Domo is powerful and expensive.
Most deployments use a fraction of either.

Usually worth fixing, but it depends on the DataFlow architecture. If the underlying datasets are reasonably clean and the connector layer is solid, we can rebuild the transformation and presentation layers on top without starting from scratch. If the DataFlows are deeply nested with undocumented logic and the datasets have no clear ownership, a controlled rebuild is sometimes faster. We make that call after the audit, not before.

Internal Domo admins are usually strong on day-to-day card building and user management but have not had the time or mandate to fix structural problems that accumulated during the initial rollout. We bring outside perspective on what good Domo architecture looks like in practice, the capacity to do the audit and rebuild work without pulling your admin off their regular queue, and patterns from other implementations that your team has not had exposure to.

We document every unique Beast Mode formula in the instance, map which cards use each version, and then work with the business stakeholders - not just the Domo admin - to agree on the correct definition. Once there is agreement, we build the canonical version, update all affected cards, and retire the variants. We also put a naming convention in place so future Beast Modes are easier to govern.

We can audit active versus inactive users, identify cards and dashboards that have no views in the past quarter, and help you scope Domo Everywhere or Domo Publish as alternatives for viewer-only users who do not need full named licenses. Whether that changes your contract terms is a conversation with Domo directly - we do not negotiate licenses on your behalf, but we can give you the usage data to have that conversation from a position of fact.

The audit phase is usually two to three weeks depending on instance size. The rebuild phase varies - a focused DataFlow and Beast Mode cleanup for a mid-size instance can run four to six weeks. A full architecture redesign including PDP, dashboard rebuild, and connector rationalization runs longer. We scope the work after the audit so you have a specific timeline before committing to the rebuild phase.

Yes. Domo has AutoML capabilities and integrates with Jupyter Notebook-style workspaces through Domo Jupyter. If your team wants to build predictive models that feed back into Domo cards or trigger alerts, we can scope that work. In practice, most mid-market teams get more value from fixing their core data pipeline and governance first before layering in predictive features - but we will tell you honestly if your situation is different.

Domo has native connectors for both Salesforce and NetSuite, and we have implemented those connections in production environments. The real work is not the connector setup - it is aligning the entity keys across systems so that a customer record in Salesforce, a customer account in NetSuite, and a row in your Domo dataset all refer to the same company without manual reconciliation. That data modeling work is where most cross-system Domo implementations break down, and it is where we spend the most time.