2.2 Beyond Data and Analytics

Data scientists usually have a good sense of data and analytics, but data scientist project is more than that. A data science project may involve people with different roles, especially in a big company:

  • a business owner or leader to identify business value;
  • program manager to ensure the data science project fits into the overall technical program development and coordinate all parties to set periodical tasks so that the project meets the preset milestones and results;
  • data owner and computation resource and infrastructure owner from the IT department;
  • dedicated team to make sure the data and model are under model governance and privacy guidelines;
  • a team to implement, maintain and refresh the model;
  • multiple rounds of discussion of resource allocation among groups (i.e., who pay for the data science project).

Effective communication and in-depth domain knowledge about the business problem are essential requirements for a successful data scientist. A data scientist may interact with people at various levels from senior leaders who set the corporate strategies to front-line employees who do the daily work. A data scientist needs to have the capability to view the problem from 10,000 feet above the ground, as well as down to the detail to the very bottom. To convert a business question into a data problem, a data scientist needs to communicate using the language the other people can understand and obtain the required information.

In the entire process of data science project defining, planning, executing and implementing, every step involves the data scientist to ensure people correctly define the business problem and reasonably evaluate the business value and success. Corporates are investing heavily in data science and machine learning with a very high expectation of return.

However, it is easy to set unrealistic goal and wrongly estimate the business impact. The data scientist lead should navigate the discussions to make sure the goal can be backed by data and analytics. Many data science projects over promise and are too optimistic on the timeline. These projects eventually fail by not delivering the preset business impact within the timeline. As data scientists, we need to identify these issues early in the stage and communicate with the entire team to make sure the project has a realistic deliverable and timeline. The data scientist team also need to work closely with data owners to identify relevant internal and external data source and evaluate the quality of the data; as well as working closely with the infrastructure team to understand the computation resources (i.e. hardware and software) available for the data science project.