Techstars Companies visit Rochester Entrepreneurial Community for Techstars++ Partnership. Part Two: LiquidLandscape.

Part 1: Nebulab

 

Company: LiquidLandscape

Based in: San Francisco, CA

Techstars connection: Barclays Accelerator, Cohort #2, London.

Tagline: Start a conversation with your data.

 

The Problem:

LiquidLandscape Cofounders Margit Zwemer and David Lin “were both what you would call in the financial world, quants,” explained Margit.  In other words, they dealt with very large sets of data.  Margit was an algorithmic trader and detected patterns to make money in the financial market.  David worked in risk management.  “He’s on one of the teams that are trying to look at the positions across the entire bank, understand the overall risk, and make sure people like me don’t bring the wrong thing crashing down,” Margit explained.

The pair met as they both were transitioning out of the finance world.  They realized there were major advancements in big data technology occurring in the San Francisco Bay Area, where they both were located at the time.  Big data technology was being used in things like retail and online dating. 

But Margit asked, “Why didn’t we have access to these tools in finance?  Why was it so hard for these technologies to penetrate into these big institutions?”

“So we decided to try and solve the problems we had had with data in our old jobs, which is what led to LiquidLandscape.”

 

The Solution:

LiquidLandscape is a general purpose data technology that allows people to better test their hypotheses.  The tool enables users to take a data set, ask questions of that data, and get real answers to those questions from this large coagulation of information.

“I’ve seen in a lot of industries is the first wave is just, how do we put the infrastructure in place?  How do we store this data?  How do we collect it, join these various silos together?  But then some organizations get stuck in that step.  They say, ‘Ok, we’re storing the data!  This is amazing!’.  But the next question is, how do you do something with it?” Margit explained.

Say you are a clinician and you want to know how to reduce sepsis risk in your patient population.

LiquidLandscape is “a tool that allows [clinicians] to ask those sorts of questions of the data without having to launch a multiyear IT app development process,” said Margit. 

LiquidLandscape was originally built to solve problems with big data sets in finance, but the team saw similar ways to apply the technology to healthcare.  Both fields saw large explosions of data at some point in their evolution.  In finance, this occurred when the markets became electronic and new regulations were added.  In healthcare, we’re now amassing massive quantities of data through processes like next generation sequencing. 

“In finance, we’re always talking about tradeoffs.  Risk and reward.  Here’s a distribution of potential outcomes and I’m trying to find a course of action that strikes the best balance,” described Margit.

This same risk/reward balance also exists in healthcare.  That’s exactly where the LiquidLandscape Notebook comes in handy.  The Notebook allows clinicians to pull in information from big data sets, examine data distribution, and determine how all the variables interact- things like test results, medications, and patient preferences- to define the best plan of action for the patient.

“It would have been a very hard transition to make [from finance to healthcare] if there weren’t programs like [Techstars++].  Because obviously if we were just two finance kids knocking on the door, it would be hard to get those introductions.  But being able to come in under the Techstars umbrella and work with the local entrepreneurial community, people like you and Jamie [Sundsbak].  It gives us that way to talk to people, understand their problems, realize there’s more parallels than we thought there were, and say, ‘Hey, maybe we can help one of these things’,” said Margit.

 

Goal for their one week in Rochester:

“I say I came here for data and doctors.  We’re looking for that early adopter that has a data set, has a problem they want to solve, but hasn’t been able to find the tools or the trained analytic experts to come in and solve it quickly,” Margit said.

 

“It’s often hard when you’re some of the first startups, coming in to interact with the big institutions.  So finding ways to streamline that process, to put people who understand how startups work, and help startups understand the cares and constraints of Mayo and its unique culture. I think keep doing that.  This is an amazing environment for stuff to spring up because there’s so many ideas sloshing around,” Margit concluded.

 

Part 3: Solenica