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
The DeepSeek R1 has arrived, and it’s not just another AI modelit’s a significant leap in AI capabilities, trained upon the previously released DeepSeek-V3-Base variant. With the full-fledged release of DeepSeek R1, it now stands on par with OpenAI o1 in both performance and flexibility. What makes it even more compelling is its open weight […] The post DeepSeek R1 vs OpenAI o1: Which One is Faster, Cheaper and Smarter?
TL;DR: Enterprise AI teams are discovering that purely agentic approaches (dynamically chaining LLM calls) dont deliver the reliability needed for production systems. The prompt-and-pray modelwhere business logic lives entirely in promptscreates systems that are unreliable, inefficient, and impossible to maintain at scale. A shift toward structured automation, which separates conversational ability from business logic execution, is needed for enterprise-grade reliability.
Announcing DataOps Data Quality TestGen 3.0: Open-Source, Generative Data Quality Software. Now With Actionable, Automatic, Data Quality Dashboards Imagine a tool that can point at any dataset, learn from your data, screen for typical data quality issues, and then automatically generate and perform powerful tests, analyzing and scoring your data to pinpoint issues before they snowball.
CIOs are an ambitious lot. Not the type to be satisfied with the status quo, they have set big goals for themselves in the upcoming year, according to countless surveys of IT execs. They want to expand their use of artificial intelligence, deliver more value from those AI investments, further boost employee productivity, drive more efficiencies, improve resiliency, expand their transformation efforts, and more.
Speaker: Ben Epstein, Stealth Founder & CTO | Tony Karrer, Founder & CTO, Aggregage
When tasked with building a fundamentally new product line with deeper insights than previously achievable for a high-value client, Ben Epstein and his team faced a significant challenge: how to harness LLMs to produce consistent, high-accuracy outputs at scale. In this new session, Ben will share how he and his team engineered a system (based on proven software engineering approaches) that employs reproducible test variations (via temperature 0 and fixed seeds), and enables non-LLM evaluation m
I recently completed the latest edition of our Business Planning Buyers Guide, which reviews and assesses the offerings of 14 providers of this software. One of the points that I look at is whether and to what extent the software provider offers out-of-the-box external data useful for forecasting, planning, analysis and evaluation. What I discovered is that the availability of this type of vital information is exceedingly slim.
This post is co-written by Dr. Leonard Heilig and Meliena Zlotos from EUROGATE. For container terminal operators, data-driven decision-making and efficient data sharing are vital to optimizing operations and boosting supply chain efficiency. Internally, making data accessible and fostering cross-departmental processing through advanced analytics and data science enhances information use and decision-making, leading to better resource allocation, reduced bottlenecks, and improved operational perf
In todays dynamic digital landscape, multi-cloud strategies have become vital for organizations aiming to leverage the best of both cloud and on-premises environments. As enterprises navigate complex data-driven transformations, hybrid and multi-cloud models offer unmatched flexibility and resilience. Heres a deep dive into why and how enterprises master multi-cloud deployments to enhance their data and AI initiatives.
In todays dynamic digital landscape, multi-cloud strategies have become vital for organizations aiming to leverage the best of both cloud and on-premises environments. As enterprises navigate complex data-driven transformations, hybrid and multi-cloud models offer unmatched flexibility and resilience. Heres a deep dive into why and how enterprises master multi-cloud deployments to enhance their data and AI initiatives.
The term "architecture" is more commonly used in the realm of data engineering and data warehouse project work, but the concept applies to BI and analytic reporting projects of all sizes. Like the architecture of a building, a complete Business Intelligence architecture contains the foundation and structure of your solution. Using the building analogy, a data platform can take on many forms, like a single-story cottage, a sprawling university campus or a towering skyscraper.
Introduction In the real world, obtaining high-quality annotated data remains a challenge. Generative AI (GenAI) models, such as GPT-4, offer a promising solution, potentially reducing the dependency on labor-intensive annotation. At Graphwise, we aim to make knowledge graph construction faster and more cost-effective. Therefore we explored how GenAI could automate several stages of the graph-building pipeline.
As data and analytics leaders, youre tasked with navigating the fast-evolving world of AI while ensuring your teams are equipped to thrive in this new era. Building organizational AI literacy is no longer optional its essential for staying competitive and unlocking innovation (such as Johnson & Johnsons Vision team did when they built working GenAI and LLM prototypes with Dataiku in less than two days at a hackathon).
The DHS compliance audit clock is ticking on Zero Trust. Government agencies can no longer ignore or delay their Zero Trust initiatives. During this virtual panel discussion—featuring Kelly Fuller Gordon, Founder and CEO of RisX, Chris Wild, Zero Trust subject matter expert at Zermount, Inc., and Principal of Cybersecurity Practice at Eliassen Group, Trey Gannon—you’ll gain a detailed understanding of the Federal Zero Trust mandate, its requirements, milestones, and deadlines.
In the era of AI, chatbots have revolutionized how we interact with technology. Perhaps one of the most impactful uses is in the healthcare industry. Chatbots are able to deliver fast, accurate information, and help individuals more effectively manage their health. In this article, we’ll learn how to develop a medical chatbot using Gemini 2.0, […] The post Building a Medical Chatbot with Gemini 2.0, Flask and Vector Embedding appeared first on Analytics Vidhya.
Theres a lot of chatter in the media that software developers will soon lose their jobs to AI. I dont buy it. It is not the end of programming. It is the end of programming as we know it today. That is not new. The first programmers connected physical circuits to perform each calculation. They were succeeded by programmers writing machine instructions as binary code to be input one bit at a time by flipping switches on the front of a computer.
Unlocking Data Team Success: Are You Process-Centric or Data-Centric? Over the years of working with data analytics teams in large and small companies, we have been fortunate enough to observe hundreds of companies. We want to share our observations about data teams, how they work and think, and their challenges. We’ve identified two distinct types of data teams: process-centric and data-centric.
Generative AI playtime may be over, as organizations cut down on experimentation and pivot toward achieving business value, with a focus on fewer, more targeted use cases. Nearly nine out of 10 senior decision-makers said they have gen AI pilot fatigue and are shifting their investments to projects that will improve business performance, according to a recent survey from NTT DATA.
Savvy B2B marketers know that a great account-based marketing (ABM) strategy leads to higher ROI and sustainable growth. In this guide, we’ll cover: What makes for a successful ABM strategy? What are the key elements and capabilities of ABM that can make a real difference? How is AI changing workflows and driving functionality? This Martech Intelligence Report on Enterprise Account-Based Marketing examines the state of ABM in 2024 and what to consider when implementing ABM software.
Despite all the interest in artificial intelligence (AI) and generative AI (GenAI), ISGs Buyers Guide for Data Platforms serves as a reminder of the ongoing importance of product experience functionality to address adaptability, manageability, reliability and usability. While new and emerging capabilities might catch the eye, features that address data platform security, performance and availability remain some of the most significant deal-breakers when enterprises are considering potential data
In our previous post Backtesting index rebalancing arbitrage with Amazon EMR and Apache Iceberg , we showed how to use Apache Iceberg in the context of strategy backtesting. In this post, we focus on data management implementation options such as accessing data directly in Amazon Simple Storage Service (Amazon S3), using popular data formats like Parquet, or using open table formats like Iceberg.
A Name That Matches the Moment For years, Clouderas platform has helped the worlds most innovative organizations turn data into action. As the AI landscape evolves from experiments into strategic, enterprise-wide initiatives, its clear that our naming should reflect that shift. Thats why were moving from Cloudera Machine Learning to Cloudera AI. This isnt just a new label or even AI washing.
In January, CDO Magazine carried an article by a consortium of authors including Dr. Tom Redman, John Ladley, Dr. Anne-Marie Smith, and others. The eye-catching headline: Data Governance is failing heres why.
GAP's AI-Driven QA Accelerators revolutionize software testing by automating repetitive tasks and enhancing test coverage. From generating test cases and Cypress code to AI-powered code reviews and detailed defect reports, our platform streamlines QA processes, saving time and resources. Accelerate API testing with Pytest-based cases and boost accuracy while reducing human error.
For over a decade, Lumina Foundation has tracked the nations progress in post-high school educational attainment, providing key insights for policymakers, researchers, and advocates. Their goal has always been clear: increasing education access and ensuring credentials lead to real economic opportunities. Now, theyre taking that mission a step further.
Reading Time: 4 minutes In 2024, generative AI (GenAI) became top-of-mind, as companies began to leverage it for increased productivity. Additionally, storage continued to grow in capacity, epitomized by an optical disk designed to store a petabyte of data, and the global Internet population. The post Denodos Predictions for 2025 appeared first on Data Management Blog - Data Integration and Modern Data Management Articles, Analysis and Information.
In this article, we dive into the concepts of machine learning and artificial intelligence model explainability and interpretability. We explore why understanding how models make predictions is crucial, especially as these technologies are used in critical fields like healthcare, finance, and legal systems. Through tools like LIME and SHAP, we demonstrate how to gain insights […] The post ML and AI Model Explainability and Interpretability appeared first on Analytics Vidhya.
Many software teams have migrated their testing and production workloads to the cloud, yet development environments often remain tied to outdated local setups, limiting efficiency and growth. This is where Coder comes in. In our 101 Coder webinar, you’ll explore how cloud-based development environments can unlock new levels of productivity. Discover how to transition from local setups to a secure, cloud-powered ecosystem with ease.
The ability to identify business opportunities is a cornerstone of success in todays fast-paced market. As industries evolve and consumer demands shift, businesses must remain agile and forward-thinking to stay relevant. Identifying business opportunities fosters growth and unlocks innovation, allowing corporations to adapt to emerging trends and outpace competitors.
Use Agentic AI for Autonomous Workflow and Task Completion Artificial Intelligence (AI) is advancing, evolving and changing at lightning speed. It is nearly impossible to keep up with the changes, and to understand what, if anything, each of the new products and developments can offer to a business. One of the newest stars in the AI universe is Agentic AI.
Perhaps we got digital transformation wrong this whole time. Instead of focusing on the transformation part, we did a lot less transforming and a lot more digitalization. Instead of seeing digital as a new paradigm for our business, we over-indexed on digitizing legacy models and processes and modernizing our existing organization. As a result, most businesses remain saddled with complexity, department silos, and old ways of doing things.
Technology should be viewed as an enabler of program success for diversity, equity, inclusion and belonging, providing extended support that enables teams to expand their reach and ability to execute more complex business processes. AI-powered recruiting platforms, for example, help remove bias from the hiring process by analyzing job descriptions and identifying language that may unintentionally deter diverse candidates.
Large enterprises face unique challenges in optimizing their Business Intelligence (BI) output due to the sheer scale and complexity of their operations. Unlike smaller organizations, where basic BI features and simple dashboards might suffice, enterprises must manage vast amounts of data from diverse sources. What are the top modern BI use cases for enterprise businesses to help you get a leg up on the competition?
Google Analytics 4 (GA4) provides valuable insights into user behavior across websites and apps. But what if you need to combine GA4 data with other sources or perform deeper analysis? Thats where Amazon Redshift and Amazon AppFlow come in. Amazon AppFlow bridges the gap between Google applications and Amazon Redshift, empowering organizations to unlock deeper insights and drive data-informed decisions.
The New York Times recently published a love letter to a data tool. The recipient of this adoration is the economic data solution provided by the St. Louis Federal Reserve and affectionally called FRED. The article starts: Fans post about him on social media. Swag bearing his name sells out on the regular. College professors dedicate class sessions and textbook sections to him.
In my journey as a data management professional, Ive come to believe that the road to becoming a truly data-centric organization is paved with more than just tools and policies its about creating a culture where data literacy and business literacy thrive.
The Shifting Asset Paradigm In 1988, Australia became one of the first countries to formally recognise intangible assets on the balance sheet. During the 1980s, Australia experienced a surge in corporate mergers and acquisitions (M&A), fuelled by economic liberalisation and deregulation. Companies were being bought and sold at values far exceeding their net tangible assets.
📌Is your Data & AI transformation struggling to really impact the business? Discover the game-changing StratOps approach that: Bridges the Gap : Connect your Data & AI strategy to your operating model, to ensure alignment at every level. Prioritizes Outcomes : Focuses on concrete business outcomes from day one, rather than capabilities in isolation.
We organize all of the trending information in your field so you don't have to. Join 42,000+ 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.
367 
Let's personalize your content