Transcript: DBTB INT Kanu Gulati

I'm canoe gulati I'm a senior associate investor with Sarah venture partners and we invest in early-stage enterprise software that learns with data and so we call it the intelligent Enterprise Fund definitely I was really excited by the quality of the audience and the quality of the other speakers but most importantly I think the location and galvanizes is pretty popular too there are a lot of really deep technical talks which I often attend here and so just by virtue of the the community here I expected the audience to be pretty well-versed with the challenges and I could start my talk from at a much higher level that was really helpful and actually change that I don't believe data by itself is really cool I would think the the insights you can generate from that data by combining these large volumes of data with the intelligence solutions and learning algorithms out there along with the distributed computing or the cloud computing I think all of these together usher in the new era of computing which we call as the for there or the intelligence computing error I think that's really cool because that can create insights which can help business leaders make decisions solve big problems improve both top-line revenue and reduce costs for the company so I I don't think data by itself is cool but what you can generate from that data in combination with some of the other industry market trends that is insanely cool so there are three things I wanted to leave the audience with one with the disc on Sept of this new era of ER in which is which we call it the intelligence computing era which you know when you start with the hardware as the first journal software as a third or second error cloud computing as the third era and so combining all of the advantages of these areas along with the large volumes of data which are getting generated in the zetas by dr today is what is the intelligence committing it so that was the one you know just a level setting inside i wanted the audience to leave but the other two were more focused on the opportunities there are inside industrial Internet of Things which again in conjunction with this new computing error and the amount of data they've been collecting so the open opportunities of where entrepreneurs and investors should be spending their time and there was like more like subway that's one of the options one of the open opportunities I spoke about was aggregating real-time data with legacy data and trying to solve the problems around streaming analytics given the hybrid schemas and the different kind of applications of streaming data I think those are really challenging and that's also big opportunities for entrepreneurs and the second open opportunity I talked about was improving more decentralized decision-making and so by focusing on things like edge computing and mobile computing which can allow decision-makers to without having to wait for a central authority to solve problems decision-makers can solve problems in a in a decentralized fashion and therefore improve the outcomes in an efficient manner I think those are really exciting those are big opportunities out there how do you become a data scientist I think you just have to just just be curious about what's the value inside the data and how can best take advantage of those insights just anybody I think working with data looking at your insights and enabling those new insights to improve operations in a company can become a data scientist a truly valuable data scientist I think would go really deep inside a vertical because that's one but that's another thing by observation or just meeting one hundreds of startups out there he realizes in machine learning solutions in general I think vertical beats horizontal so being focused in one vertical focusing on combining your technical skills with domain specific skills and with domain specific data can develop you one can develop really brilliant solutions which can truly make an impact as opposed to just being a horizontal solution provider out there so I would say just being curious about data and then picking a water bill and focusing on that you