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The Legal Entity Identifier, Data Provenance and OBASHI

In the aftermath of the 2008 financial crisis, the finance industry is working on a number of regulatory initiatives, including Solvency II, Basel III and Dodd-Frank.

Fundamentally, the aim of these various initiatives is to reduce risk, be it insolvency risk in the EU insurance companies, liquidity risk in banking, or systemic risk across the U.S / global financial sector.

One of the contributory factors to the 2008 crisis is that there was no standardisation of the codes which financial institutions use to identify other financial institutions. 

This circumstance continues today, in that there is little code standardisation within each of the many individual financial institutions which, in combination, comprise the global financial system.  Each of these financial entities (for example, a bank) might have several identifying codes within different systems or across departments. 

As a consequence, across the global financial system, there is an inability to ascertain the exposure one institution has with another institution in a timely manner, leading to increased risk within individual legal entities and across finance as a whole.

A major problem when Lehman Brothers failed during the 2008 crisis was that counterparties were unable to quickly see or assess their exposure, and thereby understand the impact of the failure on their own organization.

In a bid to address this issue, the Financial Stability Board (FSB) was tasked by the G20 to provide it with recommendations on how to redress the inherent systemic risk within the finance industry.

In late 2012, the G20 endorsed the FSB’s view that a global system be put in place, the Legal Entity Identifier (LEI), which should uniquely identify participants to financial transactions.  Once successfully established and adopted by the finance industry, it will enable an entity’s exposure to be assessed, and systemic risk mitigated.

Here at OBASHI we have been working as advisors to the FSB LEI project.

Since inception, a guiding principle of the project has been that the global LEI system should support a high degree of federation and local implementation, under agreed and common standards.

In practical terms this means that each jurisdiction around the world can have one or more local implementations of the system to allow the local provision of LEIs, but each local implementation will act as a peer to other jurisdictions.  Each local system will update a central repository and other peer systems around the globe, creating a federated system.

In such a globally federated system it will be important to understand how flows of data interact with people, process and technology.  Understanding where data comes from, where it goes to, how it is changed and what it is used for will be critical to maintaining quality.

Understanding the data supply chain – the data’s provenance or lineage – will be key.

Data consultant Ken O’Connor uses an interesting analogy in a recent blog, where, in light of the recent ‘horsemeat scandal’ in Europe, he compares the consumer food supply chain with the data supply chain in business.

He argues that, just as we as individuals want clarity about the ‘provenance’ of our food so as to reduce risk, in business we should have clarity about the ‘provenance’ of data.

To explain the principle of ‘data provenance’, he uses the definition found in the book "Data Resource Simplexity", written by Michael Brackett,

“Data Provenance is provenance applied to the organisation’s data resource. The data provenance principle states that the source of data, how the data were captured, the meaning of the data when they were first captured, where the data were stored, the path of those data to the current location, how the data were moved along that path, and how those data were altered along that path must be documented to ensure the authenticity of those data and their appropriateness for supporting the business”.

Documenting such aspects of how data is used in the business is where the OBASHI methodology and software are very useful.

Our breakthrough modelling technology models how assets that enable and support data flow – people, process and technology - interact.

Using the OBASHI methodology, on a Business and IT diagram (B&IT) you map owners or stakeholders in the business, using “elements” to represent them within a horizontal Ownership layer.  Under each owner you map the owner’s business processes as elements within a Business layer.  The applications used to support those business processes get mapped under each process in an Application layer.  The operating System, Hardware, and Infrastructure assets are each mapped in a similar way in their respective layers. The initial letters of the six layers (Ownership, Business, Application, System, Hardware and Infrastructure) spell OBASHI.

The elements placed in the B&ITs layers represent the organisation’s assets (people, process and technology) and in combination with their position in the layers show their context of use within the organisation.  A wealth of contextual data, business, financial and technical, can be stored and referenced behind each element.

Once B&ITs have been established, you can overlay how data flows between the elements to create Data Analysis Views (DAVs).  This lets you model how the elements (the people, process and technology), link together for data to flow through them. 

A flow of data can start and stop at any element in any layer on the B&IT.   The element that starts flow we call a ‘provider’ and the terminating element in the flow we call a ‘consumer’.  This helps you understand both where the data came from and the use the consumer makes of the data.  Each data flow can represent a service or operation.  DAVs show how the data traverses the organisation, and how and why it is used.

With OBASHI in mind, if you re-read Brackett’s “data provenance principle”, you can understand how OBASHI helps provide the framework and guiding principles for mapping and understanding data provenance.

When the LEI system is up and running it will be used to identify any and every participant, in any and every financial transaction globally.  If it is to solve systemic risk within the finance industry the LEI system needs to be accurate, and data quality must be exemplary.

Set this into a global operational context of thousands of implementations, each jurisdiction conforming to regional legal and regulatory requirements, capturing data in multiple languages and scripts, and all of that being used to update data in every other local LEI system…and you can understand why data provenance within the Global LEI System will be critical to engendering the trust required to make it a success.

As advisors to the LEI project, we think OBASHI can help build that trust.

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