Making Leon Data Actionable with Grafana: From Exports to a Reliable Management View
- Oliver Groht
- Jun 2
- 5 min read
Many charter and business aviation operators work with Leon every day and therefore have a wealth of operational flight data at their fingertips. Yet management steering often remains surprisingly fuzzy: profitability per aircraft, empty-leg share, utilisation, or liquidity are then decided more “from experience” than from a robust, shared view.
The key point: “Data available” does not automatically mean “manageable.” Manageability only emerges when operational and commercial perspectives converge into clearly defined KPIs—reproducible, traceable, and updated on a regular cadence.
In this article, we show which questions standard exports typically leave unanswered, why Excel reporting hits limits in practice, and how a pragmatic setup combining Leon, a central data foundation, and Grafana enables a shared steering view for operations and finance.
Why reporting via exports often doesn’t translate into steering
Leon reflects operational reality very well: legs, trip types, block and flight times, fuel, distances, and much more. What is often missing is the step from operational detail data to management decisions.
Across many organisations, we see a recurring pattern:
Exports are pulled manually
Excel logic grows “over time”
Definitions are not documented
Numbers are hard to reproduce
The consequence for decision-makers: discussions are not about actions (“What do we do?”) but about the data basis (“Which number is correct?”). This is exactly where the approach of making Leon data actionable with Grafana comes in: a shared, reliable view across operations and finance.
Which steering questions standard exports often cannot answer cleanly
Standard exports are useful—but rarely structured in a way that produces reliable steering KPIs without additional logic. Typical questions from managing directors and owners include:
Profitability & unit economics
Revenue per flight hour (overall and per aircraft)
Comparability across periods and aircraft
Utilisation & efficiency
Ratio of flight time to block time as an indicator of operational overhead
Classification of turnaround, handling, positioning
Empty-leg / ferry share
What is it really?
What is the economic impact (e.g., lost contribution margin as a steering metric)?
Forecast & order book
Future bookings by legs, block time, expected revenue
Liquidity
Open and overdue invoices as a prioritised management view (not just a list)
Customer and network perspective
Top customers, route/country profiles as a basis for commercial decisions
The value comes from answering these questions consistently, transparently, and repeatably—not “somehow.”
The core hurdle: operations and finance follow different logics
Operational data (Leon) and commercial data (accounting/controlling) can both be correct—but they follow different time references and levels of granularity. A classic example:
Revenue by sell date vs.
Revenue by realisation date
Both views are legitimate. But without clear definitions, avoidable friction arises. “Export + pivot” often fails here because:
Definitions become implicit and inconsistent
Versioning is missing (“Which file is the right one?”)
Changes are not traceable (“Why is the number different today?”)
What leadership teams need is a single source of truth: a central data foundation, documented KPI definitions, and visible data freshness.
Practical setup: Leon → central data foundation → Grafana
The principle is deliberately simple: make data reliable first, then visualise. A dashboard is only as good as the logic underneath.
A pragmatic setup consists of four building blocks:
1) Extraction - Leon data is extracted via leon2db (an in-house development by Arkcanis Consulting GmbH).
2) Centralisation - Load into a central PostgreSQL database. - Each import receives a timestamp so freshness is transparent.
3) Integration of commercial data - Enrich with accounting/controlling data (e.g., P&L metrics such as revenue, cost of materials, EBITDA), depending on structure—for example by cost centre or aircraft.
4) Visualisation & steering - Grafana as a management dashboard with filters (time period, aircraft, route, etc.) and year-over-year comparison.
Result: operations and finance work from a shared factual basis—without constant switching between files, tools, and definitions.
KPI modules that support decisions (instead of just “looking nice”)
A good dashboard is not a collection point; it is a set of modules that make concrete decisions easier. Proven modules include:
1) Revenue view with clear time logic
Revenue by sell date (sales performance, closing dynamics)
Revenue by realisation date (operational delivery, period-based steering)
Important: make definitions visible in the dashboard (short, unambiguous).
2) Unit economics & utilisation per aircraft
Revenue per flight hour (overall and per aircraft)
Comparison across time, fleet, and mission profiles
Benefit for decision-makers: discussions about pricing, fleet deployment, and service mix become fact-based.
3) Operations mix by trip type
Example classification of legs:
PAX
Cargo
Technical
Training
Ambulance
Owner
Benefit: utilisation is interpreted correctly—not every flight hour is commercially comparable.
4) Empty-leg / ferry focus with economic impact
Make empty-leg share transparent
Add a steering metric such as “potential lost profit” (as an approximation/estimate, methodically well-defined)
Benefit: “too many empty legs” turns into a concrete optimisation question (where, why, which patterns, which measures).
5) Efficiency and cost-driver perspective
Ratio of flight time to block time
Fuel consumption (total, per flight hour)
Distances
Benefit: comparability and trend steering without overloading leaders with operational detail.
6) Demand & cash for planning confidence
Future bookings / order book
Open and overdue invoices as a liquidity cockpit
Top customers and network profiles (airports/countries)
Benefit: planning, liquidity, and commercial decisions reinforce each other.
Business value: what concretely improves for decision-makers
The benefit does not come from “more data,” but from less friction and better decisions:
Executive management
Profitability per aircraft/customer becomes visible
better decisions on pricing, fleet, and service mix
Operations
empty legs become quantifiable
planning and positioning become more targeted
Finance
liquidity steering becomes prioritised (instead of maintaining lists)
less reconciliation effort due to clear definitions
Across the company
a shared KPI language reduces debates about numbers
regular reviews become routine (instead of “Excel fire drills”)
Typical pitfalls—and pragmatic countermeasures
To turn a dashboard into a steering routine, these points should be addressed early:
1) Ambiguous KPI definitions - Countermeasure: a lean reporting guide (e.g., in Confluence); definitions also visible directly in the dashboard.
2) Inconsistent data quality - Countermeasure: mapping/normalisation in data preparation; clear rules for exceptions.
3) “Real time” is overestimated - Countermeasure: a sensible import cycle plus a visible import timestamp.
4) Missing traceability (audit/changes) - Countermeasure: versioning of dashboard and logic; documented changes.
5) Lack of adoption - Countermeasure: start with a small set of decision-relevant modules and expand iteratively together.
A 3-stage approach: from first dashboard to a steering routine
Stage 1: Target picture & top questions
Which 5 decisions should become faster/better? (e.g., profitability, empty legs, cash, forecast)
Define KPI definitions
Stage 2: Data foundation
Leon extraction, central data foundation, plausibility checks
Stage 3: Dashboard & operations
Grafana views, filters, year-over-year comparison
governance + fixed review routine (weekly/monthly)
Conclusion: steering comes from clear definitions and a shared view
Leon data exists in many operators—the decisive step is to move it from isolated exports into a reproducible steering view. With a central data foundation and Grafana, operations and finance perspectives can be connected so that profitability, empty-leg share, cash, and forecast become reliably manageable.
Next step (practical)
Define your internal top 5 steering questions and then check:
Which data is available today but not consistently defined?
Which KPIs are not reproducible (Excel logic, manual steps)?
Where are time logic and responsibilities missing?
If you want, Arkcanis can support you in building a reliable KPI and data foundation—from the first management dashboard to an established review routine.
More about our service area Data Pipelines & ETL: https://www.arkcanis.com/arkcanis-consulting-gmbh/consulting/datapipelines-etl
About Arkcanis Consulting
Arkcanis Consulting GmbH is the specialized advisory unit of the Arkcanis Group. We design scalable process and data architectures for airlines, AOCs, operators, and technology-driven organizations — with a clear focus on aviation engineering, Leon integrations, Atlassian architectures, ETL pipelines, and real-time dashboards.
As the founder of catworkx GmbH — one of the largest Atlassian partners in the DACH region — Oliver Groht brings more than 25 years of experience in Jira and Confluence architecture, process consulting, and enterprise-wide scaling. He combines this background with deep technical expertise in Leon GraphQL, data engineering, Grafana, and Flight Ops workflows.
The result: measurable, transparent, and resilient structures that enable operational excellence and strengthen strategic decision-making at the management and C-level.
