Leveraging an Iterative Process for Agile AnalyticsA Modern Approach to Market Development Strategies
Iterative research embraces agility, allowing businesses to test hypotheses, gather insights, and implement changes swiftly. This approach is particularly beneficial in fast-paced markets where consumer behaviors and preferences can shift rapidly. Before we breakdown how we create build analytical models though an iterative process, let’s start with the basics.
What is an ‘iterative process’ in Agile Analytics?
The iterative process is a cornerstone of agile analytics, enabling teams to deliver timely, relevant data driven insights through continuous cycles of development, feedback, and refinement. Rather than being a one-time, linear effort, this process is repeated in cycles, each time improving the model or analytical insight based on new data, errors, or performance evaluations. This approach contrasts with traditional methods by emphasizing flexibility, collaboration, and responsiveness to change.
What are the Key Steps in CMI’s Iterative Process of Advanced Analytics?
1) DISCOVER
- Clearly define the business problem or objective.
- Translate the business goal into an analytical question.
2) PLAN
- Gather relevant data from various sources.
- Clean, transform, and engineer features as needed.
- Select model.
3) BUILD A DATA STRATEGY
- Choose appropriate algorithms.
- Train models using subsets of the data.
- Assess model performance using metrics.
- Identify overfitting, underfitting, or bias.
4) REVIEW
- Adjust parameters, try new features, or select different algorithms.
- Iterate on preprocessing, feature selection, or sampling techniques.
- Use cross-validation, holdout data, or A/B testing for robust evaluation.
- Continue looping through prior steps as needed.
- Continuously monitor performance and retrain when necessary.
Traditional
Define all up front
Deliver all at once
AGILE
Define iteratively
Deliver
Why Does Iteration matter in Advanced Analytics?
Iteration turns customer analytics from a one-off report into an evolving decision engine. It allows you to:
- Learn from Errors: Every cycle reveals insights into data quality, model behavior, or assumptions.
- Handle Model Drift: As real-world data changes, models need to be retrained or updated.
- Explore Alternatives: Multiple iterations allow analysts to test various hypotheses or feature sets.
- Improve Predictive Power: Incremental improvements can significantly enhance accuracy and business growth strategies.