Data strategy
- Does the tool fit the business vision?
- Does the tool help optimize key business values?
- Does it make sense to have long-term access to raw data?
Integrations
- What other tools should access this data?
- How complicated are the integrations?
- Are existing interfaces regularly updated?
Data protection
- How important is the issue of data protection?
- Should data be stored within the EU?
- Which compliance guidelines apply?
Functionality
- What exactly does the analytics tool need to do?
- Which features are particularly important?
- Do users like the product?
Usability
- How comprehensible is the tool?
- Is sufficient access possible with mobile devices?
- How fast is the user interface?
- Do the prospective users like the product?
Onboarding and maintenance
- Are there in-house users with experience?
- How good are the documentation and support?
- Can other specialists be called upon if necessary?
License model
- What budget is available?
- Should the application be hosted on its own server (“On-Premises”)? If yes, who will take care of the administration?
- Is a SaaS product more suitable?
Tip and conclusion
My tip: don’t fall in love with any tool too quickly. Especially not on the basis of advertising. It is better to take an academic approach and create a weighted and evaluated decision matrix with all relevant features. Differentiate between must-haves and nice-to-haves.
And very important: test your favorite in parallel operation for a while! All tool providers allow a free trial.