In recent years, virtually every organization has rushed to build metrics and dashboards to measure performance. Having dashboards that show financial KPIs, process controls, quality indicators, incident tracking, or operational performance has become the norm. The concept of Business Intelligence (BI) has evolved, creating the impression that simply having dashboards enables better decisions and improved profitability from day one.

But in the race to “have dashboards,” companies often forget a crucial truth: a dashboard is only as good as the data behind it.

Today it’s relatively easy to design a good-looking dashboard—BI tools make it simple to create charts in minutes. The real challenge lies behind the visualization: preparing, consolidating, and governing data so the metrics actually make sense and can be trusted.

What things are important when creating any kind of "Business Intelligence":

Data needs to be prepared

Before showing anything, data needs to be prepared, in a custom and specific way that applies to your data and business logic. This means:

• Cleaning inconsistencies, missing values, and source errors.

• Standardizing formats across systems.

• Resolving duplicates.

• Applying and validating business rules.

Without solid cleaning and validation, any metric can be misleading. Making decisions on bad data is worse than having no data at all.

The best data is the kind you don’t have to update

You’ve probably thought at some point that inputting data manually into a spreadsheet is a waste of time, and that it's pointless because you could just fake it and nobody would notice.

To ensure information flows correctly, you need well-designed data pipelines—processes that automate extracting, transforming, and loading (ETL/ELT) data from different systems.

A good pipeline:

• Pulls raw data from multiple sources.

• Applies business logic and transformations.

• Delivers clean, analysis-ready data.

• Runs on a schedule reliably.

This ensures data is always up to date and trustworthy, removing error-prone manual work.

Measuring what's important for you

Measuring what everyone else measures isn't enough. Every organization has unique processes and goals, so it needs custom metrics.

Defining good metrics means:

• Understanding key processes.

• Identifying indicators that really add value.

• Aligning them with strategic goals.

• Documenting them clearly so everyone understands them the same way.

Dealing with different systems, data types, and granularities

Most organizations don't rely on just one system. Finance, production, logistics, CRM, and quality often live on separate platforms.

Orchestrating data means:

• Connecting these sources reliably.

• Resolving differences in formats and granularity.

• Ensuring synchronization and consistency.

Without integration, there’s no single, unified view of the business.

Conclusions

A flashy dashboard solves nothing on its own. To deliver real decision-making power, you need to invest in:

• Building robust data pipelines.

• Cleaning and validating data properly.

• Defining custom, business-aligned metrics.

• Orchestrating data across different systems.

Only then can you ensure information flows correctly throughout the organization—and that dashboards become more than pretty charts. They become a true tool for informed decision-making.