Have you ever wondered how can we justify the business values created by data science? Is there any difference between a profit centre setup (i.e. client/stakeholder funded) for data science versus a cost centre setup (i.e. centrally funded)?
For any business function in a company, it is important for the business function to be able to justify the value that it brings to a company.
This is no different for the data science function in a company.
For data science function that operates as a profit centre, such as in data science consulting and tech product development, the business value is relatively easy to justify by looking at the share of revenue/profit that can be attributed to outputs from data science.
E.g. A consulting project that lasted 6 months with 3 billed FTE (one of which is a data scientist) brought in an EBIT of $300k, so we could attribute $100k to the value that was contributed by data science.
In most conventional companies, the data science function operates as a cost centre. The business value provided by a cost centre can be indirectly justified by the value of the business processes that it supports.
However data science as a cost centre differs from most other cost centres. This is because data science is a new field whose purpose is (almost) entirely to improve efficiencies in existing business processes owned by other functions. This means that a data science function can only justify its business value if it can help other business functions justify their values more effectively.
E.g. A data science team created a tool that automatically optimises scheduling of worker shifts, reducing the time needed for the planning team to manually schedule shifts from 10 hours per week to 1 hour per week. Assuming a FTE costs $50k per year (~$21.4 per hour), this leads to ~$10k of cost savings per year contributed by a data science solution.
Regardless of whether operating as a profit centre or a cost centre, the need to justify business values from data science is only going to increase in the future. Especially when the AI hype wave recedes.
