In Part 1, we introduced Managed Data & Insights as Coeo’s managed service for modern data platforms, combining operational support, enhancement capacity and specialist expertise across Microsoft Fabric, Power BI, Azure Databricks, Azure Synapse and related Azure services.
The core idea was simple: most data teams need reliable BAU support, controlled improvement capacity and specialist expertise without having to build every capability permanently in-house. In Part 2, we’ll look at what that means in practice.
The examples below show how three clients adapted Managed Data & Insights around their own teams, platforms and priorities. Same service, different operating model.
These are not intended as fixed packages. They illustrate the range of ways a managed service can be configured: taking BAU pressure off an internal team, extending coverage outside normal hours, or providing continuity after go-live so new value can keep flowing.
Retail: “We need BAU taken off our plate so we can innovate”
A retail client had a strong internal data function delivering new datasets and improving reporting. The problem wasn’t capability, it was capacity.
BAU demand was consuming all of their time: operational requests, failures, refresh issues, small changes, stakeholder queries, ‘can you just…’ work. The team could keep things running, but innovation kept slipping because the day-to-day never stopped.
How we helped:
- Acted as an extension of the client’s data team and took ownership of BAU responsibilities across their Azure, Microsoft Fabric and Power BI reporting estate – monitoring, incident response, operational changes and predictable maintenance work.
- Picked up enhancement work from the backlog when the client team hit capacity limits, so progress didn’t stall whenever internal priorities spiked.
Why this matters: data and analytics solutions are rarely static. The platform needs to stay reliable and evolve. Taking BAU off the team doesn’t just free time; it protects delivery momentum and keeps improvement continuous.
Construction: “We want early-hours coverage, but only escalation during the UK day”
A construction client’s requirement was very different. They didn’t want full outsourcing. They had internal ownership and wanted to remain hands-on during UK business hours.
What they needed was early-hours coverage when their team wasn’t online, a reliable escalation path for specialist support and advice, and confidence that issues would be handled before they disrupted the business day.
How we helped:
- Our offshore team provided full support in the early hours – monitoring, triage, incident response and restoration across their data & analytics stack.
- During UK hours, the client team took over operational ownership, using Managed Data & Insights for L3 support, escalations and advisory input only.
Why this matters: This is flexibility without ‘handing over the keys’. The client kept ownership and pace during the day, while still benefiting from a wider support window and consistent operational practice.
Education: “We need support after go-live – and we want to build more use cases”
An education client went live with a new data solution successfully. The usual pattern after go-live is that the project ends, the team moves on, and the platform is left in a ‘good luck’ state.
But this client had an immediate next challenge: demand for additional use cases grew quickly after deployment. They needed ongoing BAU support and a way to deliver use cases without constant project mobilisation.
How we helped:
- Provided BAU operations (monitoring, incident handling, controlled change) across their Azure Synapse, Azure Databricks and Power BI reporting environment, keeping the solution reliable post go-live.
- Used the Enhance model to deliver multiple additional use cases – prioritised, governed, and delivered iteratively.
Why this matters: This avoids the stop-start cycle of ‘project → pause → project’. Instead, the platform becomes a living product: stable enough to trust, and flexible enough to keep delivering new value.
What the examples have in common
For a data leader, the important point is not that these clients used the same service. It is that each client kept control of the outcome they cared about most: freeing internal teams to innovate, extending operational coverage without outsourcing everything, or turning a successful go-live into a sustainable long-term capability.
These examples work because the service isn’t built around a single operating model. It flexes around what a client actually needs:
- Extension of your team: taking on BAU, supplementing specialist skills, and picking up enhancements when internal capacity is stretched
- Out-of-hours coverage plus L3 escalation during UK hours
- Post go-live continuity that turns delivery into a stable long-term capability
And because our Managed Data & Insights scope covers modern platforms – including Microsoft Fabric, Azure Databricks, Azure Synapse, and Power BI – the service aligns to your architecture rather than forcing you into a single toolset.
When Managed Data & Insights tends to be a good fit
If you recognise any of these, it’s worth a conversation:
- BAU support is consuming the time you want to spend delivering new value
- Your platform needs stronger operational discipline (monitoring, incident/problem/change)
- You need flexibility without committing to permanent headcount
- You want a steady cadence of enhancement and improvement, not sporadic projects
Final thought: the goal is momentum
The best analytics platforms are not just technically stable. They are operationally resilient and continuously improving.
Managed Data & Insights gives you a practical way to maintain momentum, whether that means taking BAU off your team, extending coverage outside normal hours, delivering a backlog of enhancements, or providing escalation support when needed.
Every organisation’s data estate is different, so the right support model should be too. If you’d like to explore what Managed Data & Insights could look like for your environment, speak to the Coeo team.