data
data on Beyond Market Intelligence: a running collection of 75 stories we have gathered and hand-picked because they are worth your time. Every post here touches on data in some way — the news, the analysis, the deep dives, and the occasional surprise find. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work with data. New stories are added to this page as we find them, so check back if you want to keep up with what is happening around data, or subscribe to the RSS feed to get them as soon as they are published. Browse the collection below, or head back to the homepage to see everything Beyond Market Intelligence is covering right now.
XDOF, just three months out of stealth, is in talks for a Series B at a $1.2B valuation
XDOF, emerging from stealth just months ago, is rapidly establishing itself as a leader in robot data solutions. Now, the startup is in discussions for a Series B funding round valued at $1.2 billion—a testament to its transformative potential. This swift ascent highlights the evolving landscape of enterprise buying, as recently explored in our article, "Startup ARR is less secure than ever.
The Power BI Developer's Survival Guide to Microsoft Fabric
Power BI developers, a significant shift is underway. Microsoft Fabric has arrived, effectively replacing Power BI Premium. This guide provides a clear, concise overview of what’s changed—and what hasn’t—to ensure a smooth transition. We'll equip you with the essential knowledge to navigate this evolution and confidently begin leveraging Fabric's capabilities. If you're exploring the broader landscape of AI-powered development, consider "How to Solve the Right Problem in the Age of Agentic AI" for a practical framework.
Ollie is betting its focus on privacy can help it win the AI assistant race
Ollie is entering the AI assistant arena with a bold proposition: prioritizing user privacy. Unlike competitors, Ollie pledges not to leverage your personal data to train its AI models or share it externally. This focus on data security aims to resonate with families seeking a trustworthy digital companion. While requiring access to daily life details to function effectively, Ollie differentiates itself through its commitment to safeguarding user information—a strategy that could prove pivotal in a crowded market.
A Practical Introduction to PySpark Window Functions
Traditional `groupBy` functions in PySpark offer a foundational approach to data aggregation, but often fall short when complex calculations require context beyond a single group. This practical introduction explores PySpark Window Functions—a powerful tool for performing calculations across a set of rows related to the current row. Discover how window functions empower you to derive richer insights, enabling more sophisticated data analysis and transformative reporting.
What We Miss About Missing Values
Missing values are a ubiquitous challenge in data science, yet their implications often go unexamined. "What We Miss About Missing Values" explores the hidden assumptions embedded within the data we *do* observe—recognizing that what's absent can be just as informative as what's present. This post delves into the biases introduced by missingness and offers a framework for more thoughtful analysis. For a related perspective on navigating complexity in data systems, see "Why RAG Complexity Should Be Earned."
5 AI Skills That Will Keep Data Scientists Relevant in 2027
## 5 AI Skills That Will Keep Data Scientists Relevant in 2027 The data science landscape is evolving rapidly. To remain valuable through 2027, focus on these five essential AI skills: Prompt Engineering, Generative AI Model Fine-Tuning, Responsible AI Implementation, Advanced Retrieval-Augmented Generation (RAG), and AI-Powered Data Synthesis. Each addresses a critical challenge – from maximizing LLM output to ensuring ethical deployment and generating synthetic datasets. Discover runnable code examples for each skill—easily pasted into your notebook—to accelerate your learning.
Your LLM Can Return Perfect JSON and Still Be Wrong
Large Language Models (LLMs) excel at producing seemingly flawless JSON outputs, yet these structures can still mask underlying inaccuracies when dealing with real-world, incomplete data. Recent exploration reveals a critical distinction: perfect formatting doesn’t guarantee factual correctness. This post dives into that nuance, examining how structured outputs can mislead and offering insights for more robust data validation. For a broader perspective on AI's impact on technological landscapes, consider "Nvidia’s $3.5B MediaTek bet reveals its plan for tackling Big Tech’s AI chip buildout."
Do you use a whiteboard when thinking? [D]
Many data scientists and engineers retain a fondness for the whiteboard's intuitive problem-solving power, even as their workflows shift to code and complex models. Originally shared by /u/Huge-Leek844, this post explores how professionals in DSP, data science, and ML integrate that visual thinking style into their daily work. Do you still rely on whiteboards, or do you transition directly to implementation? Explore the discussion and consider how techniques like those highlighted in "FlexGanttFX is Open Source" can complement your approach.
Cloudflare Extends AI Search to Make it Easier for Agents and Developers to Search Custom Data
Cloudflare is expanding AI Search, simplifying data access for both agents and developers. This built-in search and retrieval service provides a ready-to-use engine for custom data, streamlining AI agent integration and enabling multimodal search. Seamlessly integrated with existing Cloudflare tools, AI Search empowers users to unlock valuable insights. Discover how this innovation transforms data workflows—for a deeper dive into AI automation fundamentals, explore our "Top 7 Free AI Automation Courses with Certificates" article.
Connecting My LangGraph AI Agent to Postgres
Connecting your LangGraph AI agent to a Postgres database unlocks powerful capabilities for data-driven workflows. This post details how to establish that connection, offering clear guidance for both local development and cloud deployment. We’ll explore setting up the backend using Docker for streamlined local testing, and then outline strategies for scaling to the cloud. For those tackling complex enterprise workflows, consider the recent exploration of an 8B AI model mirroring Claude Opus—a relevant challenge in managing substantial data sets.
Sprains, pain, and whiplash: Waymo and Zoox test drivers are getting hurt as robotaxis scale
Scaling autonomous vehicle testing presents unforeseen challenges, as evidenced by recent OSHA data revealing over two dozen injuries sustained by Waymo and Zoox test drivers in 2024 and 2025. These incidents, stemming from abrupt vehicle maneuvers like hard braking, underscore the complexities of ensuring human safety alongside evolving AI systems. While the pursuit of driverless technology progresses, prioritizing driver well-being remains paramount.
Viral AI startup Instinct has raised $350 million at a $2.5 billion valuation
Instinct, the viral AI startup, has secured a remarkable $350 million in funding, catapulting its valuation to $2.5 billion just one year after launch. This rapid ascent has ignited significant industry hype – and sparked important conversations around data privacy. Instinct’s impressive growth underscores the surging demand for advanced AI infrastructure, a trend also reflected in Amazon's recent decision to triple its Nvidia chip order. Explore this evolving landscape further with our deep dive into Anthropic’s compute-intensive partnership with Nscale.
Reviewing 4 papers for AAAI 2027 and none have code, Reject? [D]
Reviewing submissions for AAAI 2027 presents a recurring challenge: empirical claims lacking supporting code or data. While a complete absence of reproducibility materials shouldn't trigger an automatic rejection—legitimate concerns around funding and intellectual property exist—it significantly impacts reviewer confidence. Flagging this explicitly in the review, requesting anonymized code during the rebuttal phase, is a pragmatic approach. As explored in "Millwright — experimenting with an end-to-end machine learning framework in Rust," ensuring verifiable results remains paramount for robust AI research.
Mastering the AI Project Cycle: From Concept to Production
Successfully deploying AI isn’t about model selection alone; it's about navigating a structured journey known as the AI Project Cycle. From precisely defining the problem to ongoing monitoring and refinement, this cycle ensures a robust and impactful AI system. Teams leveraging this approach consistently achieve better outcomes, moving beyond experimentation to sustainable production. Explore this essential framework and discover how to transform your AI initiatives. For a deeper dive into related challenges, see "Is Agentic AI Just Automation?".
One Document Type, a Million Files: Structured Extraction into the SQL Table RAG Queries
Unlock the power of your enterprise data with structured extraction. This guide, "One Document Type, a Million Files," details a streamlined approach to transforming unstructured documents into SQL tables optimized for Retrieval-Augmented Generation (RAG) queries. In just one hour with two people, extract six to ten key fields, leveraging signals to ensure data integrity and filter accuracy. Explore how this method empowers efficient data access and analysis—a critical step toward future-focused data management.
The Data & AI Leadership Questions That Will Define the Next Stage of Enterprise AI
For leaders translating data and AI strategy into tangible enterprise results, the next phase demands focused attention. We’ve identified the critical questions shaping this evolution – inquiries around agent integration, secure model deployment, and the evolving role of AI in development workflows. Explore these pivotal considerations and discover how to navigate the complexities of enterprise AI adoption. For deeper insight into agent-native platforms, see our interview with OpenAI’s Thibault Sottiaux on TechCrunch.
Beyond Embedded: How DuckDB v2.0 Shifts Architecture Toward Distributed Network Capabilities
DuckDB v2.0, codenamed "Cyanoptera," represents a significant architectural shift, moving beyond embedded processing toward distributed network capabilities. This preview release, built on over 10,000 commits, introduces a client/server mode for network connections alongside key improvements in extension portability and data type handling. Performance is enhanced through asynchronous I/O and storage optimizations, setting the stage for a more scalable future. General availability is slated for fall 2026. For a deeper dive into related architectural considerations, explore "Mini book: Architecture as a Socio-Technical Craft."
Build an End-to-End Data Science Project with Grok Build and Grok 4.6
Ready to build a production-ready data science project from start to finish? With Grok Build and Grok 4.6, you can streamline your workflow, encompassing everything from Exploratory Data Analysis (EDA) and scikit-learn model training to FastAPI API creation, rigorous testing, and seamless cloud deployment. This comprehensive approach empowers you to transform raw data into impactful, scalable solutions. For a deeper dive into related techniques, explore our recent article on "Implementing Watermarking for Language Models."
Uber faces fine of nearly $1B over automated driver suspensions
The Dutch Data Protection Authority has levied a significant €825 million fine against Uber, marking the second largest penalty ever issued under Europe’s GDPR. This substantial fine stems from Uber’s practice of suspending drivers without adequate due process. The decision underscores the importance of data protection and responsible AI implementation. As enterprises increasingly leverage AI agents, it’s crucial to consider limitations on autonomy, as explored in our recent article, "Enterprises winning with AI agents are limiting how much the agents can do alone."
Estimating from No Data: Deriving a Continuous Score from Categories
Facing a data scarcity challenge? "Estimating from No Data: Deriving a Continuous Score from Categories" explores a compelling solution: leveraging low-capacity networks to generate fine-grained scores even when training data is limited to categorical labels. This walkthrough unpacks the underlying mathematics, offering a practical approach to unlock valuable insights from seemingly incomplete datasets. It’s a future-focused technique for data professionals seeking to maximize utility from available information. For context on the broader AI data landscape, see "AI data startup Micro1 reaches $500M gross run rate."
The Types of Dimensions in a Star Schema, and How to Use Them
Dimensional modeling hinges on understanding dimensions—one of its two core object types. But dimensions aren't monolithic; they encompass several distinct varieties, each serving a specific purpose in structuring data for analysis. This post explores these types, detailing how to effectively leverage them within a star schema to unlock deeper insights. We’ll clarify their roles in providing context and enabling powerful data exploration. For a related perspective on optimizing data retrieval, see "Retrieve One Row from a Table, Not the Whole Table: Row-Level Chunks for RAG."
AI data startup Micro1 reaches $500M gross run rate amid AI training boom
Micro1, an AI data startup, has achieved a remarkable $500 million gross run rate, fueled by the surging demand for high-quality AI training data. This rapid growth underscores a pivotal moment in the AI landscape, where specialized data infrastructure is increasingly critical. Micro1’s success highlights the transformative potential of accessible data solutions, empowering organizations to accelerate their AI initiatives. As OpenAI gains traction with business users, as detailed in our recent article, the need for robust data platforms like Micro1’s is only set to intensify.
OpenAI is gaining on Anthropic with business users, new data indicates
Recent data reveals a tightening race between OpenAI and Anthropic for business user adoption, demonstrating a notable shift in enterprise AI spending. Businesses are exhibiting a willingness to switch platforms as each lab releases new models, creating volatility that warrants careful consideration for investors. This fluidity raises questions about the long-term "stickiness" of enterprise AI investments. For deeper insights into related challenges, explore our recent article, "The LLM Judge That Kept Agreeing With Itself," detailing a crucial production incident.
Learn what VCs actually want, from a founder who’s raised $1B
Securing venture capital hinges on a clear grasp of your company’s financial health. Investors prioritize founders who demonstrate this understanding—a lack of clarity around metrics or delayed fundraising can severely impact valuation and deal terms. In this episode of Build Mode, host Isabelle Johannessen speaks with Sasha Orloff, founder and CEO of Puzzle, a founder who’s raised $1B, to uncover what VCs truly seek. For deeper insights into data security concerns impacting investors, explore our recent article on the Alation cyberattack.