deep Learning Natural Language Processing Machine Learning AI 146

Unite.AI

AUGUST 28, 2024

Challenges of Using AI in Healthcare Physicians, doctors, nurses, and other healthcare providers face many challenges integrating AI into their workflows, from displacement of human labor to data quality issues. Interoperability Problems and Data Quality Issues Data from different sources can often fail to integrate seamlessly.

Data Quality 315

Data Quality Algorithm AI AI 315

AssemblyAI

OCTOBER 31, 2024

  Instead of optimizing for Word Error Rate (WER), we focused on delivering immediately usable data: properly formatted emails, validated phone numbers, and structured timestamps – the kind of output that lets you build reliable, production-ready applications. This is why we built Universal-2. 

Automation 147

Automation Data Quality Explainability AI 147

Marktechpost

FEBRUARY 8, 2025

Code generation models have made remarkable progress through increased computational power and improved training data quality. These models undergo pre-training and supervised fine-tuning (SFT) using extensive coding data from web sources. State-of-the-art models like Code-Llama, Qwen2.5-Coder, Starting from Qwen2.5-Coder-Instruct-7B,

Automation 53

Automation Large Language Models Data Quality Conversational AI 53

Unite.AI

FEBRUARY 7, 2024

MLOps are practices that automate and simplify ML workflows and deployments. 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. Solutions such as data validation and augmentation enhance data robustness.

Machine Learning 291

Machine Learning Large Language Models LLM BERT 291

Marktechpost

MARCH 6, 2024

To overcome these issues, a team of researchers has presented Gen4Gen, a semi-automated method for creating datasets. The findings have shown that the quality of multi-concept personalized image production can be significantly improved by improving data quality and utilizing efficient prompting tactics.

Automation 132

Automation Artificial Intelligence Artificial Intelligence Data Quality 132

Unite.AI

OCTOBER 30, 2023

When unstructured data surfaces during AI development, the DevOps process plays a crucial role in data cleansing, ultimately enhancing the overall model quality. Improving AI quality: AI system effectiveness hinges on data quality. Poor data can distort AI responses.

DevOps 308

DevOps Software Development Automation Artificial Intelligence 308

AssemblyAI

JANUARY 22, 2025

Automated insight generation The real value comes from automatically turning these insights into action. Plenty of businesses capture data, but using it is a whole different story. Automating routine tasks like call scoring, note-taking, and insight generation reduces manual effort and improves consistency.

Automation 130

Automation AI AI Business Intelligence 130

Marktechpost

JULY 14, 2024

Collecting, monitoring, and maintaining a web data pipeline can be daunting and time-consuming when dealing with large amounts of data. Traditional approaches’ struggles can compromise data quality and availability with pagination, dynamic content, bot detection, and site modifications.

Data Extraction 108

Data Extraction Automation Data Quality AI 108

Unite.AI

FEBRUARY 6, 2025

But it means that companies must overcome the challenges experienced so far in GenAII projects, including: Poor data quality: GenAI ends up only being as good as the data it uses, and many companies still dont trust their data. But GenAI agents can fully automate responses without involving people. Prediction 5.

LLM 112

LLM Automation Data Quality Prompt Engineer 112

Uber AI

FEBRUARY 23, 2023

Data quality is of paramount importance at Uber, powering critical decisions and features. In this blog learn how we automated column-level drift detection in batch datasets at Uber scale, reducing the median time to detect issues in critical datasets by 5X.

Data Drift 92

Data Drift Automation Data Quality AI 92

AWS Machine Learning Blog

JULY 11, 2024

This allows customers to further pre-train selected models using their own proprietary data to tailor model responses to their business context. The quality of the custom model depends on multiple factors including the training data quality and hyperparameters used to customize the model.

Automation 129

Automation Large Language Models Data Quality Machine Learning 129