Too often I see data product development starting from drafting data flows and technical architecture for the solution. That should be the last thing to do before deciding if the product idea is good enough.
The first step is that you need to evaluate the business value of your possible data product. Does it make any sense? What kind of problem does it solve? Who are the customers? Is the market mature enough for it? These are just example questions and you should craft your business viability evaluation questions based on your needs.
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The second step is to evaluate if you are allowed to do it legally. You might have a great idea but one of the data sources you will need to use is licensed in a way that it’s just not possible. Or then the local legislation prohibits you to use the data for that specific purpose. Here the lawyers often kill otherwise sound business plans and prevent you to make heavy losses in court. The solution might also be that we just decide to find a workaround or negotiate better deals with data owners.
The third aspect is ethical - is the data product creating outcomes that are good for the related participants. The ethical side of doing business is now becoming more and more important. Your idea might make sense in business numbers, and it might be legal to implement it, but it might not be ethical. In that case, you should not proceed but find an alternative way.
The final and last aspect is technical. This is not the problem nowadays. If all the above aspects have a green light, then the technical solution should be designed for implementation.
Needless to say but all the above is not mandated to happen in series, you can do the steps in parallel and often you need to go back and forth as the product idea crystallizes.
Aspects are defined in Open Data Product Specification
The above aspects: business, legal, ethical and technical are the 4 pillars of the Open Data Product Standard. The Open Data Product Specification is a vendor-neutral, open-source machine-readable data product metadata model. It defines the objects and attributes as well as the structure of digital data products. Take a look at it at https://opendataproducts.org