How to build a data science methodology that works for your team
The below interview is syndicated from Built In San Francisco.
Built In: Tell us a bit about the data science methodology your team follows. What are the core tenets or steps of this methodology?
Mark:
Data Science is very much a team sport and here at Atlassian we adopt a very collaborative engagement model.
We start out by ensuring that any AI projects are firmly anchored within a business context, this is critical for the overall success of any Data Science team. The key high-level ML lifecycle steps are then: Business Context > Data Prep > Build Model > Validate > Deploy > Monitor > Refine.
This is an iterative process and we ensure a close partnership with the appropriate teams at each stage, ultimately creating a ‘flywheel effect’ to optimize and compress the ML model R&D deployment cycle.
BI: What steps did you take to develop this methodology? Was it inspired or influenced by another methodology (like CRISP-DM, SEMMA or KDD)?
Mark: The current methodology has evolved over many years through study and experimentation and is inspired by multiple sources in particular PDSA (Plan-Do-Study-Act or ‘Deming/ Shewhart’ cycle), DMAIC (Define-Measure-Analyze-Improve-Control cycle) and CRISP-DM (Cross-Industry-Standard-Process for Data Mining), for example. It is essentially a hybrid of these different approaches tailored to the specific environment and use case.
BI: How have you evolved this methodology over time to ensure it suits your team’s needs? How did you know it needed refining?
Mark: As Data Science at Atlassian has grown, the approach and process has evolved and become more formalized. Infrastructure development around core ML services and tooling has also allowed us to adjust and scale accordingly.
The key principle around this or any methodology is flexibility.
The ability to adapt and pivot as required in an ever-changing environment. In essence a methodology is really only a guide and should be flexible enough to be tailored to a specific situation as needed.
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About the Author
Mark Scarr is the Head of Data Science at Atlassian. He leads a team of machine learning specialists who provide end-to-end solutions for intelligent monetization strategies.
