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Data Discovery Tools

June 8, 2020

by Jeffrey Hewett, JD

Granite has released SERAReview - a data review process and tool set.  SERAReview is the result of our team investing a lot of effort, thought, time and money.  Why would we do such a thing?

Our project experience over the last 10 - 15 years has allowed us to observe a shift in corporate work tasks.   Previously, documents were the primary corporate communication means, work product and discovery sources within large organizations.  Now our large litigation clients have moved beyond boxes, file drawers and electronic documents stored on networks and local drives.  Employees work with discrete data elements required for their positions, entered into workflow systems, which contain the whole of the process information set.  And most workflow processes do not routinely include the generation and retention of documents.

Unfortunately, legacy discovery tools remain document focused.  From enterprise database systems, discovery teams frequently request report generations - or new report creations - which, being workflow process based, rarely contain the broad data set needed to meet litigation requirements.   The discovery team may transform exported data sets into Excel charts, for document based review within existing review systems.  Large column and row based data sets transformed to documents for review and production represent a challenging task.


Avoiding the document metaphor and working with the underlying data presents a quicker path to identifying relevant and responsive information.  Data is inherently more flexible than documents.   Filtering for depth ( row volume) and breadth (column counts) may be done with greater specificity and accuracy.  Reviewers may be presented with a focused information set, enabling greater review process speed and accuracy.  Data review is a different process - yet done right, requires fewer resources, less time and therefore less money to complete.  We're excited to offer SERAReview as the first step in new discovery solutions for large data problems.

So, either that or we're really confused.  Let's go find out together.  :-)

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