Data Science POC 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. Domain Knowledge Transfer
Businesses are often experts in their own domain. This domain expertise is valuable to help direct future solutions.
3. Problem Definition/Clarification
POCs usually start with a vague idea of what problem they are trying to solve. But the problem definition often changes over time, becoming more concrete, adapting to what is possible given the data.
4. Data Exploration
Using the domain knowledge, we gather even more insight by exploring your data. You never know what you’ll find!
5. Data Analysis
In this phase expert data analysts extract knowledge from the data. This often leverages actionable insight and is used to validate whether the solution is viable.
6. Model Exploration
After the data analysis validates that the idea is sound, an initial phase of model exploration is intended to validate whether the problem can be automated.
7. Model Analysis
After this phase the models are analysed to ensure they are performant enough to suggest viability. Note that even at this late stage it is sometimes necessary to revisit the problem definition.
8. Reporting
Once models are validated then it’s time to report the results back to the stakeholders. After this phase we often start looking at another problem, or promote it to a fully-fledged machine learning development project.