Big demand-supply gap for data scientists: Puneet Jetli of Happiest Minds
For big data, candidates need programming skills, domain understanding and not get knocked down by the volume of data they are seeing, says CEO-digital transformation and enterprise solutions business, Happiest Minds
Diksha Gupta, TimesJobs.com
The real world adoption of big data has not been close to expectations. But 2016 is expected to see a lot of action on this domain, and with it will grow jobs of data scientists and data analysts.
TimesJobs.com spoke to Puneet Jetli, CEO-digital transformation and enterprise solutions business at Happiest Minds, about how is the field of big data faring and where are the real opportunities. Excerpts:
IBM CEO Ginni Rometty said it's no longer about being a digital business but a cognitive business. What is your take on this?
Different companies have different ways to present this. But in Happiest Minds, we don't separate the two. For us, the digital business cannot without the insights element and therefore, to that extent, I do agree that more and more of digital businesses will get powered by cognitive aspects or the insights aspect. But are these two separate businesses? I wouldn't want to get into those semantics because it is really a semantic issue for us. Digital business cannot exist and be successful and thriving unless there is an element of intelligence or cognitive element embedded into it.
What's the difference between data science and data intelligence?
Essentially, it is about data at the bottom, and then getting insights or intelligence from it. Now, that could happen in multiple shapes and forms. In the good old days also, we could get this intelligence or insights by just doing mixed reporting. That element was called visualisation and it still exists. So, that is a simple and primitive form. Then, it is really a question of the need to apply statistics to data, so that I could get a different level of insights, especially when we are getting into the predictive world -- which is what the cognitive world is all about. That is where people begin to talk more about data science into it, because there is a statistical or a science element which comes into it. But data intelligence is all compulsive, because intelligence could come from purely looking at data or visualisation or reporting of data or applied data science and getting into predictive analytics around that.
Yes. The industry is using the term business intelligence for some time. Many a times, people want to say that the industry has matured beyond business intelligence to data science - which is really into the analytics part of the business. So if people are using the term data intelligence from a business intelligence standpoint, then data science would look like the next frontier. If you look at these insights that you get from the data, all of this is really a part of the same pyramid.
Big data has been around for quite some time now. But the issue is that people know there is big data, people know there are tools to analyse big data but they generally don't know what to do with the analysis. Is this correct?
I would agree to it. The reason why I agree with you is because typically in the past when people consumed data, they had largely consumed it in form of reports like MIS or dashboards. Essentially, in that sense, one is looking at data, or one is looking at some intelligence which has been provided, but still have to make the decision and act on it. The big data world is changing and we are moving away from the spot where people were manually looking at data and trying to decipher and then acting on it. The world is moving towards real-time response. So, how does one actually collect so much of data, how does one try and analyse it in real-time and how does one act on it in as much of real-time as possible. And, that is the science which didn't exist till now. But now when we are talking of big data analytics, we are talking about areas like internet of things. It actually requires almost real-time reaction and response. That is something which is really beginning to emerge now. While big data technology as such existed, this whole thing of figuring out insights in almost real-time and incorporating that into your overall ecosystem of Gamification and responding in real-time is a new area.
Yes, in the true sense, absolutely! People were doing it in different ways. People are now doing it in a way which will probably see much more return of investments and much more differentiation coming from big data than what people had probably done in the past.
So, is this what will change about big data in 2016 or is there more to it?
In the past 3-4 years, I would say we were able to capture different kind of data elements and we were able to handle much more complex data in high velocity business scenarios. I think that will continue to evolve. And, you will see that in 2016, the focus will become more on how we can actually connect these insights that we are getting back into enterprise systems to make it more agile and more responsive in real-time.
Looking at the job scenario for big data scientists and analysts, is the demand meeting the supply?
I think any market you take, there will be a gap in demand and supply as far as a particular skill set is concerned. Not only, in the next year, but I think, in the foreseeable future. As businesses are willing to get differentiated and powered by insights and cognitive kind of business begins to evolve, there will be a huge leap. I think today also, we are not able to service the entire need which exists and I think this scenario will kind of continue for some time.
Is any effort being made to bridge the gap?
Because there are multiple skill sets involved and this is a high-end work, it is not just about training people and putting them. It is a complex problem because you need to understand the technology related to data is programming oriented. Then, you need to be able to simplify data and you need to be able to analyse data by applying statistical models. And, for doing all that, you need multi-faceted skills, you need programming skills, domain understanding, and ability to look at data and not get knocked down by the volume of data you are seeing, to be able to see meaning patterns. So, it only comes with experience, not something which can be taught to people in class. So, there is a multiple dimensional approach to a problem solving, it is always about a much more complex thing to achieve. And that is what we are seeing in big data. And, that is why there is a gap between demand and supply, which every projection says will continue to increase actually.
IoT will become another contributor to the big data environment. And also, as I said, with IoT, the feedback loop will also get completed. Because, a lot of people are already kind of collecting data, but there is no point collecting data unless you are analysing it. And, there is no point in analysing it unless you are actually reacting to or using it in as much of real-time as possible. So IoT will actually be a beneficiary, because of the big data technology and IoT will also, therefore, begin to drive in creating much more agile in a data-driven organisation.
It is one of the contributors. I would say AI requires a combination of multiple things. Big data is one of the constituents of that. But, just big data is not a contributor to AI, because, you need big data technology, you need insights technology; you need business process automation technology for AI. So, AI or cognitive sciences or cognitive computing, as it is called, requires a few more things to start looking at big data analytics.
What industries will get impacted by Big Data the most?
I cannot think of any industry that has been left untouched. If you look at e-commerce industry, it is big user of big data. If you look at financial services, I think, they are only in the cusp of adapting a big way. If you look at manufacturing, especially using IoT, it is going to be a big user of data technologies. Across the spectrum of the supply chain processes, everything is going to get influenced by big data. You look at travel industry. Insurance, for example, is going to get impacted because, today, people are talking about giving you insurance based on your driving habits. And, that can only be powered through big data and its technology.
How will big data shape up in 2016?
I think it will again be different. For example, the Indian government is now beginning to talk about smart cities. Smart cities cannot be implemented until you are implementing the concept of IoT and big data. So, if the government wants to move ahead and implement next generation kind of technology for enabling smart cities, big data will have to play a role. In a lot of consumer-centric industries, where you need to understand the consumers and engage with them to provide differentiated products and services and experience, you will see increasing adoption of that. I see a lot of big data usage in retail and manufacturing industries as well.