Abhishek: And I will ask a question back to Holly and Larry. From your observations, how do you see machine learning inter-playing with human interactivity with data and analysis? Do you see the machines taking over and answering all the questions, or do you see a important human element in this is well.
Larry: No I was going to say you know machines need to be trained to provide great answers to questions that humans ask. So that's where things are. You need human power to figure out what are the problems worth solving, and you need machines to provide computation power, to provide the repeatability, but I don't think they we're at a stage where machines can run BI by themselves. You still need that human hand, and you still need software to provide those expiration capabilities. Holly, please?
Holly: Think about what machine learning is for. It is to answer questions, and tools like InetSoft's are really used to create better questions, and so there's actually an input side to the benefits of machine learning for BI. There is an output.
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Like I said machine learning creates data, but when I talk to data science teams and our customers who love InetSoft because it puts the human inside the loop of the machine learning workflow, it helps them validate that the algorithms and the whole process is working. So BI and analytics and especially digital analytics can help at all stages of the machine learning life cycle.
Abhishek: Yeah, I completely agree. I mean I think this is such a complementary technology to the human element of data exploration or visual exploration of data, but I have a hard time imagining a world where the machine is autonomous. Machine learning is being used ways now to pre-build dashboards and the start of a data connections and technologies.
But at no point can it completely augment or replace the human. It can augment, but it certainly can't replace the curiosity of people. Ultimately why are these investments happening around the entire world? It is our curiosity, and we are trying to answer the question.
Holly: It inspires the humans to do more, to do higher value analytics.
Abhishek: That's right. They are complementary. Our next trend, and this is the one we tried to fit as many industry buzzwords in one trend as possible, that the convergence of IOT, Cloud, and Big Data is here. So we have the trifecta. This trifecta has created new opportunities for self-service analytics.
Larry, why don't you start us off with some thoughts on this. Larry: There is an obvious trend, and there is not so obvious trend here. So the obvious trend is obviously the IOT is not only the biggest buzzword on the planet right now, but we are actually seeing a lot of that actually getting realized from healthcare to consumer packaged goods.
I have seen use cases that actually have been realized and bringing value to customers. It is generating massive volumes of structured and unstructured data, and an increasing share of which is actually being deployed on cloud services. What it means for the analytics market is a couple things.
We have seen there has been tremendous innovation. In terms of figuring out how to capture data from sensors and from remote platforms, it could be an oil rig 20,000 feet under the ocean or the blades of an airplane, but we still need a lot to happen for that to make sense to the end-user.