Data Analytics News: Domo Drops to Progress for $400M

This week’s data analytics news brings a landmark acquisition, a sobering look at AI return on investment, and a string of product launches reshaping how enterprises govern and deploy artificial intelligence. From a $400 million buyout to new research revealing that most companies still can’t prove their AI investment is paying off, here’s everything data professionals need to know heading into the weekend.

Progress Software Acquires Domo’s AI and Data Platform for $400M

The biggest story in data analytics news this week is the $400 million acquisition of Domo’s AI and data platform by Progress Software. Announced July 22, 2026, Domo’s Board of Directors unanimously approved an agreement to sell substantially all of the company’s assets to Progress Software Corporation, with the deal expected to close before Progress’s fiscal year ending November 30, 2026. The $400 million cash price represents an 81% premium over Domo’s 30-day volume-weighted average share price, giving shareholders approximately $4.84 per share in net proceeds. Progress is betting that Domo’s agentic platform — built to turn fragmented enterprise data into governed, AI-ready intelligence — will meaningfully extend its existing data management portfolio and help enterprises move from raw data chaos to decision-ready AI.

Domino Data Lab: 57% of Enterprises Say AI ROI Still Lags Spend

Despite significant progress in getting AI into production, the majority of enterprises still can’t point to a clear financial return. A new survey by Domino Data Lab finds that 57% of organizations report that AI ROI does not yet outpace their AI spend — a figure that is unchanged from 2025 — even as 93% say production outcomes have improved year over year. The data highlights a persistent gap: teams are getting better at operationalizing models, but they’re struggling to translate those improvements into measurable business value. For data analysts and engineers, this is a signal that instrumentation and business-case framing around AI pipelines matters as much as model accuracy. If your organization is building the infrastructure to query and filter analytics outputs at scale, you’re building the foundation that turns operational AI success into financial ROI.

DataRobot Gives Enterprises Full Control Over Where and How AI Runs

DataRobot launched a major update this week giving enterprises explicit control over where their AI models are deployed and how they are governed. The release directly addresses a growing demand from regulated industries — financial services, healthcare, government — for deployment flexibility across cloud, on-premises, and hybrid environments without sacrificing governance. DataRobot is positioning model placement control, infrastructure choice, and compliance monitoring as differentiators for organizations scaling AI into production. The update is especially relevant for data teams whose stack involves machine learning pipelines that need to satisfy internal compliance requirements before a model can be promoted to production.

DataCamp Launches Learning Collaboration With Google Cloud

DataCamp announced a new learning collaboration with Google Cloud this week, extending its curriculum with role-based learning paths focused on practical Google Cloud skills. The partnership targets data practitioners looking to modernize applications, secure cloud environments, and develop hands-on expertise in AI and data engineering on Google Cloud’s platform. With more organizations running analytics workloads directly on managed cloud infrastructure, this collaboration positions DataCamp as a go-to resource for practitioners navigating the shift from on-premises tooling to cloud-native data stacks.

Simplilearn and Duke University Partner on Agentic AI Career Program

Simplilearn is collaborating with Duke University on a new 20-week program designed to prepare professionals for careers in generative and agentic AI. The program includes live online instruction, hands-on projects, and a dedicated Agentic AI workshop, giving participants a structured path from foundational AI concepts to real-world use cases. As organizations race to build and govern AI agents that can autonomously act on business data, demand for professionals who understand both the technical and governance dimensions of agentic AI is accelerating rapidly.

Gurobi Publishes Inaugural State of Mathematical Optimization in Academia

Gurobi released the first edition of its State of Mathematical Optimization in Academia report, drawing on surveys from more than 1,180 students and faculty. The headline finding: mathematical optimization is increasingly intersecting with generative AI in academic research and teaching, with coding assistance and model generation emerging as the most promising use cases for combining GenAI with traditional optimization tools. For data scientists and analysts working on operations research, supply chain, or financial modeling problems, this report signals that the next frontier isn’t optimization or GenAI in isolation — it’s hybrid approaches that use each where it has a comparative advantage.

Looking Ahead

This week’s data analytics news underscores two converging pressures on data teams: consolidation at the platform layer (Domo, Progress Software) and a mounting imperative to prove business value from AI investments (Domino Data Lab’s sobering survey). Whether you’re an analyst building dashboards, an engineer managing pipelines, or a data scientist deploying models, the industry is moving fast — and keeping up with the tools and deals shaping the ecosystem is no longer optional. Check back here for daily data analytics news coverage, and explore our SQL for Data Analysis series to sharpen the querying fundamentals that underpin every analytics workflow.