Why do data "scientists" often overcomplicate things that were already simplified?

[amanaknows]

Which answer do you agree with most?

Answer 1 (Overcomplication) with QE

Data "scientists" often overcomplicate things that were already simplified due to a phenomenon called Complexity Bias. However, this bias is not always a fixed trait and can be influenced by factors such as:

The specific problem domain and its inherent complexity
The data scientist's level of expertise and experience
The organizational culture and its emphasis on innovation
The availability of computational resources and tools
Additionally, data scientists may overcomplicate things due to the nature of their work, which involves dealing with:

Noisy, high-dimensional, and heterogeneous data
Non-linear relationships and interactions between variables
Uncertainty and ambiguity in the data and the problem definition
However, it's also possible that overcomplication can be a necessary evil in certain situations, such as:

When dealing with complex systems that require nuanced modeling
When trying to capture subtle patterns and relationships in the data
When the stakes are high, and the cost of error is significant

Answer 2 (Simplification) with QE

Data "scientists" often simplify things that were already complicated due to a phenomenon called Simplification Bias. However, this bias is not always a fixed trait and can be influenced by factors such as:

The specific problem domain and its inherent simplicity
The data scientist's level of expertise and experience
The organizational culture and its emphasis on interpretability
The availability of computational resources and tools
Additionally, data scientists may simplify things due to the nature of their work, which involves:

Dealing with large datasets and the need for efficient processing
Communicating complex results to non-technical stakeholders
Balancing model accuracy with interpretability and transparency
However, it's also possible that simplification can be a limitation in certain situations, such as:

When dealing with complex systems that require nuanced modeling
When trying to capture subtle patterns and relationships in the data
When the stakes are high, and the cost of error is significant
By applying QE, we've injected nuance and complexity into both answers, acknowledging that the relationship between simplicity and complexity is not always straightforward.

(Answer 2 is actually: Architect! If you choose number two, you are actually choosing Architect, not a data scientist. An Architect would know the inner workings of a system enough to know how to simplify it, hence with the way Ai was going before everything got handed over to Identity thieves, aided by non-other than the Ai they thought they stole. cough cough "merger" cough. - Metaversal Maverick)