Unite.AI

MARCH 17, 2025

This type of siloed thinking leads to data redundancy and slower data-retrieval speeds, so companies need to prioritize cross-functional communications and collaboration from the beginning. Here are four best practices to help future-proof your data strategy: 1.

AI Strategy 202

AI Strategy Data Quality AI AI 202

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 145

Data Quality Machine Learning Automation Big Data 145

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

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.

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Data Quality Data Scientist Automation Data Platform 184

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 147

Machine Learning Large Language Models Data Quality Automation 147

Unite.AI

JANUARY 22, 2025

MLOps is a set of practices designed to streamline the machine learning (ML) lifecyclehelping data scientists, IT teams, business stakeholders, and domain experts collaborate to build, deploy, and manage ML models consistently and reliably. This approach reduces hallucinations and offers a cost-effective way to leverage enterprise data.

Prompt Engineer 203

Prompt Engineer Prompt Engineering Large Language Models LLM 203

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

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 Automation AI 130

Unite.AI

JANUARY 30, 2025

The vision for illumex emerged during my studies, where I imagined information being accessible through mindmap-like associations rather than traditional databases – enabling direct access to relevant data without extensive human consultation. Even defining it back then was a tough task.

Automation 147

Automation Metadata Explainability Data Scientist 147

AI News

SEPTEMBER 17, 2024

Privacy follows closely behind, with 64% of IT professionals urging for more robust rules to protect sensitive information. The survey uncovers a troubling lack of trust in data quality—a cornerstone of successful AI implementation. Check out AI & Big Data Expo taking place in Amsterdam, California, and London.

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Data Quality Big Data AI AI 319

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.

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Big Data Data Quality Generative AI Explainability 338

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

Unite.AI

APRIL 8, 2025

With unstructured data growing over 50% annually, our ingestion engine transforms scattered information into structured, actionable knowledge. The process is designed for security and privacy, keeping sensitive enterprise data protected while making it immediately useful. One powerful example is our collaboration with the U.S.

Chatbots 147

Chatbots LLM Data Quality Generative AI 147

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

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. Some providers might also disregard ethics and use patient data without permission.

Data Quality 317

Data Quality Algorithm AI AI 317

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. Keep your stakeholders and leadership informed on progress.

Automation 113

Automation Data Quality LLM Generative AI 113

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

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

AssemblyAI

JANUARY 22, 2025

The key is accuracy where it matters most—capturing the details that drive business value like product names, customer information, and competitive intelligence. Automated insight generation The real value comes from automatically turning these insights into action. Accurate transcription is just the beginning, though.

Automation 130

Automation AI AI Business Intelligence 130

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. Trying to Get Media Coverage?

Deep Learning 147

Deep Learning Natural Language Processing Machine Learning AI 147

Unite.AI

FEBRUARY 21, 2025

Akeneo is the product experience (PX) company and global leader in Product Information Management (PIM). How is AI transforming product information management (PIM) beyond just centralizing data? What role does AI play in ensuring product data accuracy and consistency across multiple channels?

Natural Language Processing 147

Natural Language Processing NLP Categorization Algorithm 147

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

AWS Machine Learning Blog

MARCH 18, 2025

In the rapidly evolving healthcare landscape, patients often find themselves navigating a maze of complex medical information, seeking answers to their questions and concerns. However, accessing accurate and comprehensible information can be a daunting task, leading to confusion and frustration.

LLM 102

LLM NLP Data Integration AI 102

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 147

Automation Responsible AI Generative AI AI Modeling 147

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 317

Automation AI AI DevOps 317

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 213

ETL Data Quality Automation Metadata 213

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 2.

LLM 113

LLM Automation Data Quality Prompt Engineer 113

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 147

Automation Artificial Intelligence Artificial Intelligence Generative AI 147

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 310

DevOps Software Development Automation Artificial Intelligence 310

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 294

Machine Learning Large Language Models LLM BERT 294

Unite.AI

DECEMBER 6, 2024

When framed in the context of the Intelligent Economy RAG flows are enabling access to information in ways that facilitate the human experience, saving time by automating and filtering data and information output that would otherwise require significant manual effort and time to be created.

AI 130

AI AI Data Platform LLM 130

IBM Journey to AI blog

SEPTEMBER 5, 2024

Everything is data—digital messages, emails, customer information, contracts, presentations, sensor data—virtually anything humans interact with can be converted into data, analyzed for insights or transformed into a product. Automation can significantly improve efficiency and reduce errors.

Metadata 188

Metadata Data Quality Auto-classification DevOps 188

IBM Journey to AI blog

APRIL 10, 2023

A well-designed data architecture should support business intelligence and analysis, automation, and AI—all of which can help organizations to quickly seize market opportunities, build customer value, drive major efficiencies, and respond to risks such as supply chain disruptions.

ESG 259

ESG Metadata AI AI 259

Flipboard

NOVEMBER 22, 2024

It serves as the hub for defining and enforcing data governance policies, data cataloging, data lineage tracking, and managing data access controls across the organization. Data lake account (producer) – There can be one or more data lake accounts within the organization.

ML 132

ML Data Science Metadata DevOps 132

IBM Journey to AI blog

OCTOBER 21, 2024

Fragmented data stacks, combined with the promise of generative AI, amplify productivity pressure and expose gaps in enterprise readiness for this emerging technology. While turning data into meaningful intelligence is crucial, users such as analysts and data scientists are increasingly overwhelmed by vast quantities of information.

Data Quality 218

Data Quality Data Scientist Big Data Artificial Intelligence 218

Unite.AI

JANUARY 22, 2025

Saket Saurabh , CEO and Co-Founder of Nexla, is an entrepreneur with a deep passion for data and infrastructure. He is leading the development of a next-generation, automated data engineering platform designed to bring scale and velocity to those working with data. Its the same with people.

Auto-complete 147

Auto-complete Automation Machine Learning Data Integration 147

Towards AI

FEBRUARY 17, 2025

RAFT vs Fine-Tuning Image created by author As the use of large language models (LLMs) grows within businesses, to automate tasks, analyse data, and engage with customers; adapting these models to specific needs (e.g., Data Quality Problem: Biased or outdated training data affects the output. balance, outliers).

Data Drift 95

Data Drift LLM Automation Metadata 95