At some point, every organization needs to present its data and metrics in a structured manner. The process of requesting the IT team to manually extract data, dump it into Excel, and create charts is not scalable. For larger organizations with more data, there’s a growing demand for accessibility and handling large volumes. The process must be automated, run stably, and often involve combining data from different tables or even multiple sources.

As a result, it becomes crucial to choose a solution — a reporting tool where you can craft dashboards, visualizations, and set up reports that serve as the one-source-of-truth. With a plethora of tools available, making a choice is no easy task. Additionally, much of the software tends to be relatively costly. Once you commit to a specific tool, switching becomes a cumbersome process. In other words, it’s essential to make the right choice from the outset.

🧭 What to consider

The first step is to be clear about your expectations and goals. Addressing these questions will help:

  • How do you envision your organization using the tool?
    Will business users engage in self-service exploration, or mostly consume pre-defined reports?

  • How is your data structured?
    Do you need heavy aggregations, table joins, or multi-warehouse setups? Or is it a simpler table structure?

  • Who will maintain it?
    Regardless of tool, someone needs to keep things running — whether it’s a consultant or internal team.

🧰 A few tools worth mentioning

Power BI

Microsoft’s BI solution integrates nicely with the Office Suite — its biggest selling point. It offers strong data modeling (tabular models), many connectors, and powerful capabilities. However, the dashboard-building experience isn’t the smoothest, and the visuals can feel a bit dated. Also, to go deeper, you need to learn DAX — a rather unintuitive formula language.

Tableau

A powerhouse in the reporting world. Known for its visual flexibility and sleek dashboards, Tableau handles big datasets well and allows for extensive custom metrics. On the downside, the web interface can feel clunky, and the price tag can be steep for smaller companies.

Looker

Owned by Google, Looker is built around a central modeling layer using LookML. It ensures data consistency and enables robust self-service reporting. Best suited for mature organizations with strong data culture. It’s fully browser-based, version-controlled, and great for data teams — but probably overkill for simpler needs.

Looker Studio (formerly Google Data Studio)

Despite the name, Looker Studio is a very different product from Looker. It’s free, user-friendly, and tightly integrated with Google Analytics — great for ad-hoc dashboards. However, it falls short on advanced features like metric building and joins.

Redash (and similar layovers)

These are nice for a quick fix, but they do lack in flexibility and how to set up things.

Others: Qlik, Spotfire, SAS, SAP…

There are plenty of other tools out there. Many are backed by large enterprises, offer stability, and integrate well with specific data environments. That said, they usually have lower market share, fewer connectors, and the visual quality often feels quite basic.

Open-source alternatives

If your needs are minimal and stable, coding your own dashboards in Python, R, with frameworks like R-Shiny or Plotly can be a great path. This gives you control and avoids licensing costs — as long as you have (or can hire) the skills. These tools are widely used and supported, so you’re not locking yourself in. Metabase is another interesting reporting tool, browser based and good for internal reporting - still lacks a bit in interactivity compared to Plotly Dash.

Final note: There’s no perfect tool — just the right one for your team, goals, and budget. The most important thing is to choose intentionally and align the choice with your long-term data strategy.