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Blending CRISP-DM with Scrum in Agile Data Science
Over the past two years, artificial intelligence has emerged from research labs into the spotlight of product development. With this shift, it’s crucial to critically evaluate our methodologies to effectively build intelligent products. By combining CRISP-DM and Scrum, organizations can bridge the gap between data scientists and stakeholders, fostering collaboration and innovation.
What is CRISP-DM?
CRISP-DM, or the Cross-Industry Standard Process for Data Mining, has been the leader in data analytics methods for nearly three decades. It provides a structured yet flexible approach ideal for data science projects, enabling iterative experimentation and hypothesis testing—the core of AI development.
Challenges in Traditional Product Development
Traditional product development often overlooks the critical role of data science, focusing instead on engineering-centric processes. However, with the recent surge in AI-infused products—up from 40% in 2023 to 78% in 2024, according to Forbes—the need for flexible methodologies that incorporate data science has become evident. Data scientists frequently face friction with Scrum purists due to their need for flexible, less predictable workflows that don’t easily fit into rigid sprint cycles.
Scrum and CRISP-DM: Differences and Synergies
No longer can organizations rely solely on Scrum structured sprints and predictable cycles. Data science requires room for exploration and creativity, which traditional Scrum doesn’t always allow. Enter CRISP-DM: a methodology that thrives on iterative experimentation. By using CRISP-DM’s structured processes alongside Scrum’s iterative cycles, teams can gain the flexibility needed for innovative AI solutions.
The 6-Phase Iterative CRISP-DM Process
CRISP-DM’s six-phase process offers a comprehensive roadmap:
- Business Understanding: Sets the project’s objectives from a business viewpoint, ensuring data scientists’ efforts align with organizational goals.
- Data Understanding: Focuses on collecting and analyzing data to uncover preliminary insights and identify patterns or anomalies.
- Data Preparation: Involves cleaning and transforming data for modeling, addressing issues like missing values and data normalization.
- Modeling: Applies various techniques to prepared data, involving algorithm selection, parameter tuning, and iterative refinement.
- Evaluation: Validates models to ensure they meet business objectives and determines the subsequent steps.
- Deployment: Integrates the model into a real-world environment, continuing the cycle with new data and objectives.
For organizations familiar with iterative frameworks like Scrum, CRISP-DM complements these approaches, guiding teams through data discovery and model development phases where clarity and rigor are essential.
A Case Study: Netflix
Netflix effectively utilizes data science to refine its recommendation algorithm. With CRISP-DM providing rigorous data handling and model training, Netflix uses Scrum in cross-functional teams to deliver continuous improvements to its recommendation system based on user behavior. By training teams in both agile and data science principles, they optimize the deployment of features that evolve with user needs.
Challenges in Hybrid Methodology Adoption
Despite the potential benefits of blending Scrum and CRISP-DM, wider adoption is impeded by stakeholder expectations. Stakeholders accustomed to rapid Scrum results need to appreciate data science’s exploratory nature, which often favors learning over immediate outcomes. Encouraging this mindset shift is crucial for incorporating data science methodologies into traditional product development processes.
In conclusion, merging CRISP-DM with Scrum can empower organizations to better leverage AI and data science, adapting to the complexities of modern product development. By embracing exploratory work inherent in data science, teams can facilitate innovation and deliver cutting-edge solutions.
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Source: https://builtin.com/articles/crisp-dm-data-science