What a dashboard is for
A business intelligence dashboard brings together on one screen the indicators management and teams need to make decisions: sales, margins, inventory, collections, service times, productivity. Its value is not in the charts but in the numbers being correct, up to date and meaning the same to everyone. A dashboard with questionable data creates more arguments than decisions.
The invisible work: the data
Most of a business intelligence project happens before the dashboard. Data must be pulled from each system (ERP, CRM, online store, spreadsheets, service platforms), cleaned, and customers and products that appear under different names must be unified. Each indicator must be defined precisely: what counts as a sale, when a customer is active, how margin is calculated. That definition is documented and visible in the dashboard itself.
What it includes
- Automatic connection to data sources and scheduled refresh.
- A single data model with documented definitions for every indicator.
- Dashboards by department and level: management, sales, operations, finance.
- Alerts when an indicator leaves its expected range.
- Permissions so each person sees what is relevant to them.
Common indicators by department
- Sales: inquiries received, conversion by stage, average order value, sales cycle and sales by channel.
- Operations: orders in progress, delivery times, stockouts and productivity.
- Finance: billing, collections, overdue receivables, days sales outstanding and margin by product or customer.
- Customer service: first response time, resolved cases, complaints and satisfaction.
What AI adds
- Natural-language questions: for example, which customers bought less this quarter than last. The answer shows how it was calculated.
- Change detection: alerts when something behaves differently from usual, even if nobody is looking.
- Summaries: a periodic written report with the most relevant changes and possible causes, marked as hypotheses.
AI does not replace precise indicator definitions: it builds on them. If the underlying data is wrong, so are the answers.
Common mistakes
- Too many indicators: if everything is important, nothing is.
- Indicators without a written definition, which each department calculates its own way.
- Data exported by hand every week, which stops updating when the person who loaded it leaves.
- Indicators without an owner: nobody decides what to do when a number gets worse.
Which tool
If the company already uses a business intelligence tool such as Power BI, we feed it with clean, integrated data. If not, a dashboard can be built inside its own systems. The choice depends on licence costs, who will use it and where the data must stay.
How the work is done
- Define the decisions the dashboard must support and the indicators behind them.
- Connect and validate data sources.
- Build a first version with a few well-defined indicators.
- Use it with the people responsible, adjust and expand.
How much dashboard development costs
It depends on the number of data sources, their quality and the indicators. Integrating and cleaning data is usually the largest part of the work. We quote after reviewing the sources.