A profile of Hank Asher, the “father of data fusion” who died in 2013 after initiating a vast shift in privacy norms through his data mining software companies
McKenzie Funk / New York Times : X: @fighto , @badassdogooder , @wolfiechristl , and @mckenziefunk X: Paul Shapiro / @fighto : An absolutely insane story. Should be made into a movie. https://www.nytimes.com/... Gentry Lane / @badassdogooder : Must read for anyone interested in data brokers and big data analysis. #natsec https://www.nytimes.com/... Wolfie Christl / @wolfiechristl : A drug smuggler helped create what are now entire divisions of data brokers like LexisNexis Risk, Thomson Reuters and TransUnion that serve both the US security state and private firms. Looking forward to reading @McKenzieFunk's book, fascinating excerpt: https://www.nytimes.com/... McKenzie Funk / @mckenziefunk : I suggest that everyone on the internet read the story I have out today in @NYTmag. I've been working on it for six years...(OK, it's a book excerpt. More on that soon). Gift link: https://www.nytimes.com/...
Context & Ripple Effects
The profile places data fusion in the longer evolution from collecting personal information to combining and reusing it across commercial systems. Earlier coverage documented that personal-data collection and resale long predated modern ad targeting, making the significance here the infrastructure and methods that made disparate records more actionable.
That lineage also helps explain why data-intensive tools have become consequential beyond marketing: related coverage traced immigration enforcement’s use of big-data systems and examined privacy-law compliance weaknesses around data-access requests.
First-order effects
- The account gives current data-broker and risk-information businesses a clearer historical provenance, tying the privacy impact to data fusion rather than to any single consumer platform.
- It sharpens scrutiny of firms associated with large-scale identity and risk data, including LexisNexis Risk, Thomson Reuters, and TransUnion, because the value of their products depends on linking records across sources.
Second-order effects
- Privacy debates shift toward the risks created when individually routine datasets are combined, increasing pressure on buyers and vendors to explain data lineage, permitted uses, and identity-matching practices.
- Compliance processes can become a weak point: as prior coverage found, some privacy-request workflows relied on insecure verification or outside providers, so broader access and deletion rights may not by themselves limit downstream fusion.
Third-order effects
- If this pattern persists, privacy governance will increasingly need to regulate inference, linkage, and downstream reuse—not only the initial collection of data.
- The market may further divide between organizations able to assemble proprietary identity and risk graphs and those constrained to narrower, more auditable datasets; the scale of that shift depends on enforcement and disclosure requirements.
The trend: Data privacy is moving from a focus on collection and consent toward the power—and accountability—created by combining data across systems.