What does a BI tool actually need to do?

Power BI, Tableau, Qlik and Data Studio have different interfaces and different strengths, but they are all trying to solve broadly the same problem. They help you move from raw business data to analysis that can be understood and used by other people.

That simple workflow is a better starting point than comparing hundreds of individual features. Once you know what your own version of that workflow looks like, the useful differences between tools become much easier to identify.

1. Start with your data

The first question is whether the tool can connect easily to the data you actually use. That might be Excel or CSV files, a relational database, a cloud data warehouse, a web application or some combination of these.

Most established BI platforms support a wide range of common sources, so this may not distinguish them very much. It becomes important when you rely on a less common system, need a particular connector, or have restrictions around where data is stored and how it can be accessed.

Do not judge this from a generic list of connector logos alone. Check the specific connection you need and, where possible, try it. A connector being available is not quite the same thing as it working conveniently with your data and your organisation's setup.

2. Data preparation and modelling

Once the data is connected, you need to be able to get it into a form that is useful for analysis. In practice, that can mean changing data types, cleaning values, creating fields, combining data, defining relationships and structuring the model behind the report.

It makes sense to consider preparation and modelling together because they are both part of getting from source data to something you can analyse properly. The amount you need will vary enormously. A simple reporting job may involve one clean table; a more complex BI project may involve several sources and a much more deliberate model.

The question is therefore not which platform has the most sophisticated modelling capability. It is whether the platform gives you enough control for the work you expect to do, without making straightforward tasks unnecessarily difficult.

3. Analysis and calculations

BI is not just about displaying data. You will normally need to create calculations that turn the underlying values into useful business measures: percentages, ratios, variances, running totals, period comparisons and KPIs, for example.

Different platforms use different calculation languages and approaches. That creates a learning curve whichever tool you choose. If your requirements are fairly standard, the major platforms will generally give you what you need. If your analysis requires more sophisticated calculations, it is worth testing those specific requirements before committing.

4. Visualisation and dashboard building

This is the part of BI tools that people see most often, and it is where personal preference can matter. You need to be able to build the charts, tables, filters and dashboard layouts that suit the information you are presenting.

Again, there is a large amount of overlap between the major platforms. They may take different routes to the same result, and some make particular types of formatting or interaction easier than others, but all are capable of producing professional business dashboards.

The best test is not to compare galleries of impressive example dashboards. Build something representative of your own work and see how easily you can control the layout, formatting and interactions that matter to you.

5. Sharing

Once a dashboard has been built, you need to establish how it is going to reach the people who need it. This is one of the areas where BI tools can be less straightforward than they first appear, because publishing and sharing models differ between platforms.

Think about how reports need to be accessed. A dashboard intended only for colleagues inside the same organisation presents a different set of requirements from one that needs to be shared with clients or embedded inside another website or application.

Licensing can also affect sharing. Some platforms require particular licences for creators, viewers or both. Others make public or embedded sharing easier. If mobile access matters, test the dashboard on the devices people will actually use rather than assuming the desktop experience will translate perfectly.

The important point is that sharing should not be treated as something to think about after the dashboard has been built. It is part of the complete BI workflow and should be tested before you commit to a platform.

6. Cost

Cost is clearly part of the decision, but BI pricing is not always as simple as comparing the monthly figure shown on a vendor's website. Different platforms use different licensing models, and the total cost can depend on how many people create reports, how many people need to view them and which sharing or deployment options you require.

A platform that looks inexpensive for one analyst can become a different proposition when reports need to be distributed across a larger organisation. Equally, paying for a more expensive platform makes little sense if the additional capabilities are not relevant to your work.

Work out the likely cost for your real situation: creators, viewers, required services and the way reports will be distributed. That gives you a much more useful comparison than the headline licence price.

So which BI tool should you choose?

If you are choosing for an organisation, the existing technology environment may narrow the decision quickly. A company heavily invested in Microsoft technology may find Power BI a natural fit. Another organisation may already have Tableau or Qlik skills, infrastructure and licences in place. Google-centric environments may find Data Studio particularly convenient for the reporting they need.

If you are choosing a tool to learn, the decision is slightly different. Look at the tools used by the employers or clients you are interested in, but do not become obsessed with finding the one perfect choice. Whichever serious BI tool you learn first, you will have to understand the same underlying ideas: data, analysis, calculations, visualisation and sharing.

That knowledge transfers. Interfaces and syntax change, but the underlying BI work does not suddenly become a different profession when you open another platform.

Shortlist the realistic options, run the same practical test in each, and make the decision from there. That will tell you far more than another hour spent reading feature comparison tables.