Intro
Over the recent holiday period, social media posts emerged regarding an alleged leak of data related to Google's ranking algorithms. Initial discussions around these leaks focused on "confirming" long-held beliefs by figures like Rand Fishkin but lacked context on the true nature of the data.
Context Matters: Document AI Warehouse
The leaked data appears to be related to Google’s Document AI Warehouse, a public Google Cloud platform used for analyzing, organizing, searching, and storing data. This public documentation is titled "Document AI Warehouse overview." Posts on platforms like Facebook suggest that the leaked data is an "internal version" of this publicly available documentation, indicating that it may not be exclusive to Google Search operations.
Leak of Internal Search Data?
The original post on SparkToro did not claim the data was from Google Search but stated that the source who provided the data to Rand Fishkin made this assertion. Fishkin, known for his meticulous approach, noted that the claim about the data originating from Google Search came from the person who emailed him, not from verified sources.
Fishkin quoted the email:
"I received an email from a person claiming to have access to a massive leak of API documentation from inside Google’s Search division."
Despite this, ex-Googlers consulted by Fishkin could only confirm that the data resembled internal Google information but did not explicitly verify that it was from Google Search.
Insights from Ex-Googlers
Ex-Googlers commented:
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"I didn’t have access to this code when I worked there. But this certainly looks legit."
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"It has all the hallmarks of an internal Google API."
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"It’s a Java-based API. And someone spent a lot of time adhering to Google’s own internal standards for documentation and naming."
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"I’d need more time to be sure, but this matches internal documentation I’m familiar with."
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"Nothing I saw in a brief review suggests this is anything but legit."
These statements highlight that while the data looks genuine, there is no definitive proof it is from Google Search.
Keeping an Open Mind
It is crucial to remain open-minded about this data since much of it remains unverified. Jumping to conclusions or using the data to confirm pre-existing beliefs can lead to confirmation bias, where one interprets information in a way that reinforces their existing views.
Definition of Confirmation Bias:
"Confirmation bias is the tendency to search for, interpret, favor, and recall information in a way that confirms or supports one’s prior beliefs or values."
Key Questions About the Google Data Leak
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Context of the Leaked Information: Is the data related to Google Search or other purposes?
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Purpose of the Data: Was it used for actual search results, or for internal data management or manipulation?
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Confirmation from Ex-Googlers: The ex-Googlers did not confirm the data is specific to Google Search, only that it appears to come from Google.
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Open-Minded Analysis: Avoid using the data to confirm long-held beliefs to prevent confirmation bias.
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Relation to Document AI Warehouse: Evidence suggests the data may relate to an external-facing API for building a document warehouse rather than Google Search.
Expert Opinions on the "Leaked" Data
SEO expert Ryan Jones shared:
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Uncertainty if the data is for production or testing.
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Lack of clarity if it's for web search or other verticals like Google Home or News.
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Speculation that some fields apply only to training datasets, not all sites.
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"We don’t know if this is for Google search or Google cloud document retrieval. APIs seem pick & choose – that’s not how I expect the algorithm to be run – what if an engineer wants to skip all those quality checks – this looks like I want to build a content warehouse app for my enterprise knowledge base."
Conclusion
At present, there is no concrete evidence that the "leaked" data is from Google Search. The context and purpose of the data remain ambiguous, with indications pointing towards it being an external-facing API for document management rather than a core component of Google's search algorithm. It's essential to approach this information with caution and avoid drawing definitive conclusions without further verification.