25 people flying blind on manual reporting: time for dashboards that actually drive decisions
Before
lost per person per week on manual reporting and data compilation
After
saved per person per week
saved per person per week
employees impacted
business processes modeled with the teams
In this mid-market company, every team was flying blind. Activity data lived in scattered Excel files, data models were undocumented, and reports were built by hand every week. 25 employees spent a significant share of their time compiling and formatting data, without the resulting dashboards actually supporting decisions. Doing pragmatic business intelligence in a mid-market company means running into realities that tools alone do not solve: data of uneven quality, legacy models that nobody fully understands, and business teams that have neither the time nor the method to formalize their needs.
SITUATION BEFORE
- Operational baseline
- Activity data scattered across Excel files, team by team. Reports built by hand every week. The existing dashboards did not serve decisions.
- Teams affected
- 25 employees across all teams (operations, financial control, HR, regulatory affairs).
- Hidden costs
- Time lost every week consolidating figures that nobody disputed but that everyone recalculated on their own, at the expense of the core business.
- Why it was not solved earlier.
- The difficulty of doing pragmatic BI in a mid-market company: undocumented data models, uneven data quality, business teams with neither the time nor the method to formalize their needs. Tools alone do not solve these problems.
THE STORY
OUR APPROACH
5 steps to get there
Working with the teams and modeling business processes
Working closely with the 4 business teams to understand their day-to-day work, model their processes and identify decision points. The goal was not to collect data but to understand how each team works, which decisions it makes, and which information it actually needs to make them.
Identifying KPIs and the decisions they drive
For each modeled process, identification of the key performance indicators and the decisions they must inform. This work separated the metrics that actually serve management from those that only feed reports nobody reads. Every KPI is tied to a concrete decision.
Deep dive into the data and quality analysis
An in-depth dive into the existing data and models, largely undocumented. Nobori combed through every source to analyze data quality: inconsistencies, duplicates, poorly filled fields, implicit business rules. In a mid-market company, that is the reality of pragmatic BI: before building dashboards, you have to understand what the data actually says and what it does not.
Data foundation and transformation pipelines
Centralization of the data scattered across business tools, cleaning and structuring with dbt on Azure. The transformation pipelines embed the quality rules identified during the deep dive and feed a single source of truth, reliable and up to date, on which the dashboards rely.
Building the management dashboards
Design and deployment of Power BI dashboards built from the business processes and KPIs identified with the teams. Each dashboard answers specific decisions, not a generic reporting need. All 25 employees have views that serve them every day.
RESULTS
The measurable impact
The management dashboards built with the teams freed up half a day per person per week across the 25 employees. The 4 modeled business processes now have indicators tied to concrete decisions. Manual reporting has been eliminated in favor of real-time views on a single source of truth.
Nobody uses your dashboards? Let's talk business processes, KPIs and decisions: the data comes after
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