At one of the world's leading multinational technology companies, I worked as a data analyst in the Central Planning department. My main responsibility was managing operational reporting: the dashboards that the planning team used every day to make decisions about resource allocation, project prioritization, and execution tracking.
The context: centralized planning at a multinational
Central Planning at this company coordinates project execution globally. That means consolidating data from multiple teams, regions, and systems into a coherent view that allows answering questions like: Are we meeting quarterly targets? Where are the deviations? What resources do we need to reallocate? Data came from SAP, regional team spreadsheets, project management tools, and ad-hoc sources that changed depending on the quarter.
The dashboards: designed for decisions, not decoration
I designed and maintained a suite of dashboards in Power BI covering three levels of detail. The first, the executive dashboard, showed accumulated execution evolution versus planned targets — the view that leadership reviewed weekly. The second, the operational dashboard, allowed the planning team to drill down by region, by project type, by period, and identify where the gaps were. The third, the detail dashboard, reached the level of individual projects for specific analysis.
The important thing wasn't making pretty dashboards — it was making them useful. Every metric was there because someone needed it to make a concrete decision. If a KPI didn't generate action, it didn't make sense to show it.
The data model: the invisible part that sustains everything
Most of the work wasn't visual — it was the data model behind it. Consolidating disparate sources, handling different granularities (daily data from some systems, weekly from others, monthly from still others), resolving nomenclature conflicts between regions, and building a dimensional model that allowed the cross-cuts the team needed without the dashboard taking 30 seconds to load.
What I learned: data is a means, not an end
That experience is where I understood that the value of data isn't in the data itself, but in the decision it allows you to make. A sophisticated dashboard that no one uses is worthless. A simple dashboard that the team reviews every day and makes concrete decisions with — that's worth it. That mindset — building for use, not for impression — is what I apply today to every data project.