The 4 steps of agile data science help identify data and machine learning models that will quickly add value to a business. The 4 steps of agile data science include applying this methodology to the concept:
Develop
Determine which high-value opportunities to tackle with predictive data science
Workshop
Dig into the data to make sure the data assets and access are clearly mapped out. Then work with subject-matter experts and internal stakeholders to examine the nuances of the opportunity and calculate how success will be measured.
Prove
Develop the solution for a logical subset of your business using your data and our AI models. Ensure the results are actionable with validated learnings and a clear return on investment.
Operationalize
Deploy the solution at enterprise scale, address priority gaps, automate data feeds, and partner on the plan to fully implement the solution for your business.
As a collaborative learning methodology, it presents the right opportunities for prediction and puts predictive data to work. As an IT-light solution that’s focused on delivering tangible outcomes with measurable returns. It delivers results quickly.