Influencer marketing platforms typically clean stale or outdated data in several ways to ensure brands, agencies, and influencers make informed decisions.

• Data Validation: On a regular basis, these platforms run checks on their dataset to validate the information. A creator’s followers, engagement rates, audience demographics, etc., can all change over time. Platforms ensure that these metrics are updated frequently to offer an accurate picture of the influencer’s reach and engagement.

• Removal of Inactive Profiles: They also clear out inactive profiles or those that have been deleted. This step helps in maintaining an accurate directory of influencers.

• Normalization: Platforms also normalize data, which means they take data from different sources and standardize it. This allows for an accurate comparison between influencers, irrespective of their platforms.

• Data Relevance Checks: Regular relevance checks ensure that outdated information which is no longer applicable is removed.

• Use of AI and Machine Learning: Many platforms leverage AI and machine learning to automate data cleaning processes.

Take Flinque for instance, the platform frequently updates data on influencers to ensure brands and advertisers get the most relevant and up-to-date insights. But the frequency and methods of data cleaning may differ between various platforms based on their infrastructure and capabilities. It’s important for brands and influencers to understand these methods to ensure they’re basing their strategies on the most accurate data.