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“Graph databases excel at spotting data relationships at scale,” says Emil Eifrem, CEO of Neo4j, a graph technology company whose products are used by the ICIJ. The ICIJ also uses machine learning to try and classify documents into broad clusters, helping differentiate, for instance, between documents related to the creation of a company, or a personal letter, or a duplicate of other documents.
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“That’s incredibly helpful, because then the information is already structured, and you can export the results in CSV into any spreadsheet software and go through the results,” says Reuter.
The ICIJ has developed a tool that allows people wanting to interrogate the documents to supply a list of names or different queries in CSV format that are cross-checked against the metadata in the documents itself. One of the ways Datashare manages to pull out those lists of names is through batch searches. The data Tika extracts is then ultimately accessed through Datashare by the end user. “Tika can handle 50 or more different documents,” says Thomas. Apache’s Tika Java framework was used to extract text from all the documents.
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Datashare parses all the documents, including scanning PDF files through optical character recognition (OCR) through Tesseract, an open-source system. Images, of which there were 2.9 million, are even more complicated to analyse computationally. Emails, PDFs and Word documents are more difficult to search for data.
Those structured files are far easier to handle and interrogate.
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“They can download documents to their own machine, but they have to use Datashare to search the documents because it’s not doable to go through 11.9 million documents without the system.”ĭatashare was vital because just four per cent of the 11.9 million files the ICIJ received as part of the Pandora Papers were ‘structured’ – that is, organised in table-based file formats such as spreadsheets and CSV files. “Everyone has to use Datashare to explore the documents,” says Reuter. Extract is part of a larger ICIJ project, called Datashare, which is a data structuring tool. “When you have millions of documents, Extract is able to tell a server to look at one document and another server to look at another,” Romera says. One, Extract, is able to share the computational load of extracting information between multiple servers. The ICIJ used two self-developed technologies in combination to comb through the documents. Some members of the ICIJ team met directly with sources and collected huge hard drives containing the documents. “We exchanged for weeks and months with the sources, and at a point we had to find a way to get the data.” Initially, the ICIJ brokered a deal with its sources that would allow them to send the data remotely without needing to travel, but as the size of the document dump grew, so did the challenges in ensuring it all could be sent to a secure server. “The first challenge for us was to get the data,” explains Pierre Romera, chief technology officer at the ICIJ. Work began on analysing the data in November 2020. “We had data from 14 different offshore providers,” says Delphine Reuter, a Belgian data journalist and researcher at the ICIJ. And it involves a lot of technical infrastructure to bring the stories of financial issues to light. The organisation behind the Pandora Papers leak, the International Consortium of Investigative Journalists (ICIJ), has spent the best part of a year coordinating simultaneous reporting from 150 different media outlets in 117 countries.
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