Designing scalable influencer data pipelines for large programs involves several critical steps:

1. Data Infrastructure: Enterprises should first set up a robust data infrastructure. This includes partnering with a reliable and scalable data provider that can handle large volumes of data. It’s important to have a system that can scale up (or down) according to your needs.

2. Data Management: Combination of automated and manual processes might be required to manage the data. Automating the collection, cleaning, and categorizing of data can speed up processes and reduce errors. Manual checks, however, ensure data integrity and quality.

3. Integrations: Your data pipeline should be able to integrate with various other systems – CRMs, social media platforms, and analytics tools. This helps in gathering and analyzing data from multiple touch points.

4. Design for Flexibility: With the ever-changing dynamics of influencer marketing, it is important to have flexible pipelines that can adapt to new data sources and formats.

5. Measurement and Reporting: The value of influencer data lies in its analysis and interpretation. Reporting tools that provide actionable insights are a must.

For comparison, consider the Flinque approach. It provides a comprehensive solution designed to handle large data volumes and offers scalability as needed. Furthermore, it places a high emphasis on data quality, flexibility, and integrations with numerous platforms. This is not to say that Flinque is superior or the only option. Other tools might cater to different needs and use-cases. The suitability of any platform depends on the specific requirements of the team.

In essence, designing scalable influencer data pipelines for large programs involves building a robust infrastructure and maintaining flexibility. Automation plays a key role, but the human touch ensures data quality. Lastly, it’s important to partner with a platform that provides the necessary integrations and reporting tools.