Oct '23 • General discussion
CRISP-DM (Cross-Industry Standard Process for Data Mining) methodology
From the perspective of data governance and expertise, the CRISP-DM (Cross-Industry Standard Process for Data Mining) methodology is a structured approach to solving complex data-related problems. It consists of several distinct phases, each serving a crucial role in the success of a data project. Let's delve into each step with a focus on data governance and expertise:
1- Business Understanding Phase:
- In this initial phase, your role as a data expert is to engage with the customer to understand their problem clearly. It's essential to communicate without using technical jargon to ensure alignment.
- Key questions to ask include:What is the specific date range you are interested in for this project?Are there any existing reports or data that relate to this request?It's an opportunity to validate if the project is scalable.Inquire about the existence of project documentation for any existing reports. This documentation is vital for understanding historical context.
2 - Data Understanding Phase:
- Your data governance expertise comes into play here. Ask for details about the databases storing relevant data.
- If the customer is uncertain, identify and engage with the database administrator responsible for the data source.
3 - Data Presentation Phase:
- While this phase may not directly involve data governance, data experts must present their findings and insights clearly and promptly to stakeholders.
4- Data Modeling Phase:
- As a data analyst, your role in this phase includes working within a developer environment and establishing table relationships.
- Ensure that data governance principles, such as data lineage and quality, are maintained during the modelling process.
5- Validation or Evaluation Phase:
- Quality assurance testing is a critical aspect of data governance. Ensure that data quality is maintained throughout this phase.
- Perform thorough validation and evaluation of the models and data to ensure they meet the required standards.
6- Deployment Phase:
- In this phase, your expertise is essential in moving the project to a production environment after User Acceptance Testing (UAT).
- Pay attention to data security and access controls during deployment to maintain data governance standards.
As a data analyst, your involvement throughout the CRISP-DM process is critical to ensuring data integrity, security, and compliance. You play a pivotal role in translating business needs into data solutions while maintaining the highest data governance and security standards. This approach gives your organization a distinctive advantage by mitigating risks and ensuring the success of data projects.
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CRISP-DM (Cross-Industry Standard Process for Data Mining) methodology
