Side-by-side
| Dimension | Decision Intelligence | Operating Intelligence |
|---|---|---|
| Primary buyer | Head of analytics, chief data officer | Operator (COO, founder) |
| User | Data analyst | Operator directly |
| Approach | Augment analyst decisioning with AI + causal modeling | Remove the analyst translation layer |
| Domain | Horizontal — apply to any data | Vertical — pre-modeled for the operating systems |
| Implementation effort | High; requires modeling investment | Low; the model ships with the product |
| Flexibility | High — any question, if modeled | Bounded — the operating questions, immediately |
| Examples | Pyramid, Tellius, Quantellia | Fairview |
The trade-off in one line
Decision intelligence is more flexible and slower. Operating intelligence is narrower and immediate. Neither is a better product; they are bets on different constraints.
If your constraint is that your analysts are good but overloaded, decision intelligence raises their throughput. If your constraint is that you have no analysts and the decision is due Thursday, throughput is not the problem — the translation layer is, and the answer is a system that ships with the model already built for CRM, billing, ads, ecommerce and accounting data.
How to tell which constraint you have
Count the people between the question and the answer. Zero means you are already fine. One or two, and they have capacity, means decision intelligence will help them go faster. One or two, and they are the bottleneck for every question, means adding tooling for them does not help the operator waiting in the queue. None at all means the horizontal, model-it-yourself approach has nobody to do the modeling — which is the situation most companies between $1M and $50M are actually in.
The second question is how novel your questions are. Genuinely novel, causal, one-off questions favour decision intelligence. Recurring operating questions — which channel is below margin, which deals are slipping, what is the projection against plan — are the same questions every week, and pre-modeling them is the whole point of operating intelligence.
Why they coexist
Enterprises with dedicated analytics functions buy decision intelligence to make those analysts more effective. Mid-market operators without analytics teams buy operating intelligence so they do not have to hire one. The categories serve different organisational stages, and a company that grows into an analytics team will plausibly end up with both.
Be honest about which stage you are at rather than which one you would like to be at. Buying the horizontal platform before you have anyone to model with it is the most common way this decision goes wrong — the licence renews, the model never gets built, and the operator is still in the spreadsheet.
What Fairview does and does not do
Fairview pre-models the operating systems of record and returns named next actions with an estimated revenue impact through the Next-Best Action Engine, reading from the Operating Dashboard. Setup is OAuth, read-only, 2–5 minutes per source.
What it does not do: arbitrary causal modeling, custom statistical work, or analysis over data outside the operating stack. If that is the requirement, a decision intelligence platform with an analyst behind it is the right purchase and we would rather say so here than waste your evaluation cycle. Our comparison policy is at editorial standards.