Data Semantics runs data platforms and BI estates under agreed service levels. Our DataOps Cockpit watches every job, pipeline, refresh, cost and network link, fixes routine failures and raises tickets automatically, while our engineers handle the rest.
Your platform
Pipelines & jobsRefreshesData movementCapacity & costGateways & networkDataOps Cockpit
Engineers behind every alert
Runs on
Microsoft FabricPower BIDatabricksSnowflakeData Semantics runs data platforms and BI estates for clients on Microsoft Fabric, Power BI, Databricks and Snowflake, under agreed service levels. Automated monitoring catches failures, checks data quality, triages user questions and watches cloud cost, while our engineers handle changes, enhancements and anything the agents escalate.
Pipelines fail at night, sources change their schema, gateways drop and capacity bills creep up. In most estates users find the problem first, by email, and the same fix is repeated week after week. A managed service built on live monitoring and automatic tickets turns that around.
One team and one set of service levels for your platform, pipelines, reports and users.
The data platform kept healthy, secure and up to date.
Every pipeline and refresh watched, with failures fixed before users see them.
Numbers checked continuously, so problems are caught at the source.
Questions answered quickly, in Teams or by ticket.
A steady flow of improvements, planned with you every month.
Faster reports and a cloud bill that matches the value you get.
Every Data Semantics managed service runs on our DataOps Cockpit. Our engineers and your team see the same live view of the platform, and problems are raised the moment they happen, not when a user notices.
The same framework runs on platforms we built and platforms others built.
Running AI agents in production?Testing, monitoring and governing agents has its own managed service.AI Agent Operations →Automated monitoring and AI-assisted triage handle the routine work of running a platform, so our engineers focus on changes, improvements and the issues that need judgement.
Known failures are fixed from the runbook and re-run; anything new goes to an engineer.
You get: most failures fixed in minutes
Checked by: engineers review every fix
How-to and data questions are answered in Teams from your documentation and semantic model, and the rest triaged.
You get: faster answers for users
Checked by: our analysts
AI drafts small report and measure changes from user requests.
You get: changes in days, not weeks
Checked by: consultants review and release
Tell us what runs on your platform today. We’ll show you what we would monitor, fix and hand back to your team.
Take over → Fix the basics → Operate → Move forward
A short health check shows where you stand before any long-term commitment.
We review your platform, pipelines and reports and tell you what’s fragile, slow, unused or costing too much.
We run the platform, support your users and deliver improvements every month under agreed service levels.
About managed data and BI
The DataOps Cockpit is the framework Data Semantics uses to run every managed data platform. It gives one live view of jobs, pipelines, refreshes, data movement, cost, capacity and network or gateway failures, and raises tickets automatically when something goes wrong.
The DataOps Cockpit watches refreshes and pipelines around the clock; automation fixes routine failures and re-run them, detect source changes and quality issues, answer user questions in Teams and flag rising costs. Engineers handle changes and anything that is escalated.
What’s covered
Microsoft Fabric, Power BI, Azure Synapse, Data Factory, Databricks and Snowflake, with sources such as SAP, Oracle, Dynamics 365 and Salesforce.
Yes. Data Semantics starts with an inventory and health check, sets up monitoring and runbooks, then takes over operations. Many of the platforms we run were built by other partners or in-house teams.
Yes. Data Semantics connects the DataOps Cockpit to your ticketing system, so failures, outages and quality issues are logged automatically with the evidence attached, routed to the right team and closed when fixed.
Yes. Each month Data Semantics plans and releases improvements with you, such as new measures, pages, reports and sources, alongside keeping everything running.
Yes. If you run AI agents in production, our AI Agent Operations service monitors and improves them alongside your data platform.
Service levels and cost
Service levels for response, resolution and data availability are agreed with you based on how critical each report and pipeline is. Monitoring runs 24/7 through the DataOps Cockpit, and routine failures are fixed and re-run automatically at any hour. Engineer response times follow those service levels, with the tightest targets on business-critical refreshes, pipelines and reports.
Usually, yes. Data Semantics tunes slow models and queries, right-sizes capacity and compute, and retires unused content, and review cost with you every quarter.
Data Semantics runs the platform health check as a fixed-price engagement. The managed data and BI service is then a monthly service under agreed service levels.
Start with a platform health check. We’ll show you what’s fragile, slow or costing too much, and how we’d run it.
