To make data driven decisions, public and private investigative agencies, especially in legal, fraud detection investigations, compliance and governance issues, are overburdened by record amounts of investigative requests. Data is growing faster than ever and by the year 2020, about 1.7 megabytes of new information will be created every second for every human being on the planet. The document reviewing process is both time consuming and expensive. These organizations seek more efficient methods to analyze data given the cost associated with people sifting through documents. For example, the most efficient lawyers can sift through 80 documents in an hour, but this process is prone to mistakes due to fatigue, etc. Digitization has given rise to opportunities for e-discovery solutions. Companies like Brainspace are developing software and machine learning tools that augment intelligence of public and private investing agencies to reduce the amount of time of reviewing documents to make data driven decisions.
Brainspace analyzes structured or unstructured data to derive concepts and context. Using visual data analytics, it then engages human interaction to refine a search for maximum relevancy. Brainspace software is able to learn at a massive scale (1 million documents in 30 mins) and the process is entirely transparent in which a user can see and interact with the machine learning.
By using Brainspace, productivity is enhanced due to interaction between the machine and human. Brainspace is better at ingesting, connecting and recalling information than humans, while humans are better at using information to reason, judge and strategize than machines. For example, after the platform organizes the unstructured texts into concepts, humans could filter based on a concept and weight “suggested contexts” to curate relevant search results.
Brainspace is differentiated versus other text searching algorithms in that it searches to recognize concepts not just text. The software “reads between the lines” as humans do, except that it can handle thousands of pages instantly. The software is dynamic and unsupervised utilizing no lexicons, synonym lists or ontologies.
Effectiveness and Commercial Promise
The market opportunity and applications of Brainspace’s leading machine learning software are enormous, though the first application has been in the investigative industry. One can imagine the immense savings associated with document reviewing costs. Other research-heavy applications include legal e-discovery, fraud detection investigations within financial-services organizations and compliance or governance issues.
For example, remember Enron? As a demo, Brainspace imported 450 million Enron documents, and traced the emails about an offshore account. It took 5 clicks and less than a minute to nail down the executives involved. By hand, it would have taken lawyer’s 6 months!
Brainspace software has many applications, but they have needed to partner with other companies to deliver a usable product to a customer. For example, Brainspace partnered with Guidance to deliver a product called EnCase. The Guidance software augments the Brainspace deciphering algorithm by providing protection against hacking and external threats. The product is used in the investigative industry to provide auditing capabilities critical to large-scale investigations.
A potential competitor to Brainspace that we identified is Graphistry, which similarly takes data, quickly understands the results, and automatically visualizes the data to the user via graphs and cloud technology. Graphistry may have better visualization software for analysis by a human user, but we believe that the patented deciphering technology of Brainspace is more powerful. Building upon the software to improve the user friendliness of Brainspace is a potential opportunity.
Brainspace could apply its context deciphering technology to online news agencies or social media sites. For instance, it could analyze twitter feeds and be a more reliable protection against “fake news” generated by underperforming algorithms. Perhaps CFO’s at companies could implement the software to automate financial packets. The software may be able to derive trends in financial statements, and derive context for those trends from access to the company email database.
Brainspace’s technology can also be very useful for the sensors revolution that is taking place, creating enormous amounts of information. Brainspace could use its expertise in analyzing structured and unstructured data to create insights from sensors data which can provide immense value for firms that use this data to monitor real-time systems.
Posted by: Dhruv Chadha, Keenan Johnston, Ashwin Avasaral, Andrew Kerosky, Akkaravuth Kopsombut, Ewelina Thompson