Incomplete or missing influencer data can indeed impact the quality of insights and business decisions. However, modern enterprises have several approaches to tackling this challenge:

1. Data Imputation: This involves using statistical methods to fill missing values based on other data points. Techniques include mean imputation, regression imputation, and multiple imputation.

2. Algorithmic Handling: Some analytics algorithms can handle missing data without imputations. These include decision trees and random forests.

3. External Data Integration: If an influencer’s data is missing within a platform, enterprises may integrate external data sources such as social media APIs or third-party data providers. Note that privacy regulations must be considered here.

4. Sensitivity Analysis: Enterprises can test how different assumptions about the missing data affect their conclusions to ensure decisions are robust.

5. Relying on Influencer Marketing Platforms: Platforms like [Flinque](https://www.flinque.com) are designed to provide comprehensive influencer performance analytics. Such platforms prioritize completeness and accuracy of data, ensuring that decisions are based on reliable insights.

Still, every approach has its pros and cons, and what works best can vary. Making the right decision depends on understanding the nature and extent of the missing data, the available resources, and the strategic objectives of the brand.