Creating API products is by far one of the most rewarding projects a company can embark on, as they open a huge avenue for revenue and customer expansion. As expressed in previous posts, API products are a gateway to a company’s core services, which are presented to its consumers as commercial products. API products acquire super powers when they are combined with a data processing strategy. This allows them to become efficient providers of either raw data or data in the form of insights aimed at improving existing processes.
In this and the next post, I will analyze the way API products can become highly profitable data products by means of identifying their goal, their value, their implementation, and their evolution.
Identifying the goal of the data product
The core services of any modern organization rely on data, either internally or externally generated. More often than not, external data comes from more than one, usually disparate, data source. Data from these disparate sources is then pre-processed, normalized, combined and joined, condensed and summarized, and finally analyzed to produce useful value. This last step is what differentiates the value provided by the company based on the ingested data. In other words, there is a lot of effort invested between generating or acquiring raw data from disparate sources and getting actual, useful value from it.
It makes sense to think about ways to obtain additional value and revenue from this effort. Data products are a nice option to achieve this goal.
Based on the above, we can identify three types of data products: Those that provide direct revenue by simply providing access to a company’s information products, those that provide indirect revenue by assisting or enhancing user’s existing processes (think predictive analytics and recommender systems), and those designed for internal use only whose sole purpose is to provide insights to help employees do their job more efficiently and effectively.
Once the type of the data product is determined, it is easy to identify the goal that will guide all the implementation efforts.
Identifying value provided by data products
Successful data products are those that provide great value for their users, either directly or indirectly. Therefore, special attention needs to be paid to the process of finding this value. The naive, and usually wrong, approach is to simply take a dataset and make a simple product out of it. Not only does this affect the relevance of the product provided, but it also affects current and future revenue opportunities and, in the long term, customer loyalty. The following considerations may be helpful in the process of identifying the value of a data product:
- Find connections between your data and other data sources that can help the user find useful insights and make decisions. Data triangulation is a nice option in this case.
- Identify at least one step in their thought process toward a goal. For instance, for organizing a garage sale, a potential process could be: Deciding to clean up and make some space, setting the price on all the items, and informing all the potential buyers about this sale. A useful data product could provide access to a list of potential buyers looking to buy used stuff at a competitive price.
- Design and implement the data product with a specific audience in mind. This will guide all the efforts aimed at converting the data into something useful.
- Design the data product in a way that it can adapt to its consumers’ needs over time.
- Combine two or more data products to create more powerful ones. For example, one data product can attract users by satisfying a non-essential need, such as entertainment, curiosity or social interaction, and another data product can use the information obtained from the interaction between the users and the first data product and provide useful insights. A good example of this is a movie ranking system with additional recommending capabilities.
All in all, a regular API product can be nicely enhanced when combined with data processing capabilities. This turns an API product into a data product that provides its users with useful insights and support for making decisions. However, it is important to note that for a data product to be successful, it is necessary to clearly identify its value based on its audience and goals, its ability to adapt to its audience’s needs, its ability to work with other data products, and the possible ways its underlying data can be processed.