A good data analyst would be able to understand the market dynamics and act accordingly to retain a working data model so as to adjust to the new environment.

The frequency with which a data model should be retained depends on various factors including the nature of the data, the rate of change in the underlying data, the business requirements, and the resources available for model maintenance. Here are some considerations to determine the appropriate retention frequency:

Data volatility: If the data being modeled changes frequently, the model may need to be updated more frequently to stay accurate and relevant.
Business requirements: Consider the specific needs of the business and stakeholders. Some industries or use cases may require real-time or near-real-time updates to the model, while others may be satisfied with periodic updates.
Resource constraints: Evaluate the availability of resources such as time, budget, and expertise required to update and maintain the model. More frequent updates may require more resources.
Performance metrics: Monitor the performance of the model over time and establish thresholds for when it should be retrained or updated. This could be based on metrics such as accuracy, precision, recall, or other relevant measures.
Regulatory compliance: Ensure that the retention frequency complies with any regulatory requirements governing the data and its use.
Technology advancements: Keep abreast of advancements in data analytics techniques and tools. New methodologies or technologies may necessitate more frequent updates to take advantage of improvements.

In summary, there’s no one-size-fits-all answer to how often a data model should be retained. It’s essential to regularly assess and adjust the retention frequency based on the factors mentioned above to ensure that the model remains effective and relevant to the organization’s needs.