Having industry or specific domain expertise is always better when dealing with B2B customers. You are able to understand their problems and speak their language. There might be circumstances where not having domain expertise might help bring out of the box thinking or solutions, but for the most part, it is safe to say that having domain expertise is better than not having it.
So, is domain expertise or industry specific knowledge required to provide predictive analytics solutions? I would venture out that it is not required, but always nice to have. In traditional consulting, you need to be an expert in their industry and processes, but for predictive analytics domain expertise is not as important.
Clients ask us if we have prior experience in their industry or specific machinery. In some cases we do, but many times we do not. We find that the level of expertise or knowledge we need to gain is specific to the machine or asset we are analyzing in order to predict failures. Having general industry knowledge would not be much help other than being able to speak the same lingo and understand acronyms in their operations.
We tackle a predictive analytics problem by letting the data do most of the talking. We certainly want and need to understand the physical behavior of the asset, but this usually takes a few phone calls to understand at a high level their function and clarify anomalies in the data.
The science and tools we use to predict failures in industrial assets place a large emphasis on the data and not so much the human operational side of the business.
If you disagree, feel free to let us know why.