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Data Congruency: The Quick Fix for Rectifying the Industry’s Continued Disregard for Science

In an increasingly competitive global economy, companies continue invest heavily in data analytics as a tool to win business. Whether the interest is to learn more about customers, or how to improve operational performance, data lines the the heart of solving the problem. While other corporate departments dominate the use of data analytical resources, in-house legal departments often go left behind. At the core of many discovery requests are: (1) email, in a structured data environment; and (2) electronic documents, typically in an unstructured environment. Simply put, structured data requires know-how on how the software manages the data within it, and unstructured data is comprised of loose documents stored nearly anywhere that data can be saved. Unfortunately, legal departments are often considered to be a cost center within a business and are left battling for budget. Some businesses have had the opportunity to acquire electronically stored information (ESI) tools to use in their discovery of mailboxes, documents and to help preserve data. Using review tools like these in order to maintain market position have added analytic engines (predictive coding) to their platform software while others may even offer to process data for free within the software. To date, however, no tools are readily available on the market solve the root complexity of data discovery, which is information governance with an emphasis on ESI discovery. Despite corporations’ continued efforts to converge upon the outside counsel base, pricing models for ESI services encourage misaligned interests.

By Matthew Blake, SVP Discovery & Forensics, OmniVere  
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In an increasingly competitive global economy, companies continue invest heavily in data analytics as a tool to win business.  Whether the interest is to learn more about customers, or how to improve operational performance, data is at the heart of solving the problem.  While other corporate departments dominate the use of data analytical resources, in-house legal departments often go left behind.

At the core of many discovery requests are: (1) email, in a structured data environment; and (2) electronic documents, typically in an unstructured environment.  Simply put, structured data requires know-how on how the software manages the data within it, and unstructured data is comprised of loose documents stored nearly anywhere that data can be saved.

Unfortunately, legal departments are often considered to be a cost center within a business and are left battling for budget.  Some businesses have had the opportunity to acquire electronically stored information (ESI) tools to use in their discovery of mailboxes, documents and to help preserve data.  Using review tools like these in order to maintain market position have added analytic engines (predictive coding) to their platform software while others may even offer to process data for free within the software.  To date, however, no tools are readily available on the market solve the root complexity of data discovery, which is information governance with an emphasis on ESI discovery.  Despite corporations’ continued efforts to converge upon the outside counsel base, pricing models for ESI services encourage misaligned  interests.

 ESI discovery must align with quantitative analytics in order to align interests and build congruency.

 Within information governance there are many complex moving parts to managing any size business.  Moreover, business activities add to the complexity of information governance, namely finance.  Certainly a business can manage its own information but when you lever inorganic growth strategies you can quickly run into risk models you may not be accustomed to recognizing.  To be specific, when Daimler AG decided to acquire U.S.-based Chrysler to the mix in order to dominate the global automotive industry as DaimlerChrysler, data analytics could have possibly played a part in the due diligence process.  In 2010 the matter of U.S. v. Daimler AG, 1:10-cr-00063 (D.D.C. 2010) Daimler AG paid $93.6 million in fines on top of fees spent on Skadden, Deloitte and Franklin Data in order to settle claims against them.  Although issues like FCPA can cloud the deal making process, applying some quantitative analytics to the organizational data with an emphasis on revenues can perhaps shed light.

 So how does a business align themselves more closely with outside counsel and ESI experts?  It can be argued that adding a data scientist to the organizational chart and moving discussions towards the center of the business and its data could help the fix.  Instead of jockeying for budget, most businesses already have a qualified data scientist to add to the team.  While meeting rooms filled with IT managers across a business’ continuum remains ideal, adding a data scientist will help provide key insights as to what directions are most statistically sound. In developing a custodian list, for example, the additional review of data scientists should help tighten, and avoid unnecessary employee removal and subsequent over-collection.

 An examination of the tools available to ESI consultants today reveals that they’re mainly consisting of collection, processing, and document review tools. Some platforms contain analytic engines licensed by OEM developers. Furthermore, take a look at the team supporting your ESI requests at the ESI vendors business – while they may be solid drivers of licensed and/or proprietary tools, are their interests aligned with yours? Instead of initiating discovery efforts with the old mantra of “let’s collect and preserve now,” try challenging and insert a reasonable quantitative process that will act equally as fast with internal and/or external data scientists to compute modeling efforts that support your qualitative decision process.

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 Some of the skills should combine applications in mathematics; statistics; and powerful, high-speed numerical computing methods to design, develop and deploy valuable solutions in the competitive ESI market. In addition, bulding a robust quantitative analysis foundation to explore multiple ways to address difficult modeling and analysis problems. Learning how to reach “correct” real-world solutions based on complex models with database-driven back ends and to devise mission-critical quality solutions.  Platforms where data scientists currently can be found are:

  • Financial data and business modeling using Microsoft Excel
  • MATLAB and SCILAB
  • Data Mining Using Structured Query Language (SQL)
  • ERwin Data Modeling Essentials
  • Toad for Data Analysts
  • Using SAP BusinessObjects

As we charge forward into 2015, corporate legal departments seeking congruency between outside legal counsel and ESI discovery experts begins with changing the organizational chart to become a more data-centric organization.

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