For the past year, OpenOil has researched various aspects of open data on extractive industries. First we looked at corporate mapping – creating a network map of BP’s 1,200 subsidiaries across 84 countries with our colleagues at OpenCorporates. Then, with PWYP in Canada, we searched systematically for contracts disclosed by companies, which led to the contract repository, now in its third edition with over 700 full text oil contracts. We worked with Global Witness to develop a concept of open source financial models which has now been deployed in three countries.
But open data works on a kind of “network effect” – each new layer enhances the value of the others. So we thought it was time to try and put different layers together.
The result is the Tanzania prototype – what open extractives data might look like when it’s all put together.
These are early days and the prototype is firmly in the open data ethos of release early and often. So there will be lacks and bugs and mis-structurings – we beg your indulgence.
But the basic concept demonstrates how much is possible from data already in the public domain. Starting from a country survey, we find some 50 oil and mining operations which have significant data around them, whether it is reserves, production, contractual details or other. We have “normalised” some of this into a database, and left other parts of the data just as they appear in investor documents. What we have found is that government and even compliance-level company documents are far from perfect – but they are authoritative enough to be of interest to people trying to piece the different pieces together.
From a country-level overview, we zoom in on one mine – Bulyanhulu gold mine. At the center of the data tour is a financial model of the project, which has produced over three million ounces of gold since it started up in 2001 – but is yet to pay income tax.
Why is that? And on what basis do we predict revenue flows to government and investors? As usual, all documentation and sources are included, according to the principles of public interest financial modeling.
The model is the fulcrum of the data tour. We zoom in geographically, from country, to district, to mine, then swivel from geography towards money and zoom out again. First, to the corporate network of Acacia Resources, a spin off of Barrick Gold, which is spread across Africa, and finally to the corporate networks that operate the other mines and oil fields of Tanzania, together with their inter-connections and affiliate networks around the world. All documented, all sources one click away.
There are no earth shattering surprises in all this and no smoking guns. But what putting all the different layers together promises, when it becomes fuller, is: full and editorially independent cost-benefit analysis of these industries. Without fear or favour. There may be some vested interests that will be ruffled by this. Ultimately, we believe the balance of stakeholder interest, whether it is investors trying to understand the risks in their portfolio or local communities weighing disruption against economic advantage, is served by rich, interwoven, quality checked system-level data. That is what we and our colleagues hope to offer.