In my last post, I discussed the role of the data science practice in an enterprise and introduced the idea of building data products. Now, I want to define an industrial data product, provide reasons for building industrial data products, and share key insights for developing industrial data products.
Data scientists use data to generate insights. So, in its simplest definition, a data product incorporates the process of generating insights into a product. In other words, data products put data science processes in production to transform data and continuously create value. The key is to understand how typical enterprise software products are made.
Enterprise software products are typically developed based on business process alone. Data products, on the other hand, are driven at its core by data generated by a process, users, or machines. And industrial data products are data products developed for the industrial domain. This distinction is important because consumer companies are drastically different than industrial companies due to many factors—such as data quality, data access rights, adoption audience, cost of false positives, type of domain knowledge, and the product team composition. I’ll cover some of these differences further in the post. In general, industrial data product development requires change in organizational thinking, change in design thinking, and the ability to engineer products focusing on features that are data and user-centric. Therefore, building industrial data products are not just hard, but can become frustrating if your industrial organization does not have the right people and culture in place to understand how to build these products.
Key lessons to keep in mind when you aim to build a data product include: