Full Data Visibility Facilitates True E-Discovery

Full Data Visibility Facilitates True E-Discovery

Timothy V. Dix, CEO, Xpriori, LLC, a service provider at the marcus evans Chief Litigation Officer Summit Spring 2015 and Fall 2015, on how Xpriori’s economically viable and automated technology enables classification of data resulting in the elimination of data/documents that contain no actionable intelligence and assures delivery of the right information.

Interview with: Timothy V. Dix, CEO of Xpriori, LLC

Chicago, Illinois, March 3, 2015 - FOR IMMEDIATE RELEASE

“The only way Chief Litigation Officers (CLOs) can assure they have all the information necessary for litigation discovery is to deploy technology that automates consideration of all documents and identifies those that contain little or no useful content. The purpose: By reduction, identify all potentially relevant information, deal with potential risks, implement defensible culling, and provide all potentially usable information. We can do this with a largely automated process. The result: Better results and lower costs,” says Timothy V. Dix, CEO of Xpriori, LLC.

“If data classification is done as part of a holistic enterprise strategy, then support initiatives, such as for e-discovery, will more likely provide the right information on a timely basis and without overlooking any smoking gun document,” he said.

Xpriori is a service provider at the marcus evans Chief Litigation Officer Summit Spring 2015 and Chief Litigation Officer Summit Fall 2015.

Tell us about the recent Lutzier v. Citigroup Inc. court case.

In this case, the Defendants had agreed to produce 46,217 documents that were identified as part of a set resulting from searches on a set of keywords. The results produced had no indexing or organization. In fact, as the Plaintiff argued, the searches actually identified 555,909 documents. The Plaintiff wanted to see them all. Without citation of any other facts, the Court was presented with a Hobbesian choice: order production of one set or the other. Actually each of the sets was the result of guesses that the search on the specific keywords would produce the documents requested.

Had the documents been classified by Xpriori, the parties and the court could have had a decision based upon consideration of the content of all the documents -- not some arbitrary number of returns on the keyword search. The basis for the court’s decision: Fear of cost and, likely, a misguided sense of proportionality rather than the content of all documents. Justice?

Could the body of documents been analyzed more efficiently?

With our current and field proven approach, all 555,909 or more could have been considered; junk eliminated; and the remainder quickly and consistently organized for efficient review. All of this done deploying highly automated process. The documents would have been clustered automatically according to text and visual similarity. The clustering is automated, operating without human intervention, but fully defensible and fast. Once done, the clusters’ contents really define an organizational structure; contents are available for further drill down and review. Our experience is that large percentages of documents are properly culled without the need for human review.

Where these solid Information Governance (IG) classification practices are deployed, the substantial disruptions to operations, incomplete responses, and high costs of e-discovery are avoided. Also, it is far less likely that any “game changer” document will be missed.

Can this work be done in smaller chunks?

Yes. What has been learned in an initial process can be carried over to newly introduced information – a current project or a new one. What has been learned from the examination of the clusters helps refine taxonomies that control the storage. No need to classify all enterprise information before executing on a discrete e-discovery project. You can build from there.

Any final words of advice?

Consider e-discovery to be a subset of IG. Strong IG practice starts with a classification that will facilitate review of information in smaller bites. To avoid false positives, avoid searching on vast quantities. Searches work better on sets of documents that are organized to a degree of homogeneity. Now the automated classification significantly reduces the amount of discrete or manual review required. Better results at lower cost.

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About Xpriori LLC

Xpriori is a privately held company headquartered Colorado Springs. The Company offers proprietary technologies, products and services for the Information Governance (“IG”), eDiscovery, compliance and review markets as well as for XML-based data management. Xpriori services are based upon automated advanced Textual and Visual Similarity Clustering technologies, as well as OrcaTec Document Decisioning Suite (TM) and other proprietary. Deliverables include sustainable day forward procedures and methodologies deployable into a set of scalable, automated processes to handle newly added data as well as legacy information. See Xpriori at www.xpriori.com; 01-719-425-9840.


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