Client Success

Lighthouse partners with a rapidly expanding pharmaceutical company to streamline its eDiscovery workflow and meet obligations more efficiently. What They Needed A large pharmaceutical client received subpoenas from several regulators. The subpoenas covered multiple product lines, implicated 60 custodians, and virtually all the company’s email. The client’s IT group identified over 35TBs of data requiring collection, processing, and review. Complicating matters further, the company had only 60 days to respond, well outside its estimated time of nine months to complete the project. Faced with this near impossible timeline, the client looked to Lighthouse for support. How We Did It Relying on procedures outlined in a jointly developed eDiscovery Playbook, Lighthouse’s data collection and forensics experts worked closely with the client’s legal and IT groups to implement a defensible strategy that greatly reduced the amount of data requiring collection. Experts from Lighthouse’s Advisory Services group worked with the client to implement a legal hold and data retention policy, customized to the various subpoenas. Lighthouse provided a unified review database, allowing outside counsel (who was responding to separate subpoenas) to leverage each other’s work product, greatly reducing review costs and preventing the inadvertent production of privileged and other sensitive materials. The Results Our combined efforts reduced the originally estimated 35TBs of data requiring review to less than 3TBs. By greatly reducing the amount of data requiring processing and review, the client saved significant review costs and reduced the estimated project completion time from nine months to only four weeks. Review cost reductions were achieved by leveraging Lighthouse’s project management team as well as the company’s proprietary suite of technology-assisted review offerings. These, and other efficiencies discovered during the project, have been implemented in future matters, continuing to drive down costs and increase value. Corporate Case Studyadvisory-services; big-data; case-study; collections; corporate; corporation; ediscovery; forensics; information-governance; investigations; pharma; privilege; privilege-review; processing; project-management; tar; tar-predictive-coding; technology-assisted-reviewediscovery-review; digital forensics; ai-and-analytics; information-governance; client-successAdvisory-Services, Big-Data, Case-Study, collections, Corporate, Corporation, eDiscovery, digital forensics, Information-Governance, investigations, Pharma, privilege, privilege-review, Processing, Project-Management, TAR, TAR-Predictive-Coding, technology-assisted-review, ediscovery-review, digital forensics, ai-and-analytics, information-governance

Lighthouse red flag report prevents proprietary data from being taken by departing employee. Key Actions A global company partnered with Lighthouse to create a proactive departing employee program to prevent data loss and theft. Lighthouse forensics experts prepared Red Flag Reports for every departing employee that fell within a specific category of employees. Each report outlined the risks associated with the departing employee based on a skilled forensic examination of their activity and data. Soon after implementing the program, a Lighthouse Red Flag Report alerted the company to suspicious activity by a departing employee indicating a high risk for data loss. Key Results Because of Lighthouse’s analysis and quick response, the company was able to: Prevent sensitive data from being disseminated outside the company. Avoid costly litigation associated with proprietary data loss. Reevaluate the departing employee’s severance package due to breach of contract, resulting in additional cost savings. ‍ What They Needed A global company was dealing with an increased risk of data loss and theft from departing employees. The company retains large volumes of proprietary data spread across their entire data landscape. Much of that data is also highly sensitive and would create a competitive disadvantage for the company if it were to end up in competitors’ hands. The company was also facing a higher volume of employee turnover—especially within roles that had access to the company’s most sensitive data (e.g., company executive and management roles). The company was concerned that these factors were creating a perfect storm for data theft and loss. They realized they needed a better system to catch instances of proprietary data loss before any data left the company. Company stakeholders reached out to Lighthouse because they knew our forensics team could help them build a proactive, repeatable solution for analyzing and reporting on departing employee activity. How We Did It Lighthouse forensics experts worked with the company to create a custom departing employee program for data loss prevention. With this program, Lighthouse experts prepared a Red Flag Report for every departing employee that fell within specified high-risk categories (e.g., employees above a specific seniority level, or employees that had access to highly sensitive company data, etc.). Each Red Flag Report was prepared by a Lighthouse forensics expert and summarized the data theft risk associated with the underlying employee. Every report contained: A high-level summary of the risk of data theft presented by the employee. A collection of attachments with highlights and comments by the Lighthouse forensics examiner (for example, a list of files stored in an employee’s personal cloud storage account, with an explanation of why that activity may indicate a higher risk of data theft). A forensic artifact categorization with associated risk ratings (e.g., if there were no suspicious search terms found during a scan of the employee’s Google search history, the examiner assigned that category a lower risk rating of “1”). Recommended next steps, with options for substantiating high-risk employee behavior. Reports were delivered to a cross-functional group of company stakeholders, including IT, human resources, and legal groups. The Results The Lighthouse program very quickly paid off for the company. Soon after initiation, Lighthouse escalated a Red Flag Report for a departing employee that showed a high risk of data loss. Specifically, the Lighthouse forensics examiner flagged that the employee had connected two different external thumb drives containing sensitive company data to their laptop. This activity was flagged by the Lighthouse forensics examiner as high risk because: The employee had already been directed by the company to return any device that had corporate data saved on it; and The employee had previously indicated that they didn’t have any devices to return. As soon as Lighthouse escalated the Red Flag Report, company stakeholders scheduled an interview with the employee. This interview resulted in the employee admitting that they had taken corporate data with them, via the two thumb drives. Because Lighthouse was able to quickly flag the employee’s suspicious activity, the company was able to retrieve the thumb drives before the proprietary data was disseminated to a competitor. The company was also able to reevaluate the employee’s severance package due to the breach of company policy, resulting in a significant cost saving. Even more importantly, the company now has a proven, proactive, and customized solution for preventing data loss and theft by departing employees—implemented by Lighthouse’s highly skilled forensics team. ‍ Corporate Case Studycase-study; corporate; corporation; forensics; investigations; collections; fraud-detection; red-flag-reporting; departing-onboarding-employeedigital forensics; client-successCase-Study, client-success, Corporate, Corporation, digital forensics, investigations, collections, fraud-detection, Red-Flag-Reporting, Departing-Onboarding-Employee, digital forensics

Self Service

A leading energy company gained the flexibility to use self-service technology and full-service expertise as needed, reducing costs and optimizing outcomes. Key Actions A multinational energy company sought eDiscovery efficiency and scalability A seamless combination of self-service Lighthouse Spectra eDiscovery and full-service Lighthouse consulting enabled them to meet a wide range of needs Minor matters can be addressed with low-cost self-service tools A full-service Lighthouse team applies in-depth review expertise to complex matters Key Results $50,000 year-over-year cost reduction 100+ hours freed for matter-critical work Flexibility to meet varying matter requirements Training improved speed and accuracy of self-service eDiscovery What They Needed A multinational energy company wanted to stop relying on an expensive patchwork of third-party eDiscovery providers and adopt a unified, cost-effective strategy. It sought transparent pricing and self-service access to the latest technology, including Relativity and Brainspace. At the same time, it needed a consistent team of experienced eDiscovery and review experts for more in-depth needs. How We Did It Lighthouse listened closely as the company described its desire for greater scalability and efficiency. We proposed a seamless combination of self-service capabilities on the Lighthouse Spectra platform and a dedicated full-service team for complex matters. This proven, flexible approach minimizes cost for minor matters while ensuring available capacity and expertise for complex projects. The Lighthouse Spectra support team accelerated onboarding through technical assistance and training. After completing a proof of concept, the client immediately began ingesting matters into Spectra. At the same time, we assembled a dedicated full-service team to be ready when needed. The Results Using the intuitive, familiar Lighthouse Spectra experience—incorporating Relativity and Brainspace functionality—the client rapidly discovered and reviewed data for internal investigations, subpoenas, and other minor matters. They no longer needed to license and manage Relativity and Brainspace separately, benefitting from a predictable, fixed-fee pricing model that fits their budget and scales to meet their needs. The Lighthouse team simplified data processing and exception handling, freeing resources to focus on strategic aspects of a given matter. As soon as a case warranted, they could triage it to the full-service team directly from the Spectra workspace. The result is a more responsive, cost-effective eDiscovery strategy, saving the company hundreds of hours and almost $50,000. Corporate Case Studycase-study; corporate; corporation; ediscovery; self-service, spectra; spectra; energy-industry; analyticsediscovery-review; client-success; lighting-the-path-to-better-ediscoveryCase-Study, Corporate, Corporation, eDiscovery, self-service, spectra, Spectra, energy-industry, analytics, ediscovery-review

Lighthouse goes beyond linear review to help a global technology company make its case to the IRS. Key Actions Targeting critical case documents with Key Document Identification rather than performing linear review on the whole document set. Identifying key events that took place within specific hours, by applying advanced linguistic modelling to overcome challenges presented by multiple time zones and different time stamp formats within email traffic. Key Results 1.5 million total documents reduced to roughly 37,500. Results in 100-500% less time and at 90-240% lower cost than linear review. Building a Case for Tax-Exempt Lunches A global technology company was facing IRS scrutiny over the complementary lunches the company provided to staff. Full-time workers were comped the meals because, the company claimed, staff were required to respond to emergencies during lunch hours. The IRS was dubious of that claim and inclined to consider the lunches a taxable benefit. To prevent the meals from being taxed, the company needed to demonstrate to the IRS that, over a two-year period, at least 50% of employees at its San Francisco office had in fact responded to an emergency between the hours of 11 a.m. and 2 p.m. local time. For evidence, the company had 1.5 million documents—mostly emails—pertaining to about 1,000 employees. The company reached out to Lighthouse for help finding the best case-building documents within those 1.5 million. Lighthouse offered its Key Document Identification service. Rather than prioritize documents for linear review, the Lighthouse team promised to identify the most valuable and evidential documents—and do so in less time and at a lower cost. Hacking Through the Haystack The Lighthouse team eliminated less-valuable documents in stages. First, they used an advanced algorithm to remove junk and duplicative documents, reducing the document set to 943,000 (a 38% reduction). Among those, the team targeted San Francisco employee names and emails, which brought the total down to 484,000 (an additional 49% reduction). From here, the team employed nuanced, multi-layered linguistic search techniques to zero in on the most necessary and informative documents. Along the way, Lighthouse encountered a number of challenges that would have thwarted other search tools and teams. One of these was the knot of different time stamps attached to emails: the last in time email in every thread was converted to Coordinated Universal Time (UTC), while every previous email in the thread was stamped according to the local time zone of the sender. The Lighthouse team circumvented this by searching the emails’ metadata, which converted all times to UTC. Using this metadata, the team was able to search using a single timeframe (6 to 9 p.m. UTC, corresponding with 11 a.m. to 2 p.m. Pacific). Another challenge was looping together all emails stemming from the same incident, so that Lighthouse could provide the company with a complete account of each emergency response (and avoid counting a given emergency more than once). The team did this by flagging one email tied to a specific emergency and using proprietary threading technology to propagate that flagging to all other emails associated with that emergency. Finally, the Lighthouse team had to classify documents by level of emergency, to help the company build the strongest case. The emergency level of some documents was already classified, thanks to a system installed by the company toward the end of the two years under investigation. But for the majority of documents, it was unknown. Lighthouse was able to classify them using advanced search features of proprietary technology, which identified key terms like “time-sensitive” and other ways emergencies were referenced in the document population. Major Savings and Critical Insights In only two weeks, a two-person team delivered on Lighthouse’s promise to help the company gather evidence, shrink the document population, and save time and money. Had the company tried to build a case with linear review instead, it would have taken up to 5 times longer and cost up to twice as much. Of the 1.5 million total documents, Lighthouse escalated approximately 37,500 (2.5% of the original dataset). To help with case building, the team sorted documents into three tiers of descending priority: employees responding to high-level emergencies during the lunch hour, employees responding to any level of emergency during the lunch hour, and employees responding to high-level emergencies at any time in the day. The Lighthouse team also normalized the metadata for all documents to make it easy for company counsel to see which employees were involved in each document and thread. Across the three tiers: 78% of San Francisco employees were tied to at least one document 74% were tied to at least one non-propagated document (i.e., an email associated with a unique emergency) 68% were the sender of at least one non-propagated document This strongly suggested that more than 50% of employees actively responded to emergencies in the target timeframe and helped counsel hit the ground running in collecting the facts to prove it. Corporate Case Studycase-study; corporate; corporation; ediscovery; fact-finding; document-review; investigations; kdi; key-document-identification; keyword-search; tech-industry; analyticsediscovery-review; ai-and-analytics; client-successCase-Study, Corporate, Corporation, eDiscovery, fact-finding, document-review, investigations, KDI, key-document-identification, keyword-search, tech-industry, analytics, ediscovery-review, ai-and-analytics

Spectra, Lighthouse's cloud-based eDiscovery software, saved a pharmaceutical company cost by managing eDiscovery for a third-party subpoena in-house. What They Needed Faced with yet another third-party subpoena, a large pharmaceutical company started to question how they could address these types of matters in a more cost-effective manner. Although sometimes larger in terms of data volume, these types of matters aren’t generally complex and commonly don’t require the expertise and oversight of an outside vendor to manage the eDiscovery process. This case, in particular, had a large data volume with a low dollar value, so the company wanted to explore options outside of the traditional vendor and outside counsel review and production process. How They Did It Lighthouse had been exploring the idea of Spectra, our cloud-based, user-driven eDiscovery solution, with this client for some time and this third-party subpoena seemed to be the perfect fit for their first run. Although the matter was a bit larger in nature, with over 150 GBs of email, it could easily be self-driven by the client’s in-house team of experts within the Spectra environment. To begin, the Spectra team onboarded the client’s team into the tool and provided training, documentation, and access. From there, the client kicked off the matter and uploaded all the documents into Nuix to be processed with the click of a button. Nuix then quickly processed this data and loaded the resulting documents into Relativity for review. Upon investigation of the resulting ~750K document set, the client decided that instead of taking the time to craft and test search terms to identify the potentially relevant files, they preferred to engage Lighthouse’s Focus Discovery team to further reduce and refine the files needing to be reviewed. As a first step, all documents were run through Brainspace to flag lesser included emails that could be removed from the review. Out of the 771,825 documents loaded to Relativity, 168,628 (or 22% of the population), were able to be removed from the review entirely. Next, the client sent Lighthouse’s Focus Discovery team a request for production as well as the subpoena to aid in the search term creation and optimization process. The Focus group worked with the client to create and then optimize the search terms until only ~5,000 hits (0.6% of promoted docs) were flagged for review. At this point, the client team was able to organize the review and review the documents to ensure privilege was considered. Finally, the ~250 relevant documents were produced inside of Spectra and delivered for service to the other side. ‍ The Results Overall, the client was not only able to save significant money on linear review due to a reduced data volume, but also on the traditional review process, as they did not have to outsource it and instead could run their matter in one easy-to-use solution, while accessing on-demand expertise of the Focus Discovery team. The experience thus far has been overwhelmingly positive and the client now has an easy-to-use, self-service solution for handling third-party subpoenas (and other similar matters) in a more cost-effective manner. ‍ ‍ Corporate Case Studycase-study; corporate; corporation; ediscovery; self-service, spectra; spectra; analytics; pharma; ai-and-analytics; processingediscovery-review; ai-and-analytics; client-successCase-Study, client-success, Corporate, Corporation, eDiscovery, self-service, spectra, Spectra, analytics, Pharma, ai-and-analytics, analytics, Processing, ediscovery-review, ai-and-analytics

Lighthouse analytics reduce responsive review by 80% and privilege review by 45%. What They Needed On behalf of a healthcare technology company, a law firm needed to review more than 3 million documents in 11 weeks under a Hart-Scott-Rodino (HSR) Second Request. Broad search terms meant more than 50 percent of the data population was potentially privileged. How Lighthouse Did It Reducing the Responsive Set and Identifying Privilege Lighthouse used a proven, court-approved technology-assisted review (TAR) approach to minimize costly and error-prone human review. Lighthouse technology quickly integrated Skype and Microsoft Teams chat data into the TAR workflow. Subject matter experts from outside counsel coded document sets to train the AI-powered models. Separate models were used to reduce the responsive set and to perform privilege detection. Once the models were trained, they rapidly and accurately analyzed the entire population of documents, achieving a recall rate of 76 percent and a precision rate of 79 percent. During privilege review, documents on which humans and models disagreed were routed to second-level privilege review. After this quality control step, Lighthouse applied its privilege log and name normalization software, helping outside counsel produce the final privilege log faster. Identifying Sensitive Data Lighthouse created regular expressions, which were used in conjunction with AI to find documents containing protected health information (PHI) or personally identifiable information (PII), which were then marked for redaction without manual review. Lighthouse AI technology also identified potentially toxic communications and flagged them for additional review before production. Results 80 percent reduction in the responsive set, eliminating 1.3 million documents 51 percent reduction in eyes-on review for PII/PHI Eliminated manual review of chat data Identified toxic communications for review before production, typically impossible in Second Request reviews 45 percent reduction in privilege review Identified 55 percent of privilege QC documents incorrectly coded by human reviewers 19,000 documents added to the privilege log efficiently and accurately using Lighthouse technology Law Firm Case Studycase-study; antitrust; ediscovery; tar; tar-predictive-coding; law-firm; hsr-second-requests; investigations; mergers; ai-and-analytics; ai-big-data; artificial-intelligence; ai; acquisitions; analytics; pii; phi; predictive-coding; prism; privilege; privilege-review; name-normalization; hipaa-phi; healthcareediscovery-review; ai-and-analytics; antitrust; client-successCase-Study, client-success, Antitrust, eDiscovery, TAR, TAR-Predictive-Coding, Law-Firm, HSR-Second-Requests, investigations, Mergers, ai-and-analytics, AI-Big-Data, artificial-intelligence, AI, Acquisitions, analytics, PII, PHI, predictive-coding, Prism, privilege, privilege-review, name-normalization, HIPAA-PHI, Healthcare, ediscovery-review, ai-and-analytics, antitrust