Mapping Corporate Networks With OpenCorporates

I was due to be at #odw13 today, but circumstances beyond my control intruded…

The presentation I was down to give related to some of the things we could do with company data from OpenCorporates. Here’s a related thing that covers some of what I was intending to talk about…

(I’m experimenting with a new way of putting together presentations by actually writing notes for each slide. Please let me know via the comments whether you think this approach makes my slidedecks any easier to understand!)

PS Interesting take on mapping BP corporate network using OpenCorporates data et al by OpenOil: Mapping BP – using open data to track Big Oil Introduction.


  1. Paul Geraghty

    For one fantastic moment there I really thought you were going to bring the 2 strands of this together, try and find company directors who followed each other on twitter – and followed official twitter feeds of companies in order to predict the emergence of new companies, or good/bad financial results or derive some kind of intelligence.

    But of course, it would mean the verification of twitter users, still, follower of Corporate feed A, Mister B could be taken to be the same Mister B who is a director … of Corporation A or one of its competitors.

    If I was in the stock market specializing in a particular sector I’d be sitting up and paying attention to this stuff.

    Fantastic work, thx for sharing.

    • Tony Hirst

      @paul (oops, I hadnlt intended to imply that tease… It’s a good idea, though:-) I think Linked In could be a great source of corporate structure info, but the data is hard to get out…

      A recent paper on “Organization Mining Using Online Social Networks” [ ] shows how crawls of social network data can be used to sketch out the structure of roles within a company. My own dabblings with Twitter suggests that many Twitter bios don’t reveal employer, suggesting Twitter is more of a casual social space?

      What I tend to use the Twitter maps for is mapping out “interest space” around a sector, as well as the identification of “people of interest” in that space.

      I’ve posted about the scrapers etc used to generate these networks. When I get a chance I’ll annotate the post with links.

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