If you want to sell data, you need to know the fair price for the data or at least understand the different models of how the data can be priced. Many companies are wondering what would be the appropriate pricing model for their own data but to be honest, there is no one-size-fits-all answer to this. Thus it is necessary to go through the different pricing models one by one. Pricing is often talked about in a very complex way because the best (most profitable) pricing model is often a combination of different models. Sometimes it is absolutely necessary and appropriate to make one's data available free of charge to various partners, but sometimes one can ask for a really high price, for example, when selling data for raw material for the service development of other companies. Now, this blog will go through a cost-based pricing model that serves as a good understanding of what your data will cost you, so the minimum price, it’s worth trading.
1. The first cost factor is data collection and integrations. Once you understand from which systems or data sources data is collected, how often data is collected, how much data is generated, and how long the data is stored, you will already begin to get a good idea of how much information you will have available. Integrations have a price tag, often data transfer also has a transaction price and the amount of data transferred has a unit price. In addition, integrations have a lifecycle, maintenance, and documentation costs. All these must be taken into account.
2. After these, when data is collected, it is good to think about storing and taking care of it. In many cases, data storage, storage type and processing have very accurate and transparent unit prices, from which the data volumes and data collection cycles mentioned in the first paragraph can be used to estimate the real costs over the life cycle of the data. Data warehousing and cloud services as well as data center solutions also come at a price.
3. If you are not selling raw data to anyone but visualized and pre-analyzed understanding, the cost of these steps must be considered. In many cases, however, there is a move to selling understanding away from selling data, so that purely cost-based pricing changes to value-based pricing. However, this also does not rule out the need to make the cost of analysis transparent, which allows for better ROI and margin calculation in all different sales models.
4. Then perhaps the most difficult to price item is the development and maintenance work done by the experts. One of the easiest cost estimates for experts is the average hourly cost and average salary if it is difficult to measure actual costs. At this stage, however, it is not worth forgetting the work that takes place during the life cycle. All technical solutions require maintenance, upgrade and documentation. Not forgetting security or other possible additional work.
5. The last thing to think about is what kind of data is sold and whether selling it requires manual work. If delivering data, billing, opening interfaces, and managing access rights is cumbersome, expensive, and time consuming, it may be very necessary to sell data only in large and long-lasting deliveries. At this stage, it is also good to think about your own gross margin over production costs, which creates a price tag for the data.
There may be many cost variables behind these simplified steps, and these actual costs may actually vary depending on the structure and operating costs of your own ICT infrastructure. While evaluating these may seem cumbersome, it is all the more useful and necessary on the road to a data economy and more cost-effective management for it as well.