Software start-up Latize, which enables data-driven decision making, is looking to raise $7 million to help it expand in Australia and North America. Marissa Lee meets co-founders Vikram Mengi, 47, and Biju K., 43.
Q What problem is Latize trying to solve?
Vikram: When Biju and I co-founded the company in 2009, everybody was investing in software without really getting the kind of outcomes that were possible.
Even today, a lot of times we meet data scientists who were hired to be data scientists, but actually what they are doing is just writing queries to download data and doing some very menial tasks because the data is not in a position to be used well.
So the idea was that we would solve this problem from a data organisation perspective.
So the data scientist can be the analytical person he or she was hired to be, rather than doing all the cleansing and organising work, and being a highly paid report writer.
We started as a two-person consulting firm in 2009 and in 2013 we launched Ulysses, which is our key data platform.
December 2013 was when we won our first million-dollar contract from the National Library Board in a public tender.
Q How does Ulysses work?
Vikram: Every government agency or a bank has hundreds of different IT systems, and data is spread across all those systems.
Ulysses takes the data that sits all over the place fragmented and joins them together in a common knowledge base.
Ulysses then enables a business user to ask questions and get to the answers using just a browser. Today when people talk about analytics, everyone thinks in terms of reports or dashboards.
The problem with those things is that they are all pre-defined. You have to define a report before it gives you something, but what if every day you ask a different question? You can't work on a pre-defined basis.
Imagine you are an investigator investigating fraud. In every case, there would be a different set of evidence. Somebody has flagged a particular transaction as a "suspicious transaction".
You want to know if there is something actually wrong or if it is just a false positive.
So you'll go back to the system and say hey, what is the relationship between these two guys? Are these related parties?
These queries, our system can answer in a click or two. It can also connect to external news or social media networks if that is required.
Q What else differentiates Ulysses from other data management platforms?
Vikram: We are one of maybe 10 companies in the world that use semantic information processing.
Most of the analytics solutions in the world today are statistical in nature. But we do not deploy statistics, we use semantics.
When you use statistics, the outcomes are a probability. Whereas if you say there is a relationship between X and Y, there is a relationship.
I'm not saying there probably is a relationship - there is.
Another application is similarities. For example, you say, Vikram is my best customer. Who are the other customers who are similar to him?
There are different types of applications where different types of approaches work better. When customers don't have a lot of data going back many years, it becomes difficult to find patterns in that data.
So there we will do well.
Q Who are your main customers?
Vikram: Government agencies, financial and education services.
Education is very well-suited to what we do. There has been a push inside and outside Singapore for holistic assessment and holistic development.
But how do you measure what is an engaged citizen? Or a creative thinker? These are actually profiles and they require diverse data points, both academic and non-academic.
Schools can't be hiring data scientists, they don't have that type of money. So it's a very good fit for us.
Q What's next for Latize?
Vikram: We are currently raising a series A round, about $5 million to $7 million. We are investing in geographical expansion.
We closed our first customer in Australia in March, and we're excited by the traction we're seeing there.
But our real market is in North America, that's the biggest market for us.
Our expansion there will be more opportunistic, more partner-led.
This article was first published on Apr 18, 2017.
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