Data Analytics News: AI ROI Lags Spend for 57% of Firms

The latest data analytics news this week delivers a sobering reality check for enterprise AI programs alongside a wave of new tools designed to close the gap between AI investment and measurable results. From a landmark survey revealing persistent ROI shortfalls to new governance platforms reshaping how teams deploy AI, there is plenty for data professionals to unpack on July 26, 2026.

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

A major new report from Domino Data Lab puts hard numbers on the feeling many data leaders have had for the past two years: AI budgets are growing faster than returns. The Fifth Annual Domino Enterprise AI Report — based on a survey of 639 senior AI leaders across financial services, life sciences, and the public sector — found that 57% of enterprises say AI ROI still fails to outpace their investment, a figure that has remained flat since 2025. The stagnation is striking because 93% of the same respondents report improved AI production capability this year, up from 88% in 2025. Domino calls this the “last-mile gap”: models are reaching production, but business users aren’t yet unlocking their value. Expanding agentic AI use and upskilling business users tied as the top investment priorities cited for the year ahead. For data teams writing queries and building pipelines to feed AI models, the report is a reminder that technical readiness alone doesn’t create business value — and that closing the gap demands better tooling, training, and cross-functional collaboration. If your organization is just beginning to structure its data for AI, our guide to filtering data with SQL’s WHERE clause covers the fundamentals of shaping datasets that downstream AI systems can actually use.

DataRobot Gives Enterprises Full Control Over Where and How Their AI Runs

On July 22, DataRobot announced sweeping updates that address a growing boardroom concern: who actually controls the infrastructure behind enterprise AI? The announcement was prompted in part by what the company described as a government directive that cut off access to a widely used AI model overnight, sending shockwaves through regulated industries including financial services, defence, and healthcare. DataRobot’s new capabilities let organizations choose where AI models are hosted — public cloud, on-premises, edge, or air-gapped sovereign environments — and govern them consistently regardless of deployment location. The company is positioning unified AI governance across deployment environments as a key differentiator, building on a July 2 announcement that had already extended its governance framework beyond the cloud. For data analysts and engineers who build the analytical layers feeding these production AI systems, the trend reinforces that governance and query-layer controls matter as much as model performance. Understanding how to precisely filter and shape data at the SQL level — as explored in our master advanced filtering guide — is increasingly relevant as governance frameworks demand tighter data provenance.

Redgate Launches Database Change Control Layer to Support AI-Driven Development

Redgate Software released a major update to Redgate Flyway Enterprise on July 21, directly targeting the governance risks created when AI accelerates software delivery. The headline feature is a new Flyway Enterprise MCP Server, which brings a governed, agentic approach to database schema changes — allowing teams using AI coding agents to manage database migrations safely without bypassing change control processes. The release also introduces advanced Databricks capabilities, with Snowflake support and operational dashboard views on the roadmap. Redgate framed the urgency by citing a Gartner projection that 40% of enterprises will decommission autonomous AI agents by 2027 due to governance gaps exposed by costly production incidents. Unlike generic AI tools that treat the database as just another deployment target, Redgate’s approach builds AI that understands the database estate, anticipates failures, and enforces workflows before changes land in production. This is data analytics news with direct implications for every team running AI-assisted development pipelines.

DataCamp Partners with Google Cloud to Expand Cloud and AI Skills

DataCamp announced a new learning collaboration with Google Cloud this week aimed at helping data practitioners build practical cloud skills through hands-on training. The partnership extends DataCamp’s cloud curriculum with role-based learning paths covering application modernization, cloud security, and AI and data engineering workflows on Google Cloud. The timing aligns with broad industry demand: as organizations migrate analytical workloads to cloud data platforms, practitioners who can bridge data engineering skills with cloud-native tooling are increasingly sought after. The collaboration is the latest in a string of platform-level upskilling deals reflecting that technical education is now a strategic priority for cloud providers trying to grow their ecosystem of skilled practitioners.

Simplilearn and Duke University Launch 20-Week Agentic AI Program

Simplilearn announced a collaboration with Duke University this week to deliver a new 20-week program designed to give working professionals a structured foundation in generative and agentic AI. The program combines live online instruction with hands-on projects and an Agentic AI workshop, connecting technical concepts to practical workplace applications. The partnership reflects the broader trend of universities and ed-tech companies teaming up to address the growing demand for agentic AI literacy — a skill set that sits at the intersection of data science, software engineering, and business process automation. For data professionals exploring the AI skills landscape, this kind of structured curriculum is increasingly relevant alongside self-directed learning resources.

Gurobi Publishes Inaugural Mathematical Optimization in Academia Report

Gurobi released its first State of Mathematical Optimization in Academia report this week, drawing on surveys of more than 1,180 students and faculty who actively use Gurobi tools. The report found that mathematical optimization is increasingly intersecting with generative AI in both teaching and research settings, with AI coding assistance and automated model generation emerging as the top use cases combining the two disciplines. The findings are relevant for data scientists and analysts working on supply chain, logistics, financial modeling, and resource allocation problems — domains where optimization and AI are beginning to converge into unified analytical workflows.

That wraps today’s data analytics news roundup. The recurring theme across this week’s announcements is the gap between AI investment and verifiable business value — and the tooling emerging to close it, from governance platforms to upskilling programs to database change control layers. For data professionals looking to build stronger analytical foundations that underpin production AI systems, revisiting core skills like SQL data filtering and advanced query techniques remains time well spent. Check back tomorrow for the next edition of our daily data analytics news briefing.