SUCCESS STORYIndustry• Global corporation

Solution• OpenText proprietary technology-assisted
review based on continuous active
learning (CAL)

Services• OpenText Managed Services

Results
Found 94 percent of relevant
documents, looking at very few
nonresponsive documents
Completed highly responsive
collection with changing
review criteria
Saved £320,342 (€366,760) in
review costs by using OpenText
technology and review teamGlobal corporation meets tightdeadlines with high-efficiency review
Company facing federal investigation meets tight deadlines, cuts
review costs with OpenText Technology-Assisted Review and OpenText
Managed Services“The OpenText review
team achieved a remarkable
review efficiency andlooked at very few
nonresponsive documents.”

Global corporation meets tight deadlines with high-efficiency review

A global corporation faced an investigation by a government
agency into alleged price fixing. The regulators believed theywould find the evidence in the documents and issued a broad
request—with just four months to get the job done.
This was not a case of finding a needle in a haystack. Rather, a wide
range of documents were responsive. A sample of the initial collection
suggested that as many as 45 percent of the documents would beresponsive. One option was to produce everything, but the client hada significant amount of confidential and proprietary informationthat it did not want to be inadvertently exposed. Thus, they needed
to produce responsive documents, but only responsive documents.
The corporation turned to OpenText proprietary technology-assisted
review based on continuous active learning (TAR 2.0) andOpenText™ Managed Services to find the relevant documents asquickly and efficiently as possible. Overall, the team achieved
94 percent recall, far greater than that required by the courts.
It did so having reviewed only 30 percent of the collection, meetingthe corporation’s deadline with room to spare. Additionally, review
costs remained substantially lower than they would have beenwith linear review or keyword search.
The managed review team used CAL from the beginning, without
the need for a senior lawyer subject matter expert (SME) to review
thousands of documents to train the system. Rather, the team got
started right away using the responsive documents already identified
for initial training with no time to waste.

The measure of a predictive review is how quickly the algorithm cansurface relevant documents. OpenText TAR 2.0 technology pickedup the trail almost immediately.
There were almost 5,000 batches in this review. Each blue line
represents the percentage of relevant documents in the batch.
In the early stages, the number reached 80 to 90 percent.
Reviewers stayed as efficient as possible to keep review costs
as low as possible.
When keywords are used to cull documents, reviewers typicallyhave to look at as many as nine nonresponsive documents for each
responsive one. That means they are wasting their time for about 90
percent of their review efforts. With OpenText TAR 2.0 technology,
statistics show the ratio is much narrower, about 2:1, which means
the team finishes faster and bills less.
In this case, the team achieved review efficiency of 1.33:1—
remarkable efficiency achieved by the combination of the TAR 2.0
engine plus reviewer expertise. In fact, the chart shows batches
quickly approached 100 percent responsive and some batches
were 100 percent responsive.
The team reviewed 600,000 documents in all and garnereduseful results.

“The average teammember looked at just133 documents to find
100 responsive ones.”

Global corporation meets tight deadlines with high-efficiency review

Cost savings

Change adjustment

Toward the beginning of the review, the client’s outside counsel
conducted a quality control (QC) review of 20 percent of thedocuments. Seeing the exceptionally high quality of the review,
it reduced the QC review to just 2.5 percent. This included review
of 35,000 potentially privileged documents and drafting 10,000 entry
privilege logs.

The OpenText TAR 2.0 algorithm is noise tolerant, which meansthat coder inconsistencies—and even changes in direction—do
not adversely affect it. With CAL, every ranking starts fresh, withno memory of the previous one. That way, if tens of thousands ofdocuments were to be retagged, the next ranking would take it instride. The same is true as the team refines its search objectives.

Over the course of the review, 15,970 documents were checked byoutside counsel and only 848 were changed (95 percent accuracy)
and almost all were changed during the first two weeks of the review
as OpenText used its QC workflow to ensure that coding was alignedwith that of outside counsel.

Adjustments happened several times in this case as understanding
increased. Yet, the team successfully navigated the multiple
major changes to the review protocol with no adverse impact
on performance.

Deadline adherence
The OpenText team needed to complete review of 45,431 documents
to meet a two week production deadline for the initial set ofdocuments. It met the deadline with no problem, working throughtwo consecutive weekends.
As new due dates arose following the initial production, OpenText
doubled its team to more than 40 reviewers to meet deadlines.

Also, just three days before the final production deadline, the teamwas tasked with reviewing contact lists for the 15 most importantcustodians. It reviewed over 10,000 entries in the short turnaround.

About OpenText
OpenText, The Information Company,
enables organizations to gain insight
through market leading information
management solutions, on-premises orin the cloud. For more information about
OpenText (NASDAQ: OTEX, TSX: OTEX)
visit opentext.com.
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