See the Trend. Understand the Context.
Siteplore turns connected operational time-series into dashboards, comparisons and analytical workflows that help engineers and operational teams understand what changed, when it changed, how assets or sites compare, and what deserves further investigation.
Having data does not automatically mean understanding performance.
Operational systems may collect thousands of measurements, yet teams can still spend hours exporting spreadsheets, switching between systems or manually comparing trends before reaching a useful engineering conclusion.
Too Many Separate Trends
Related measurements may exist in different dashboards or systems, making context difficult to see.
Current Value Without History
A value can look acceptable today while a longer-term trend shows meaningful deterioration.
No Operational Context
Changes in load, production, equipment state or environment can alter how a measurement should be interpreted.
Difficult Comparison
Similar assets, areas or sites may perform differently without an easy way to compare them.
Event Without Before & After
Investigation becomes harder when the operating trend around an event is not readily available.
Dashboard Without Decision
Attractive visualization creates little value unless it supports a real operational or engineering question.
Analytics should help answer an operational question — not just display a metric.
What Changed?
Identify when a measurement, condition or performance pattern began to change.
Is It Normal?
Compare current behavior with relevant historical patterns or operating ranges.
What Happened at the Same Time?
Compare related signals to understand operational context.
Which Asset Performs Differently?
Compare similar assets under relevant operating conditions.
Is Performance Improving?
Evaluate changes after maintenance, operational adjustment or improvement activity.
What Deserves Investigation?
Prioritize meaningful deviations instead of manually reviewing every available signal.
Turn historical measurements into useful operational comparisons.
Trend Analysis
Review how a selected measurement changes over time.
Asset Comparison
Compare similar machines, feeders, stations or other monitored assets.
Period Comparison
Compare today, previous shifts, weeks or other relevant operating periods.
Multi-Signal Analysis
Place related measurements on the same analytical view to evaluate coincident changes.
Event Analysis
Review conditions before, during and after a selected monitoring event.
KPI Monitoring
Present selected indicators where the KPI definition is meaningful to the use case.
Load & State Analysis
Interpret metrics against equipment load or operating state where available.
Area / Site Comparison
Compare selected metrics across different operating areas or sites.
Availability Analysis
Review missing, delayed or incomplete data that may affect interpretation.
Analytics starts before the dashboard.
Useful visualization depends on credible measurement, structured time-series data, contextual metadata and an analytical question worth answering.
Field Data
Sensors, meters and operational systems provide the source data.
Time-Series Data
Measurements are organized with useful timestamps and metadata.
Dashboard
Present KPIs, trends and related context.
Compare & Investigate
Explore patterns, relationships and events.
Operational Action
Inspect, prioritize, maintain or improve based on evidence.
A useful dashboard reduces the time needed to understand the operation.
Start With the User
Operators, maintenance teams, engineers and managers do not necessarily need the same dashboard.
Show What Matters First
Critical context should be easier to find than secondary information.
Preserve Engineering Units
Units, ranges and measurement meaning should remain clear.
Provide Historical Context
A current number should often be accompanied by relevant historical behavior.
Reduce Visual Noise
More panels do not automatically provide more operational insight.
Enable Drill-Down
Move from site-level context toward specific areas, assets or measurements.
From “something changed” to a structured investigation.
Siteplore dashboards can support a repeatable engineering workflow rather than relying on isolated screenshots or one-time observations.
Move beyond historical charts when the data foundation is ready.
The Grafana Cloud environment used by Siteplore can provide additional analytical capabilities where they are supported by the customer’s stack, data source, plan and configuration.
Investigations
Use structured investigation workflows to explore meaningful monitoring changes and related context.
Metric Forecasts
Forecast suitable time-series metrics to provide additional future context, anomaly-detection support or capacity-planning insight.
Outlier Detection
Identify unusual behavior relative to suitable analytical references where configured.
Sift Investigations
Where available, automated investigative workflows can help surface relevant signals for further engineering review.
AI Assistant
Available assistant capabilities can support exploration, search and analysis of monitored information.
Alerts & Investigations
Combine selected monitoring events with historical and analytical context to support faster investigation.
Automations & Watchers
Available workflow capabilities can support repeated monitoring and follow-up processes where suitable.
Search
Search capabilities can help users navigate monitoring resources and related information.
SLO & Service Context
Where relevant, selected service-level concepts can support availability and performance monitoring objectives.
Advanced analytics availability depends on the selected Grafana Cloud plan, customer stack, compatible data source, historical data and project configuration. Forecasting, anomaly detection and AI-assisted analysis should not be interpreted as guaranteed prediction or automatic engineering diagnosis.
Better algorithms cannot compensate for poor measurement context.
Reliable Measurement
Sensors and instruments must provide credible information about the physical condition.
Correct Timestamp
Time alignment matters when comparing events across multiple signals.
Consistent Units
Engineering units and scaling should remain consistent and understandable.
Asset Metadata
Site, area, asset and device identity help preserve data meaning.
Operating Context
Load, state, production or environmental context can change interpretation.
Data Availability
Missing or delayed data should be understood before drawing conclusions.
Different operational questions require different analytical views.
Energy Performance Analysis
Compare selected energy consumption across periods, areas or loads.
→ Compare
→ Investigate Change
Asset Trend Investigation
Review condition trends and operating context around a developing change.
→ Historical Trend
→ Engineering Review
Utility Performance
Compare selected utility measurements with operational demand.
→ Context
→ Performance Review
Environmental Trend Analysis
Review environmental patterns across time or monitoring stations.
→ Trend
→ Compare
Before / After Maintenance
Compare equipment behavior before and after selected maintenance activity.
→ Maintenance Event
→ Performance Comparison
Site Benchmarking
Compare selected standardized metrics across multiple sites.
→ Standard Metric
→ Comparison
One analytics layer can provide context across different monitoring domains.
RekaSense
Analyze compatible environmental, water, agriculture and process time-series measurements.
Explore RekaSense →PowerWatch
Analyze selected electrical and energy trends, comparisons and operating context.
Explore PowerWatch →MachineGuard
Review equipment condition history, trends and selected operating context.
Explore MachineGuard →Build dashboards around the decisions users need to make.
Exact deliverables depend on available data, customer requirements, monitoring scope and the selected Siteplore engagement.
Analytics Requirement Definition
Define the operational or engineering questions the dashboard should support.
Dashboard Architecture
Organize site, area, asset and detailed analytical views.
Historical Trend Views
Relevant time-series visualizations for selected metrics.
Comparison Views
Asset, period, area or site comparison where appropriate.
KPI Definition
Selected calculated indicators where their engineering meaning is clearly defined.
Event Context Views
Historical context around selected monitoring events.
Advanced Analytics Configuration
Suitable advanced capabilities where supported by the deployment and data.
Training & Handover
Explain how users should navigate and interpret configured analytical views.
Start with visibility. Increase analytical sophistication as the data earns your confidence.
Visualize
Establish reliable dashboards and historical trends.
Compare
Compare periods, assets and operating conditions.
Investigate
Combine related signals around meaningful changes.
Model
Evaluate forecasting or outlier analytics where appropriate.
Improve
Use evidence to improve operational and maintenance decisions.
Analytics supports engineering judgment. It does not replace it.
Siteplore provides historical visibility, comparison and analytical context. Advanced methods can help identify patterns, but conclusions still depend on data quality, application knowledge and appropriate verification.
| Capability | Siteplore Dashboards & Analytics | Important Boundary |
|---|---|---|
| Historical visualization | Yes | Depends on available historical data |
| Asset / period comparison | Yes | Requires comparable data and context |
| Calculated KPIs | Where defined | Formula and engineering meaning must be valid |
| Event investigation | Yes | Limited by available measurements |
| Outlier detection | Potentially | Requires suitable data and configured capability |
| Metric forecasting | Potentially | Forecast quality depends on time-series behavior |
| AI-assisted analysis | Where available | Not a verified engineering diagnosis by itself |
| Guaranteed failure prediction | No | Requires validated domain-specific predictive model |
| Guaranteed remaining useful life | No | Not claimed without validated RUL methodology |
| Automatic root-cause conclusion | No | Further engineering investigation remains necessary |
| Automatic process control | No | Dedicated control engineering required |
Define what the dashboard should help your team understand.
Site Assessment
For organizations that need to define measurements, KPIs and analytical questions before building dashboards.
- Operational problem review
- Data-source review
- Measurement assessment
- Analytics requirement definition
- Recommended monitoring architecture
90-Day Paid Pilot
Validate a focused dashboard and analytical workflow using selected operational data.
- Selected data sources
- Historical dashboard
- Comparison views
- Analytical investigation workflow
- Pilot findings & scale-up recommendation
Siteplore Deployment
For organizations that need multiple dashboards, sites or analytical domains in one monitoring architecture.
- Dashboard hierarchy
- Multiple monitored domains
- Historical analytics
- Customer Grafana environment
- Training & support scope
Dashboards & Analytics FAQ
What is the difference between a dashboard and analytics?
A dashboard presents information. Analytics goes further by comparing, contextualizing and investigating data to answer a specific operational or engineering question.
Does Siteplore use Grafana Cloud for dashboards?
Grafana Cloud can provide the visualization and analytical interface within the Siteplore architecture. Siteplore also includes the field-data, edge, time-series and engineering layers required to make those dashboards useful.
Where is the historical data stored?
Siteplore can use a configured cloud time-series platform such as InfluxDB Cloud for measurement history, with Grafana Cloud acting as the monitoring and analytical interface.
Can dashboards show real-time and historical data together?
Yes, where the data architecture supports it. Current-state information can be shown together with historical context to make interpretation easier.
Can users compare two machines?
Yes, where comparable measurements exist for both machines. Differences should still be interpreted in the context of load, service and operating condition.
Can Siteplore compare multiple sites?
Yes, where multi-site monitoring and standardized metrics are part of the deployment architecture.
Can dashboards calculate KPIs?
Calculated indicators can be configured where the required source data, formula and engineering definition are available.
Can dashboards show maintenance events?
Maintenance or operating-event context can potentially be incorporated where the relevant information is available and integrated.
Can Siteplore forecast future trends?
Grafana Cloud Metric Forecast capabilities can be evaluated for suitable time-series metrics where available within the customer stack. Forecasting provides analytical context and should not be treated as a guaranteed future outcome.
Can Siteplore automatically detect unusual data?
Outlier-detection capabilities can be evaluated for suitable metrics. Performance depends on the quality and behavior of the underlying data.
Can AI determine the root cause of a machine problem?
AI-assisted analysis can support exploration and investigation where available, but its output should not automatically be treated as a verified engineering root cause.
Does MachineGuard predict remaining useful life?
Remaining useful life should not be claimed unless a validated RUL model has been developed for the relevant asset, failure mode and available data.
Can Siteplore replace Excel analysis?
Siteplore can reduce repeated manual extraction and comparison for monitoring use cases, but specialized engineering studies may still require separate analytical tools.
Can customers build their own dashboards?
The ability to modify dashboards depends on the access role and customer monitoring architecture configured for the deployment.
Does every customer receive advanced AI and machine-learning features?
No. Advanced capabilities depend on the Grafana Cloud plan, customer stack, compatible data source, available history and project configuration.
What data is needed before advanced analytics is useful?
The most important foundation is reliable and relevant measurement data, correct timestamps, consistent units, useful asset metadata and enough operating history for the intended analytical method.
How should we start designing a dashboard?
Start with the question the user needs to answer. Then identify the measurements, context and comparisons needed to answer it. The visualization comes after that.
A dashboard should not just show what the data says. It should help your team understand what the operation is doing.
Rekacipta can help structure the data, define meaningful analytical questions, build Siteplore dashboards and create historical comparison and investigation workflows that support better engineering decisions.