Time Allocation Strategies: Balancing Time Investment Across Phases in Data Science
In the Data Analytics Lifecycle, there are six phases:
1. Discovery: In this phase, the team learns about the business domain and assesses the available resources. They frame the business problem as an analytics challenge and formulate initial hypotheses.
2. Data preparation: This phase involves setting up an analytic sandbox and executing extract, load, and transform (ELT) or extract, transform, and load (ETL) processes to get the data into the sandbox.
3. Model planning: In this phase, the team determines the approach to be used for analysis, selects the appropriate models, and plans the implementation of these models.
4. Model building: Here, the team builds the models using the selected approach and techniques. They perform rigorous investigation of the datasets and refine the models as needed.
5. Communicate results: In this phase, the team presents the findings and insights from the analysis to stakeholders. They communicate the results in a clear and understandable manner.
6. Operationalize: The final phase involves integrating the models and insights into the organization’s operations. This includes implementing the models into production systems and monitoring their performance.
The data science team would expect to invest the most time in Phase 4: Model building. This is because Phase 4 involves the actual development and implementation of the analytical models, which requires extensive data analysis, testing, and refinement. It is a crucial phase where the team needs to ensure that the models are accurate, reliable, and aligned with the project’s objectives. Therefore, it is expected to be time-consuming as it involves complex tasks and iterative processes to build and fine-tune the models.
The team would expect to spend the least amount of time in Phase 2: Data preparation. This is because Phase 2 involves transforming and conditioning the data so that the team can work with it and analyze it. It is a necessary step, but it may not require as much time as other phases such as model planning or model building, which involve more complex tasks and decision-making processes.
