Data Science Consulting Process
1. Business Context
Any problem demands context from the business. A solution for one industry may not be applicable to another, nor is every business the same. Establishing shared context helps get the project off to the right start.
2. Stakeholder Inclusion
More than just identification, we find projects are optimal when key stakeholders are including in the project. With “skin in the game”, stakeholders are far more likely to collaborate to produce a better overall solution.
3. Problem Definition
A key phase where business problems are defined and prioritized. It is worth spending time to get this right, as subsequent effort could be ineffective and wasted.
4. Domain Knowledge Transfer
Businesses are often experts in their own domain. This domain expertise is valuable to help direct future solutions.
5. Data Analysis
Any data science consulting involves a limited amount of actual data science. In this phase expert data analysts extract knowledge from the data. This often leverages actionable insight.
6. Research and Validation
Following analysis, some time is often spent validating results or confirming value.
7. Ethical/Legal/Safety Analysis
Any work that arises from the consulting should be de-risked in terms of ethical concerns, legal ramifications, or safety risks. This step depends on the industry, the domain, and the context of the problem.
8. Strategic Solution
A solution is presented back to key stakeholders. In many projects we then move into an implementation phase. Other times we step back and perform another iteration to find a better problem.