This site uses cookies to improve your experience. To help us insure we adhere to various privacy regulations, please select your country/region of residence. If you do not select a country, we will assume you are from the United States. Select your Cookie Settings or view our Privacy Policy and Terms of Use.
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Used for the proper function of the website
Used for monitoring website traffic and interactions
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Strictly Necessary: Used for the proper function of the website
Performance/Analytics: Used for monitoring website traffic and interactions
In this contributed article, engineering leader Uma Uppin emphasizes that high-quality data is fundamental to effective AI systems, as poor data quality leads to unreliable and potentially costly model outcomes.
This post is part of an ongoing series about governing the machine learning (ML) lifecycle at scale. This post dives deep into how to set up datagovernance at scale using Amazon DataZone for the data mesh. However, as data volumes and complexity continue to grow, effective datagovernance becomes a critical challenge.
Discover the vital role of datagovernance in the communications, media, and entertainment industry. Learn how robust datagovernance enables personalized experiences, ensures AI transparency, and mitigates compliance risks.
Key Takeaways: Prioritize metadata maturity as the foundation for scalable, impactful datagovernance. Recognize that artificial intelligence is a datagovernance accelerator and a process that must be governed to monitor ethical considerations and risk.
Speaker: Shreya Rajpal, Co-Founder and CEO at Guardrails AI & Travis Addair, Co-Founder and CTO at Predibase
However, productionizing LLMs comes with a unique set of challenges such as model brittleness, total cost of ownership, datagovernance and privacy, and the need for consistent, accurate outputs.
March 04, 2025 — AI-powered integration company Nexla announced a major update to the Nexla Integration Platform, expanding its no-code integration, RAG pipeline engineering, and datagovernance capabilities with the intent to make enterprise-grade GenAI more accessible. SAN MATEO, Calif.,
Relyance AI, a leading AI-powered datagovernance platform that provides complete visibility and control over enterprise-wide data, announced a $32.1 million Series B funding round to scale operations and meet the needs of the exploding use of artificial intelligence in the enterprise.
In 2025, preventing risks from both cyber criminals and AI use will be top mandates for most CIOs. Ransomware in particular continues to vex enterprises, and unstructured data is a vast, largely unprotected asset.
If you want to stay ahead of the curve, networking with top AI minds, exploring cutting-edge innovations, and attending AI conferences is a must. According to Statista, the AI industry is expected to grow at an annual rate of 27.67% , reaching a market size of US$826.70bn by 2030. Lets dive in!
This article explores the critical role of datagovernance in ensuring the accuracy, compliance, and integrity of data throughout AI model development and deployment.
Key Takeaways: Interest in datagovernance is on the rise 71% of organizations report that their organization has a datagovernance program, compared to 60% in 2023. Datagovernance is a top data integrity challenge, cited by 54% of organizations second only to data quality (56%).
Artificial Intelligence is top-of-mind with every C-suite in Retail & Consumer Goods. Companies see the potential to deliver better customer service, derive faster.
Moving generative AI applications from the proof of concept stage into production requires control, reliability and datagovernance. Organizations are turning to open.
Artificial Intelligence (AI) stands at the forefront of transforming datagovernance strategies, offering innovative solutions that enhance data integrity and security. In this post, let’s understand the growing role of AI in datagovernance, making it more dynamic, efficient, and secure.
The emergence of artificial intelligence (AI) brings datagovernance into sharp focus because grounding large language models (LLMs) with secure, trusted data is the only way to ensure accurate responses. So, what exactly is AIdatagovernance?
Artificial intelligence (AI) is revolutionizing how organizations use data, and these big changes are providing capabilities for improved decision-making and predictive insights. However, as AI becomes more integrated into business and daily life, it also introduces legal complexities that require careful oversight.
Artificial Intelligence (AI) is all the rage, and rightly so. By now most of us have experienced how Gen AI and the LLMs (large language models) that fuel it are primed to transform the way we create, research, collaborate, engage, and much more. Can AIs responses be trusted? Can it do it without bias?
In this contributed article, Fredrik Forslund, Vice President and General Manager of International Sales for Blancco Technology Group, emphasizes the importance of adhering to cybersecurity and datagovernance best practices prior to an M&A to prevent a data breach and ensure the protection of an organization’s customer and corporate data.
That is where DeepSeek comes in as a significant change in the AI industry. By pioneering innovative approaches to model architecture, training methods, and hardware optimization, the company has made high-performance AI models accessible to a much broader audience. stock market (about $1,800 per person in the US).
Terms like “datagovernance,” “Generative AI” and “large language models” are becoming commonplace in the workplace. But for business leaders, it takes more.
This is my monthly check-in to share with you the people and ideas I encounter as a data evangelist with DATAVERSITY. This month, we’re talking about the interplay between DataGovernance and artificial intelligence (AI). Read last month’s column here.)
Last Updated on November 4, 2024 by Editorial Team Author(s): David Sweenor Originally published on Towards AI. Sweenor As artificial intelligence (AI) becomes ubiquitous, it’s reshaping decision-making in ways that go far beyond the scope of traditional business automation. What makes AIgovernance different from datagovernance?
However, the increasing complexity of the data landscape is making it a huge challenge to provide users and applications with fast access required, while ensuring regulatory compliance.
The breach, which involved an outsider gaining access to internal messaging systems, left many worried that a national adversary could do the same and potentially weaponize generative AI technologies. National security aside, the […] The post The DataGovernance Wake-Up Call From the OpenAI Breach appeared first on DATAVERSITY.
the data intelligence company, launched its AIGovernance solution to help organizations realize value from their data and AI initiatives. The solution ensures that AI models are developed using secure, compliant, and well-documented data. Alation Inc.,
Artificial intelligence (AI) is rapidly transforming the world, and non-profit organizations are no exception. AI can be used to improve efficiency, effectiveness, and reach in a variety of ways, from automating tasks to providing personalized services.
In this contributed article, Anita Schjøll Abildgaard, CEO and Co-Founder of Iris.ai, believes that while legislators work towards governance that enables appropriate, effective oversight without stifling innovation, organizations working on AI technology have a responsibility for ethical development.
As I’ve been working to challenge the status quo on DataGovernance – I get a lot of questions about how it will “really” work. In 2019, I wrote the book “Disrupting DataGovernance” because I firmly believe […] The post Dear Laura: How Will AI Impact DataGovernance?
The AI revolution is causing a huge problem for people who want their lives to be private. Datagovernance is going to be one of the most crucial things in the future as we work towards more adoption of artificial intelligence and machine learning. The AI Revolution. AI is facilitating a lot of new developments.
Author(s): Vita Haas Originally published on Towards AI. As we venture deeper, a fascinating paradox emerges: while AI capabilities surge forward at breakneck speed, our regulatory frameworks struggle to keep pace. Image by Me and AI, My Partner in Crime The Regulatory Catch-22 “Exponential change is coming. It is inevitable.
The sudden advent of large language model (LLM) AI tools, such as ChatGPT, Duet AI for Google Cloud, and Microsoft 365 Copilot, is opening new frontiers in AI-generated content and solutions.
Introduction Artificial intelligence (AI) is rapidly becoming a fundamental part of our daily lives, from self-driving cars to virtual personal assistants. However, as AI technology advances, it is crucial to consider the ethical implications of its development and use. The use of AI […].
Key Takeaways: Data integrity is essential for AI success and reliability – helping you prevent harmful biases and inaccuracies in AI models. Robust datagovernance for AI ensures data privacy, compliance, and ethical AI use. Let’s explore some of the biggest takeaways.
The healthcare industry faces arguably the highest stakes when it comes to datagovernance. For starters, healthcare organizations constantly encounter vast (and ever-increasing) amounts of highly regulated personal data. healthcare, managing the accuracy, quality and integrity of data is the focus of datagovernance.
Key Takeaways: Data integrity is required for AI initiatives, better decision-making, and more – but data trust is on the decline. Data quality and datagovernance are the top data integrity challenges, and priorities. AI drives the demand for data integrity.
In our last blog , we delved into the seven most prevalent data challenges that can be addressed with effective datagovernance. Today we will share our approach to developing a datagovernance program to drive data transformation and fuel a data-driven culture.
In a world where data is a crucial asset for training AI models, we've seen firsthand at AssemblyAI how properly managing this vital resource is essential in making progress toward our goal of democratizing state-of-the-art Speech AI. That's where our AI Lakehouse comes in.
The post DataGovernance at the Edge of the Cloud appeared first on DATAVERSITY. With that, I’ve long believed that for most large cloud platform providers offering managed services, such as document editing and storage, email services and calendar […].
We organize all of the trending information in your field so you don't have to. Join 17,000+ users and stay up to date on the latest articles your peers are reading.
You know about us, now we want to get to know you!
Let's personalize your content
Let's get even more personalized
We recognize your account from another site in our network, please click 'Send Email' below to continue with verifying your account and setting a password.
Let's personalize your content