The Particular Nature of Data Science Projects
Many people, especially those working in or with IT (Information Technology) departments, are familiar with software engineering practices and with the typical features of software development projects. Unfortunately many of them have the same expectations for Data Science projects. But they must change some entrenched and old practices. In this post we explain what is unique in the goals that Data Science projects pursue and why these unique aspects make their return on investment much higher.
Software development projects have as its ultimate goal the making of a software with determined features (that were specified in an iterative or not fashion) to satisfy clear functional business needs. This is unlike Data Science projects.
Data Science projects have unique goals
Companies are sitting on a lot of unused but valuable data. The insights that Data Science projects provide can improve business plans and strategies greatly. For example, advanced predictive techniques and real-time processing can result in large enhancements, encompassing different areas and achieving different goals. Data Science applications may also lead to better decision support and even enable automated decision support and early warning systems in real time.
Data Science projects have two fundamental goals: data insights and data discovery. They require an open exploration of data, starting from the capture and transformation of raw data to the later focus on data discovery and modeling. Their business goals are broad and more ambitious in their diverse and large areas of application, seeking objectives such as:
- Increased operational efficiency
- Better customer services
- Enhanced performance
- Greater health and life saving measures
- Better price targeting
- Lower machine failure rates
- Lower customer defection rates
- Higher quality measures
- Better capacity forecast
- Improved resource control
As a result, the return on investment (what you get from what you invest) is much higher in Data Science projects, not only in monetary value but also in strategical importance.
