Sramana Mitra: I think what made Google happen is their business model innovation.
Radhika Subramanian: It was their business model, but it was also the fact that it was ranked search as opposed to just search. There are some of those parallels here as well. I don’t know how many times we hear this where companies will tell us, “We have this Big Data solution, but it’s manually intensive. The setup is really high.” We often hear, “A lot of these products are software-enabled services. These companies are using software as a way to actually bring you professional services.” The software becomes a way for them to line up 20 to 30 of their people and say, “Well, you’re going to need our people because our people are going to do the data modeling.” That’s not really automation.
Sramana Mitra: Are there any competitors that offer that?
Radhika Subramanian: Not exactly in this one thing that Emcien does well, which is complete automation of the analysis. In that one thing, we do not have a direct competitor. In that, I think, we’re creating this blue ocean strategy.
Sramana Mitra: Since we’ve already done use cases and competitive map, let’s go to the next part of the discussion. What are the trends that you’re tracking in your industry? Where do you see this industry going? Obviously, it has become very hot especially in the last couple of years. I think it’s not just hype. >>>
Radhika Subramanian: It’s a method that extracts all of those queries – what we call non-node queries. Send the data and say, “These are all the questions you should have asked. I’m going to rank it for you the way Google does. With that, I will isolate just the core of what you need to know.” Let me give this to you in an example so that it’s not so abstract. >>>
Sramana Mitra: Can you please position this for me in the context of your competitive landscape? We’ve tons of Big Data companies. We’ve seen this problem from many different angles. I’d like you to get granular in positioning your product based on exactly what you do.
Radhika Subramanian: If you have data, the three steps in analyzing around data are collect, analyze, and report. Those are the three steps. There are tons and tons of companies that are in the collection and storage space. It’s very mature and growing fast. Tons of innovation is happening there. Emcien has nothing to do with that. Let’s now go to the reporting side. Reporting was born because once you started taking data and shoving it to databases, businesses really struggle to get the data out. That’s really how BI was born. That’s reporting. >>>
Sramana Mitra: Were you still based in Georgia?
Radhika Subramanian: All this was happening in Atlanta, yes. Literally, on Georgia Tech campus. I launched the company at ATDC which is the Georgia Tech Incubator. They reached out and funneled millions of dollars into Emcien. Then, we started to do the first implementation and the payback was huge. It was phenomenal. The customers were Caterpillar and John Deere.
The dealers of these companies are huge. They are big billion dollar companies. We did a private placement with Adco, which is a dealer of Caterpillar, and did our Series A round. We started to build the company. Then the dot com burst happened. When the manufacturing and financial downturn happened, I thought, “Okay, Radhika how are you going to get up from that?”
More commentary on how Big Data’s rise leads to massive automation, productivity growth, and elimination of jobs! Exciting technology trends, scary human society predictions.
Sramana Mitra: Radhika, let’s introduce our audience to yourself. Tell us a little bit about you and how you got to Emcien.
Radhika Subramanian: My name is Radhika Subramanian. I am the CEO of Emcien. Emcien’s product is automated analytics for Big Data. Having said that, let me take a step back and let me tell you about me. I am from India and I’m an engineer. My undergraduate degree was Electronic Engineering. I came to Georgia Tech and did my masters in Industrial Engineering. I ended up starting my first company completely by mistake because I was in the computational group at Georgia Tech. This is way before Big Data became sexy.
Sramana Mitra: You mentioned that there’s also feedback going into engineering. Does that imply that you see bug reports on social media?
Howard Lau: It’s not so much bug reports. It’s something like product enhancement request.
Sramana Mitra: That’s for product management though, right?
Howard Lau: Yes, product management.
Sramana Mitra: You’re doing a sentiment analysis. If it’s a negative sentiment analysis, you’re trying to take action against that?
Howard Lau: That’s a great insight. It is sentiment analysis. At the most basic level, you’re looking for things like positive, neutral, or negative sentiment. Our engine allows us to dive in to another level of data. If what they’re looking to is negative sentiment, they can dive in to what is that negative sentiment due to. Is it due to coverage, pricing, or service level? It is in that fine level of detail that they can then respond to appropriately.