Regulatory Law gets SaaSy
Interview with Malcom Gray, Co-Founder of Libryo
Alongside our blogs, here, we thought it would make sense to establish a podcast dedicated to accelerating learning by interviewing data scientists, seasoned experts, thought leaders, and visionaries, all of whom are interested in sharing their experiences and lessons learned with others across the global data community. You can find the link to our podcasts below!
We recently sat down with Malcom Gray, Co-Founder of Libryo, a SaaS platform built to overcome regulatory complexity. Libryo provides an intuitive dashboard which lists all legal obligations per the needs of its customers, enabling individuals, working in any organization to understand legal obligations on a widespread level.
Below are four key takeaways.
1. Law is a complex data problem rather than a big data problem. Accessing data is primarily the first step in the process and legal data is not easily accessible at a global level yet as it is in very different formats and very different stages of development and digitization. Libryo is building many different types of solutions and aiming to cover from Artificial Intelligence to Indiana Jones. Datasets have got to be able to do everything.
2. Lawyers won’t be replaced, instead improved! There’s a lot of work that is done today by humans which is both a waste of time of human skill and capacity. It is a total waste of resource from a commercial perspective for business and so a lot of the heavy lifting can be done by the machines that Libryo build, which will then free up lawyers and many of the associates to focus on more high-value work. As the industry is evolving, it will unlock a huge amount of value because there’s a lot of latent potential and latent need within the broader community to access legal skills. But there are a lot of barriers to entry and access, however Libryo and the kind of solutions that can be built on top of the library of datasets will unlock a lot of that. So, if you are a lawyer, don’t be scared. Instead, it’s going to make you a lot better than you really are.
3. Build a data structure correctly with effective degrees of standardization. What makes Libryo data so unique is the way it’s structured and them being fortunate to pull the data sets post 2015–16 where they have been able to use a lot of the emerging solutions and that provides them the basis on which they are able to create a very flat data structure which allows them to build the kind of algorithms that can work with the data. If you don’t build a data structure correctly and don’t effectively create a degree of standardization, it’s impossible to use the data from multiple different jurisdictions in a way that is comparable and understandable.
4. The future could be as simple as “Libryo it”. Libryo believes in addressing a social justice issue — How do we make sure that the people know what the law is in the environment where they are, in the time that they need? This is still unknown in 2018. They predict that in time many people will build different applications understanding unique or niche problems on top of a Libryo dataset. Libryo will deliver the primary apps and the primary solutions to the market. They see many other people building solutions on top of their dataset that really is enriching and evaluated to niche and unique communities. This is how Libryo will remain relevant.
Be sure to check out the full audio, here:
Next up?
Key lessons from a panel discussion between:
Dr. Tony Jebara (Director of Machine Learning Research at Netflix)
Ilya Kirnos (Founding Partner/CTO at SignalFire)
Trent McConaghy (CTO at Ocean Protocol)
and, moderated by…
Mike Reiner (Venture Partner at OpenOcean and Co-founder at CITY.AI)
