Business cases of individual projects are collected and structured to improve forecasting accuracy and reliability. For each case, FLIGHTMAP has an explicit data quality indicator. A wide set of rules is available to perform sanity checks, for both the case owners as well as the decision-makers.
2. Built-in data quality visibility from the start
Business cases of individual projects are collected and structured to improve forecasting
accuracy and reliability. For each case, FLIGHTMAP has an explicit data quality indicator. A
wide set of rules is available to perform sanity checks, for both the case owners as well as
the decision-makers.
By making this data quality visible, decision-makers can focus on the right information.
3. Step-wise improvement through workflow
A systematic and explicit project and portfolio workflow drives data quality improvement.
FLIGHTMAP’s versioning mechanism supports incremental capturing of new insights, and
comments and alerts support the required dialogue. The most important improvement areas
can be identified with a sensitivity analysis.
Decision-makers can therefore easily track improvement of data quality.
4. High data quality through expert analysis
Beyond a project team’s own input, FLIGHTMAP supports better data quality by involving
analysts and experts. With different experts that agree on the main business case drivers,
data quality levels go up. Where they disagree about forecasts, scenario’s will reveal the
impact of their different views.
Together, all data quality features support better decision making, to create more value.