Now is the time to raise a very general 10-step list about how to answer business questions with data, what steps are in the data value chain, and at what point the data is worth starting to sell. There are more specific tools and mechanisms for each of these points, but it is essential to understand what the whole should include.
1) Form a business question or hypothesis you want answered from the data.
2) Think about all the data (i.e. what variables and from which data sources should be collected) so that you can answer the question.
3) Collect raw data from the above data sources, which serves as a basis and raw material for all data-related decision and action making.
4) Categorize and review incoming data and define metadata such as collection time, location, ownership, GDRP details, data model, accuracy, life cycle, etc. If you know that the data is even curable for only two years, then it is not worth it retain for five years in the data warehouse. Defining a lifecycle at this stage minimizes cost over the lifecycle. Quality control before storing data also minimizes problems and costs when data is used for decision making.
5) Save the information in the correct location. Not all data should be stored in one place. This can be a significant cost factor during the lifetime.
6) Combine information. Combine information at the data model level into a harmonious entity against which a business can also ask other questions for which the data provides business value.
7) Analyze the answers to the question you gave in step 1 from the data. At this point, you can already search for answers by visualizing and generalizing (or looking for exceptions) in the data.
8) These answers, i.e. insights, generate value and can be brought up automatically by visualizing the question-answer pairs that the business needs in its day-to-day work, service development, and other analysis.
9) Make decisions related to the new insights mentioned above that affect your business and then evaluate the value of the data you collect, analyze, and visualize. With the business impact, you can give a value for your data and set a price tag for it.
After all, do not sell your raw data for anyone but sell analyzed and visualized data with a much higher value. In other writings, we give examples of what different channels and ways you have for selling data and to whom it should be sold. But our recommendation is that the raw data itself (especially if the quality has not been checked and the metadata is not in order) has no significant value but as you refine the data for yourself understanding, it also becomes more valuable to others, making it worthwhile to use and use as a asset for exchange.