CIOs should elevate their data-driven organizations for the AI era by sponsoring citizen data analytics initiatives.

Citizen data science aimed to bring data visualization, reporting, and dashboarding to business teams. Citizen analytics takes this several steps forward and aims to help business teams deliver capabilities across the full data, analytics, and AI lifecycle.

I recently attended Domopalooza, Domo’s annual conference, where they demonstrated their vision for data and AI. Domo is best known for its BI capabilities, but CIOs should take a fresh look at its no-code workflows, robust data integration capabilities, and emerging AI Catalyst for developing AI agents.

What CIOs need to know about Domo

At Domopalooza, CEOs and CIOs shared how citizen analytics impacts their businesses. Here’s a recap:

• Rick Bauerly, founder and CEO of Granite Partners, discussed the three biggest challenges facing manufacturers: Demand forecasting, pricing mastery, and planning/scheduling. He then showcased the analytics applications they developed on Domo, including revenue forecasting, production planning, and safety.
• Jani Radhakrishnan, CEO of Regional One Health Solutions, discussed how providing real-time patient feedback to managers, recommending changes, and capturing their actions help connect insights, actions, and outcomes.
• Scott Wruble, CIO of Feld Entertainment, creates integrated analytics that connect data across Snowflake, NetSuite, and Domo – demonstrating significant value through a streamlined technology stack.
• Tom Thomas, SVP of data strategy, analytics, and AI at Ford Direct, shared insights on their platform, which connects data from dealer websites, CRM systems, advertising, inventory, and other sources.

Below are three differentiating capabilities from Domo that CIOs need to know.

1. Develop analytics-driven workflows

Data science and workflow applications often live in two distinct worlds, with different teams and skill sets. While many BI platforms can embed data visualizations into applications, few make it easy. Developers often have to customize the BI tool’s embed code with custom JavaScript, and the UX can feel disconnected from the work to be done.

Domo does the reverse, embedding applications and workflows into a UX dominated by analytics. These include dashboards where you can update data, complete a workflow, or trigger an AI agent and then see real-time updates to the analytics.

As AI agents take on more of the work, a UX that devotes more space to data visualizations and embedded analytics can be very powerful.

2. Simplify to Magic ETLs with AI capabilities

I have reviewed several data integration and data pipeline technologies. I have also been a hands-on developer with several data integration and prep platforms. Many fail in balancing the trade-offs among capabilities, ease of use, and operational and testing capabilities.

I haven’t used Domo’s data integration capabilities, but branding it “Magic ETL” sets a high bar for expectations. Here’s the magic they demoed:

• Supporting unstructured information, including tiles (steps you drop into a data flow) for document inputs, classification, sentiment analysis, and text generation.
• Testing capabilities to see how many rows of data will be affected in each step of a data flow.
• Disabling tiles to help diagnose issues or rerun part of a data flow.
• Using split joins to diagnose issues with mismatched rows when joining data.

Andrea Henderson, senior product manager at Domo, showed me some of the other advanced Magic ETL tiles. They include tiles for AI forecasting, outlier detection, and removing duplicates, in addition to tiles for other common data transformations.

3. Innovate with AI agent development and integration

Domo presented examples of pragmatic AI capabilities and a commitment to connecting to the AI ecosystem. Some specifics:

• Use Claude and Cursor to generate code that interacts with Domo. In one demo, Lovable connected to Domo’s app dev capabilities and was used to replicate the basic functionality of a popular SaaS application.
• Rapidly develop AI agents using toolkits (reusable components) and knowledge to establish their functions with context and governance.
• Develop a semantic layer based on the Open Semantic Interchange (OSI), a standard that Snowflake, Databricks, Salesforce, Collibra, and other major technologies have committed to supporting.
• Connect AI agents to others using Domo’s MCP server.

Why review Domo

Domo presented a one-stop shop for data, AI, and analytics that can be a compelling buy for midsize organizations with significant data/AI business opportunities. Larger enterprises committed to using standard tools and requiring platforms that integrate well in the data/AI ecosystem will also benefit. Domo demonstrated a clear commitment to improving its product through customer feedback, with practical enhancements needed by development, data, and business professionals.

Their vision for AI is also worth noting. While some SaaS platforms seek fully automated agentic AI, Domo’s strategy is to connect people with data to make decisions with context.

Review all the new Domo features announced at Domopalooza.