Nand Kishor Contributor

Nand Kishor is the Product Manager of House of Bots. After finishing his studies in computer science, he ideated & re-launched Real Estate Business Intelligence Tool, where he created one of the leading Business Intelligence Tool for property price analysis in 2012. He also writes, research and sharing knowledge about Artificial Intelligence (AI), Machine Learning (ML), Data Science, Big Data, Python Language etc... ...

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Nand Kishor is the Product Manager of House of Bots. After finishing his studies in computer science, he ideated & re-launched Real Estate Business Intelligence Tool, where he created one of the leading Business Intelligence Tool for property price analysis in 2012. He also writes, research and sharing knowledge about Artificial Intelligence (AI), Machine Learning (ML), Data Science, Big Data, Python Language etc...

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Harnessing the Power of Data: Q&A with a Chief Data Scientist

By Nand Kishor |Email | Jan 15, 2018 | 5238 Views

Bennett Borden, chief data scientist at the Philadelphia firm of Drinker, Biddle & Reath, discusses his take on the evolution of legal analytics.

In 2015, Bennett Borden became the talk of the legal community after he was named the first chief data scientist at the Philadelphia firm of Drinker, Biddle & Reath. He brought with him extensive experience with data analytics strategy, including groundbreaking research into the use of machine-based learning and unstructured data for organizational insight. Here, he discusses his unique background and take on the evolution of legal analytics.

Q: How did your background lead to your role as chief data scientist at Drinker Biddle?
A: My background is unusual. I started out in the intelligence community, spending several years at the CIA before going to law school at Georgetown. I was raised professionally in an environment where information was a resource to be mined and leveraged. At Georgetown, the e-discovery revolution was at its height, so my skillset of getting insight out of data was quite applicable. But frankly, I was immediately offended by the inefficiency of the e-discovery process. Putting humans in front of screens to look at documents to figure out what they are is the most inefficient way to get insight out of large volumes of data.

I set out to try to improve that process but purposely began to steer my career into a broader context than just e-discovery - that is, the idea of understanding human conduct by the digital trail we leave, with its implication on litigation, M&A, due diligence, even employee satisfaction and retention. I brought this entire information governance practice to Drinker Biddle. However, even then, I felt there were so many advances in analytics and big data that I needed to go back to school. So in 2013, I got a graduate degree in data science, and in 2015, we created the role of chief data scientist.

Q: What are the biggest challenges for law firms using legal analytics?
A: There are really two big challenges. One is on the inward-facing side. All of these tools and processes that allow firms to look at themselves help them become more efficient. The problem is the institutional conflict and tension between the need for greater efficiency and the old school billable hour model. The only answer is for that billable hour model to go away. I've been grateful for how creative and supportive our senior management is. We work from data-driven decisions to come up with more efficient pricing mechanisms based on outcomes, not just on time, so when we become more efficient, it benefits the client but doesn't carve our margins to nothing.

The other big challenge is that lawyers need to become proficient with the legal and ethical issues around Big Data in order to advise clients better. With the information we are gathering, we can do more things with that data than ever. But there are privacy and data ownership issues that have never faced legal challenges before because we've never been able to do it before. As society goes through times of great technology evolution, the law is always behind.

Q: What excites you most about the legal analytics evolution?
A: There are two things that excite me the most: One is that our ability to figure out what happened and why with absolute certainty and with unbelievable speed is unparalleled in our history as lawyers. There are massive strategic advantages to our ability to get the right information with certainty and with incredible immediacy.

Then, looking forward in time, being able to predict the future, that's where really cool stuff starts to happen. We spend so much time, especially litigators and investigators, fixing problems that already happened. Wouldn't it be great if we could predict when problems will happen and head off in the first place? My graduate work was based on how you can predict corporate misconduct, with predictive algorithms that highlight situations most likely to lead to some kind of misconduct and act to prevent it from happening. That's crazy cool.

Q: How do you see the future of the legal analytics landscape?
A:There are two main areas that law firms are really starting to wake up to. One is smart data-driven decisions about how they staff engagements - including how they price things. There is a lot of work going on to make law firms more efficient, and it's about time.

Then, there is applying analytics to legal tasks that machines are better at doing, such as legal research. The idea of having people look through cases and figure out what the law is based on reading a whole bunch of stuff is no different than 100 years ago - it makes no sense. These technologies are starting to help us look at cases, even at trial dockets, like how long it takes a particular judge to rule and which motions have succeeded. This is just starting to take off. It means we have to figure out a way to let machines do what machines do best, to capture value and to let humans do what humans do best. That tension still has to be resolved.

Source: Law.com