Dataconsultant operates and improves business intelligence environments for organisations that need dependable dashboards, governed metrics, responsive user support and controlled change. The service combines monitoring, incident handling, semantic-model maintenance, data-quality checks, release management and continuous improvement so business teams can use reporting with greater confidence.
A managed business intelligence service is an ongoing operating model for supporting, governing and improving dashboards, reports, semantic models, data refreshes and user requests. Instead of treating BI as a sequence of isolated projects, the service establishes clear ownership, service levels, monitoring, escalation, change control and improvement priorities.
It is most useful where reporting has become business-critical, internal capacity is constrained, platform skills are fragmented, users experience repeated failures, or leaders need more predictable BI operations and cost visibility.
The service focuses on operational weaknesses that reduce trust, delay decisions and consume specialist capacity.
Impact: Teams lose confidence in reports and spend time checking whether information is current.
Response: Monitor schedules, dependencies, failures and recurring causes with documented escalation and recovery procedures.
Impact: Meetings focus on reconciling numbers rather than making decisions.
Response: Maintain governed semantic models, metric definitions, ownership and controlled changes.
Impact: Strategic analytics and transformation work is delayed by repetitive incidents and requests.
Response: Provide a structured service desk, triage model, runbooks and defined enhancement capacity.
Impact: Defects, access issues and performance regressions increase operational risk.
Response: Use release controls, testing evidence, approvals, rollback planning and environment separation.
Impact: Backlogs grow without transparent prioritisation or resource planning.
Response: Report demand, service performance, platform usage, enhancement effort and risk-based priorities.
Impact: Investment is not translated into consistent decision support.
Response: Combine user support, rationalisation, training, usage analysis and feedback-led improvement.
The operating model can be tailored from focused platform support to broader end-to-end BI operations.
Maintain day-to-day service health across reports, dashboards, models, gateways, refreshes and dependencies.
Handle incidents, access requests, data questions and service communications through documented queues and escalation paths.
Protect consistency by controlling semantic models, business definitions, ownership, quality checks and lineage evidence.
Plan, test, approve and deploy fixes and enhancements with traceable release evidence.
Reduce technical debt, duplicated reports and avoidable support demand while improving adoption and performance.
Deliverables are adapted to the agreed responsibility boundary, platform estate and service maturity.
| Deliverable | Purpose | Typical content | Review cadence |
|---|---|---|---|
| Service definition | Clarify responsibilities and boundaries | Scope, hours, roles, dependencies, priorities, exclusions and escalation routes | At transition and when scope changes |
| BI asset register | Establish operational visibility | Reports, dashboards, owners, users, data sources, schedules, environments and criticality | Continuously maintained |
| Runbooks and support knowledge | Standardise repeatable operations | Monitoring, recovery, access, release, incident and communication procedures | After material changes and incidents |
| Service performance report | Support governance and decisions | Demand, incidents, service levels, refresh reliability, risks, backlog and improvement actions | Agreed weekly or monthly cadence |
| Controlled enhancement backlog | Prioritise change transparently | Business value, urgency, risk, effort, dependencies, acceptance criteria and status | Regular service review |
| Quality and control evidence | Demonstrate traceable operation | Test results, approvals, access reviews, exceptions, reconciliations and release records | According to control requirements |
| Improvement roadmap | Move beyond reactive support | Rationalisation, automation, performance, governance, adoption and capability priorities | Quarterly or agreed cycle |
The process avoids fixed assumptions about timing and scales according to the estate, risk level and available evidence.
Confirm business priorities, critical reports, users, platforms, current pain points, service hours and expected outcomes.
Review architecture, report inventory, refresh dependencies, documentation, support history, security controls and open risks.
Define responsibility boundaries, service levels, queues, escalation, governance, reporting, environments and change controls.
Validate access, observe current operations, document runbooks, test recovery procedures and baseline performance.
Prioritise critical failures, aged requests, control gaps, performance issues and undocumented dependencies.
Run the service, report outcomes, manage changes, analyse recurring demand and deliver prioritised improvements.
Reliable BI requires clear decision rights across business ownership, data, technology, risk and service delivery.
The service can be designed around the client’s existing BI and data estate rather than forcing a single vendor stack.
Microsoft Power BI, Tableau, Qlik, Looker and other enterprise reporting tools where access and supportability are confirmed.
Cloud warehouses, lakehouses, databases, marts and governed semantic layers that supply reporting workloads.
Batch, API, orchestration, gateway and transformation dependencies that affect refresh reliability and recoverability.
Ticketing, monitoring, documentation, version control, testing, identity and change-management tools used within the operating model.
The most appropriate model depends on business criticality, internal capability, demand volatility and responsibility boundaries.
Defined support for a specific BI platform, report portfolio or operational workload.
Broader responsibility for service management, monitoring, user support, controlled change and improvement.
Dataconsultant operates agreed workloads alongside the client’s BI, data and technology teams.
Measures should be baselined, linked to service responsibilities and interpreted with known dependencies.
| Measure | What it indicates | Important interpretation |
|---|---|---|
| Refresh success rate | Reliability of scheduled reporting data updates | Separate BI failures from upstream source and infrastructure failures |
| Incident response and resolution | Speed and consistency of support operations | Segment by severity, business impact and dependency ownership |
| Recurring defect rate | Effectiveness of root-cause and problem management | Track repeated causes rather than ticket volume alone |
| Dashboard performance | User experience and model efficiency | Measure representative workloads and agreed thresholds |
| Backlog age and throughput | Demand management and delivery capacity | Consider priority, complexity and waiting dependencies |
| Data-quality exceptions | Fitness and consistency of reported information | Assign ownership and record accepted limitations |
| Adoption and active usage | Whether reporting supports intended users and decisions | Usage does not by itself prove business value |
| Release success rate | Quality of controlled change | Include rollback, defects and post-release impact |
A reliable estimate requires discovery. Fixed prices or timelines without an inventory, responsibility boundary and dependency review can create avoidable commercial and service risk.
It is an ongoing service for operating, supporting, governing and improving dashboards, reports, semantic models, refresh pipelines and user requests. The service normally includes defined ownership, monitoring, incident handling, change control, reporting and continuous improvement.
Scope can include transition, asset inventory, refresh monitoring, incident and request handling, semantic-model maintenance, data-quality checks, access administration, testing, release management, documentation, service reporting, user enablement and prioritised improvements.
The service can be designed around Microsoft Power BI, Tableau, Qlik, Looker and other enterprise reporting tools, together with supporting warehouses, lakehouses, databases, gateways and integration services. Supportability is validated during discovery.
Yes. A co-managed model can divide responsibility by platform, business unit, support tier, type of work or service hours. The RACI, escalation process and acceptance criteria should be documented before transition.
It can include agreed enhancement or development capacity. Larger projects are usually separated from operational support so scope, priorities, testing and commercial treatment remain transparent.
There is no reliable fixed duration without discovery. Timing depends on estate size, platforms, documentation, access approvals, open incidents, service hours, source-system dependencies, regulatory requirements and the quality of knowledge transfer.
Measures may include refresh success, response and resolution, availability, recurring defects, data-quality exceptions, backlog ageing, release success, performance, adoption and stakeholder satisfaction. Measures should reflect what the provider can control.
Pricing is influenced by report estate size, platform and source complexity, service hours, ticket volumes, service levels, enhancement capacity, governance obligations, environments and transition effort. A written estimate can be developed after initial scoping.
The service operates within the client’s approved identity, access, classification, retention, logging, change and incident policies. Least privilege, named access, approval records and periodic reviews can be included. Legal and specialist security accountability remains with authorised parties.
Yes. Improvement work can address model design, calculations, query patterns, visuals, refresh architecture, gateway configuration, data volumes and usage. Root causes may also sit in upstream data platforms and require other teams.
Yes. Rationalisation can combine inventory, usage evidence, owner review, dependency analysis, archive controls and communication. Reports should not be retired solely because usage appears low without confirming business, regulatory and periodic needs.
Clients typically provide accountable owners, secure access, platform and architecture information, current documentation, support history, priorities, policies, source-system contacts and timely decisions. Missing evidence or delayed approvals should be recorded as service dependencies.
Yes, where scope, controls, evidence, residency, access, retention, audit and third-party obligations are explicitly defined. Requirements vary by jurisdiction and sector and should be validated by legal, compliance, privacy and security specialists.
The service can diagnose and route the issue, provide impact evidence, communicate status and coordinate recovery under agreed operational-level agreements. Resolution responsibility remains with the team that owns the failing dependency unless otherwise contracted.
Review platform competence, transition method, service governance, security controls, documentation, escalation, reporting, improvement approach, staffing resilience, commercial transparency, exit provisions and ability to work with internal teams and other vendors.
