Augmented Decision Dashboard
Transform your raw data into readable, interpreted, and commented indicators to facilitate decision-making.
A dashboard shows figures; it does not always say which ones deserve the leadership team’s attention before Monday’s meeting.
Fasfox builds systems that detect significant variations in tracked indicators, link each deviation to a cause identifiable in the underlying data, and draft commentary the finance controller reviews before it goes out.
Forty indicators on a Monday morning, and no one knows which ones matter
Every week, the leadership team receives a Power BI or Tableau dashboard with dozens of indicators: revenue by branch, service rate, margin by site, absenteeism. Working out what actually moved, and telling a normal fluctuation from something worth acting on, takes time the team does not always have before the meeting, so the same three or four indicators end up carrying the whole discussion.
A comment that repeats the number instead of explaining it
The obvious move is to ask a language model to comment on the figures. The result often just restates what the chart already showed, “revenue increased”, without linking the change to a probable cause. Worse, if the underlying data model has a scoping error or double-counts something, the commentary repeats it with the same confidence as an established fact.
A stable data model, the starting condition
This only works with a data model that is already validated, stable indicator definitions, consistent scope across branches or sites, and an agreed threshold for what counts as a significant change. Layered on top of data that is still unstable or loosely defined, a commentary layer just dresses the problem up with a veneer of credibility. Fixing the data model afterwards, once managers have started quoting the commentary in meetings, is a harder conversation than having it up front.
A read-through before the number reaches the boardroom
We build a system that flags deviations against defined thresholds, links each variation to a candidate cause identifiable in the underlying data, and drafts commentary that cites the source figures. The finance controller or operations director reads that commentary before it reaches the leadership meeting, and can discard it if the proposed cause does not hold up.