How high are our IT costs compared with others? How far along are others with AI? How expensive are comparable providers? Almost every managing director and CFO asks these questions. Reliable answers are rare, because comparison requires figures nobody likes to share.
The contradiction and how to resolve it
The answer is anonymity with rules. Companies submit their KPIs in a uniform structure. Results are only shown when a comparison group has a minimum number of participants, so that no individual value can be identified. Each participant sees their own value relative to the group, never the values of others.
The uniform structure is more than a form. Every KPI needs a fixed definition: what counts as IT costs and what does not? Without that, you are comparing apples with oranges, and the best data protection is of no use.
Does this sound familiar?
If you want to know where your company stands on costs or AI adoption, let us show you in a first conversation how anonymous comparison works.
The technology behind it
Snowflake provides the right building blocks: roles and row-level security separate a company's own data from group values, and analysis runs in a secured environment. The minimum group size is enforced as a rule in the platform, not as an agreement. If a group falls below the threshold, the query returns no value. Statistical offices have worked on the same principle for years.
On this basis we are developing a platform that starts with two topics where comparison is in particularly high demand: AI adoption and provider cost comparison. The foundation is the same Data Platform we build for individual companies.
Three takeaways
- Comparison requires trust. Securing anonymity technically gets you data nobody would otherwise share.
- Minimum group size is the most important rule. It prevents an average from turning into an individual value.
- Start with one topic. Two clearly defined KPI areas deliver value faster than comprehensive reporting.