Editor: Mary, what is the most significant eDiscovery development affecting corporate ESI?
Mack: The pendulum is swinging hard in the direction of reduced costs for eDiscovery. We can see this taking shape in the form of “self-help” initiatives by corporate counsel and CIOs; law firms rethinking their approach to eDiscovery models (including things like pricing and machine-assisted coding); and new initiatives among the courts, the Rules Committee and even Congress on the need for cost reform.
Editor: Why was the House Judiciary Subcommittee on the Constitution’s hearing so important?
Mack: Rather than tackling eDiscovery head on, the House Judiciary Subcommittee on the Constitution’s hearing on the costs and burdens of civil discovery was the biggest surprise because it deferred to the Judicial Conference Rules Committee in its effort to develop amendments to the Rules of Civil Procedure.
Yet the hearing was important because it focused on the great need for federal rules changes to address over-preservation, which is so vexing to corporate IT departments and legal departments. I noticed that in your March issue you published the testimony that GE’s Tom Hill submitted in connection with that hearing about the cost impact of over-preservation. Patrick Oot* gives a great overview of how the Federal Rules of Civil Procedure can be amended, and Tom Allman* is also writing publicly on the rules changes.
Efforts are underway to address the over-preservation issue. Bob Owen* has a preservation reset proposal that he is popularizing as part of the effort to obtain rules changes.
Editor: Technology-assisted review and predictive coding as espoused by Judge Peck have also been in the spotlight recently. What are your thoughts on these developments and their impact on eDiscovery efficiencies and costs?
Mack: First, I support Judge Peck’s judicial management toward reducing costs, speeding trials and encouraging cooperation. Judge Peck is one of a handful of jurists who understands and can communicate about computers, and who is willing to help educate the bar. The attack on his ethics is unwarranted as well as unwelcome in the eDiscovery community.
It is unfortunate that “predictive coding” is a loaded term now associated with one vendor. In this predictive coding model, a seed set is necessary. However, with the seed set protocol, counsel can find themselves forced to disclose the seed set(s) of documents fed into the software – even the nonresponsive documents.
This is a big change and has the possibility of broadening the scope at an early stage instead of narrowing the scope. It assumes a high degree of cooperation by the parties, or a lot of judicial management. In the protocol discussed, there were multiple bites of the apple envisioned, and, still, the plaintiffs took issue because they didn’t trust the effectiveness of the technology, didn’t want to pay for what they considered needlessly expensive technology and didn’t like the sample size for sampling the nonresponsive documents.
Rather than approximately 2,399 nonresponsive documents, the plaintiffs felt statistics entitled them to 16,555 documents. These arguments are taking place, not at the end of a production after a challenge, but during the very early cycles of seed sets, training, testing, validating and getting consensus between opponents on what is “accurate” and what is “enough.”
ZyLAB has a different approach to “predictive coding.” We use machines to reduce expenses, where the methodology is transparent and the nonresponsive documents do not need to be produced (unless, of course, the court ordered it.) It is rules based. For example, if a person sent or received communications or is talked about during a particular time-frame, between certain dates, or if a date is mentioned in the documents, those documents could be coded as responsive. Variations in spelling and email nicknames can be automatically coded as new documents are added to the mix. It doesn’t require waiting for the whole group of documents that you are reviewing to be re-indexed.
eDiscovery attorneys are looking for something a little more accurate, and that is the approach that ZyLAB is taking. We provide random sampling tools to validate that what is left behind is appropriately left behind. I expect to see in the next year that a random sampling of what is left behind will be the most significant contribution to cost savings for corporations. This will allow them intelligently to exclude custodians, which is one of the most significant ways of reducing costs.
Editor: Describe the merits of qualitative versus quantitative Early Case Assessment (ECA).
Mack: Quantitative ECA takes a broad brush over the data and gives you numbers and can reduce a data set, whereas qualitative ECA actually helps you find the documents you need to prove or settle your case or to argue proportionality.
Quantitative eDiscovery tells you a lot about how much data you have. It is a great tool for budgeting, especially for organizations that are still using linear, page-by- page review at attorney hourly rates. Qualitative ECA is more suited to fact development and investigation. The legal team winds up with far more insight early on, which can reduce costs by accelerating the settlement process or reducing the scope.
The qualitative approach provides risk-reward valuation and answers proportionality inquiries. It selects documents based upon whether they are related to other documents or the quality of the document, for example, the ratio of hits to the size of the document. It looks at and indexes everything, including PDFs that may not have text in them – perhaps only a picture for security purposes. It takes into account technical documents like the autocads used by the auto manufacturers, chip makers and the airline industry that are the blueprints they use when they are designing a product.
Editor: Why is the ability to process such nonstandard items so important?
Mack: Many of ZyLAB’s clients choose us because we are one of the few vendors that can handle drawings that are common for IP/patent portfolios, environmental and product liability cases. We can index foreign languages in the right way: for example, if the language is supposed to go right to left and English goes left to right, we don’t force the language in the direction of English when we index it.
There are many reasons to use our software and services. Some may have a specific need to find relevant schematics. Others may need automatic redaction of personally identifiable information, such as Social Security numbers. This results in double savings because it is both a huge time saver as well as an hourly rate saver. We have a chart that shows how much money can be saved for the number of redactions you would anticipate.
Many of the clients that use our technology are government entities. We are currently getting more inquiries outside of the litigation area triggered by data breach laws. Many organizations want to proactively redact their information to avoid inadvertent breaches.
In the ZyLAB Production Module that we have just upgraded and released, we give the corporation or their attorneys the maximum flexibility available. For example, they might want to produce everything in TIFF. However, the government might say that is fine for commercial litigation, but we would like to see your Word or Excel documents. Our software allows that flexibility so that our clients can negotiate on the merits of the case rather than on production specifications.
Editor: How can inadvertent disclosure of trade secrets or other confidential information be avoided?
Mack: Many clients are using our technology to proactively monitor sensitive documents to avoid data seepage and loss of trade secrets. This is an enormous cost savings and also helps protect the corporation in this era of the FCPA, UK Bribery Act, Dodd-Frank and whistleblowers.
ZyLAB’s data mining allows clients to quickly and automatically catalog the appropriate security or confidentiality designation for a document. The integrated production allows images and native files to be branded with the appropriate designation to protect the information. Using the ZyLAB rules-based approach, it is much less likely our clients will need to disclose a sample set of nonresponsive documents.
Editor: Please discuss the emergence of hybrid configurations for eDiscovery systems that combine in-house data control with external storage and deployment services.
Mack: Many of our corporate clients are purchasing in-house systems. There are two scenarios: the first is that they have a big case right now and they have purchased the software and they need to get started right away, or they wish to trial the system without a capital purchase. They will ask us to process ESI in the cloud for them, and then we will transfer the material to them behind their firewall once they have purchased and deployed the software.
The second model is where corporations decide they don’t want to own hardware anymore. They don’t wish to manage and upgrade it with all the work that entails. For those companies, we offer the convenience of a cloud-based system with the flexibility of configuration and client-directed prioritization of activity.
From a cost perspective, it is important to find an eDiscovery company that can support such seamless and cost-effective transitions from processing services, to SaaS, to on-premise.
Editor: So a company can maintain control of its ESI, which it has moved in-house, and still easily use all that material stored in the cloud or in-house using various types of services and SaaS?
Mack: Yes. This permits enterprise data that is related to multiple matters to be more effectively processed and reviewed once and reused. Automatic redaction, the reuse of work product, the random sampling and our search technology are the pillars of cost savings for our clients.
Editor: Tell us about intelligent governance and proactive eDiscovery.
Mack: Our clients are repurposing the eDiscovery tools they previously purchased from us to respond to eDiscovery in order to take proactive measures. For example, clients may use the software to randomly sample data set aside for destruction due to retention policies. This proactive eDiscovery tool leads to intelligent governance and litigation readiness for the long term and attacks costs at the source: too much data that has no business or legal purpose. And it helps to resolve the issue of over-preservation we discussed earlier.
Editor: Do you provide a financial modeling tool for controlling other eDiscovery costs?
Mack: Not all investments are equal. We provide a financial model to demonstrate cost savings for less valuable activities (like attorneys drawing boxes and redacting by hand rather than auto-redacting). This allows money to be freed up for more strategic legal advice.
Editor: We have covered many topics. What is your role in helping our readers gain a better understanding of what you have discussed?
Mack: As an enterprise advisor on these topics, I can help them to better understand what corporations really need to consider today to do eDiscovery right and reduce costs.