Questions that the whitepaper answers:

1. What skill sets are needed for a data project?
2. How to structure a data project?
3. What preconditions must be met to ensure the success of a data project?
4. What challenges are often encountered in data projects, and what best practices can be put in place to overcome them?

It is challenging to create successful data products and harness the potential of data. A project may start off with all the right components in place, such as a clear business problem, a multidisciplinary team, and the availability of the right tools. However, without a well-founded way of working that offers structure and guidance, a data project remains vulnerable to losing track.

In 2020 we discussed the obstacles and best practices encountered in data projects. Going through research articles and blogs, we couldn’t quite find the answers we were looking for. Despite the great body of articles that touch upon the topic, the level of detail and focus they were looking for was lacking. Looking to reach conclusions on how to lead successful data projects or implement the best practices, we decided to structure the obstacles and best practices we encountered from our experience with a variety of clients. We decided to write down their findings in this paper, collecting best practices and existing frameworks, and combining these into a coherent vision on a way of working within agile data projects. Within Xomnia, this approach is now known under the name: WOW-X. We want to share our findings and conclusions with anyone that is encountering challenges while executing data projects. This paper is written for people responsible for managing data projects and delivering data products.

The first section covers preparatory work as the paper digs into the prerequisites of a given data project, including skill sets and tech-related requirements. This is followed by a deep dive into three main points of focus throughout the course of any data project, which aim to to increase the likelihood of a successful and valuable outcome. The first point of focus is use case exploration, the second is development, and the third is operations. For each focal point, commonly observed obstacles are discussed along with best practices to circumvent said obstacles. The goal is to leave the reader with a better understanding of the requirements of successful data product development.