Business & Technology

The 6 next big things in data for 2026

Training data begat large language models—and ever since, they’ve been intimately bound. This year’s honorees include a database tuned for AI agents, a dataset for AI-aided diagnosis of breast tumors, and a platform that gives companies more control over how their data is acquired and used. DatabricksFor bringing databases up to AI speedAccording to Databricks, AI agent creation of test and development environments on its platform has grown from 0.1% two years ago to 97% today—a dynamic that traditional database architectures have been ill-equipped to accommodate. In response, the company developed Lakebase, a ground-up rearchitecture of databases that can spin them up and shut them down at AI speeds, facilitating the multitude of small, fast workloads of AI-driven development. In August, Databricks raised $5 billion in a strategic funding round, valuing it at $190 billion. The company’s revenue run rate has grown 80% year over year. DataPelagoFor transparently routing code to the optimal processorWhile GPUs and NPUs are the processor architectures most closely aligned with AI, high-performance computing tasks also take advantage of two other kinds of processors, CPUs and FPGAs. Effectively routing the right computations to the right processors can reduce significant overhead. DataPelago’s Nucleus modernizes CPU-centric designs of legacy systems and acts as a bridge between data lakes and query engines. It routes computing requests to the most efficient processor type available without requiring any code changes. The company says that its Accelerator for Apache Spark delivers up to 10x faster performance at 80% lower compute costs. In August, DetaPelago was acquired by NetApp, which put Nucleus at the core of its full-stack AI offering. DDNFor scaling storage to supercomputer workloadsTraining ever larger and more capable data models has been hampered by slow data path throughput, which can result in GPU underutilization. DDN’s EXAScaler is a parallel file system technology. One of its highest-profile clients is Google Cloud’s Managed Lustre, a file service used for AI and high-performance computing models. Salesforce and Nvidia are also customers. The managed service can scale up to 10 terabytes per second by combining parallel I/O and high-throughput shared storage. DDN is on track to double revenue to $1 billion in 2026 on surging AI storage demand. HydrolixFor improving site traffic insights by watching the botsAI agent and bot traffic now accounts for more than half of all web traffic, according to Cloudflare. Hydrolix’s Bot Insights makes this behavior visible, providing personalized dashboards for different job roles and allowing customers to decide which ones to block. They can also query the data to answer questions about latency during checkouts or rising search costs. Hydrolix reports that Bot Insights reduces incident attribution time by 95%. The company recently raised an $80 million Series C venture round and was featured as the engine powering AWS’s Agentic Intelligent Operations platform for streaming media iMeritFor improving breast cancer diagnosis through a breakthrough datasetWhen detected early, the five-year survival rate for breast cancer exceeds 90%. The clinical standard for screening—Digital Breast Tomosynthesis, also known as 3D mammography—outperforms traditional 2D mammography for detecting lesions that could be cancerous, particularly in women with dense breast tissue. Unfortunately, this method is held back by a shortage of biopsy-confirmed datasets. iMerit has worked with Segmed and Advocate Health to release an open-source, freely available dataset of more than 500 biopsy-confirmed exam results. The dataset was released in accordance with HIPAA regulations and split roughly between benign and malignant cases. The dataset should help researchers hone AI’s ability to more accurately and quickly assist diagnosis. In August, data and AI company EXL completed its $310 million acquisition of iMerit. Mozilla Data CollectiveFor giving usage control to dataset ownersOne of the most prominent controversies around AI concerns compensation of the parties behind the data sources used in its training. While content owners are striking deals with AI giants or blocking bots to pave the way for negotiations, the Mozilla Data Collective takes a more proactive approach. Entities that own datasets can share them under their own terms while retaining ownership and control over how that data is used, including compensation. The collective includes more than 190 organizations sharing over 1,700 curated datasets. This year, Mozilla spun out the Data Collective as an independent entity. The companies and individuals behind these technologies are among the honorees in Fast Company’s Next Big Things in Tech awards for 2026. Read more about the winners across all categories and the methodology behind the selection process.