Talend: Yves de Montcheuil, VP, Marketing
Many predictions from last year said that big data would become mainstream in 2013. Do you think that actually happened?
To a degree, yes. In 2012, we saw a lot of proof of concept, testing the waters of big data. In 2013, a significant number of projects actually went live with big data. We are past the early adopters. We are probably in the early majority of the adoption phase.
What are some big data trends we’re going to be seeing in 2014?
One of the big trends we’ll be seeing in big data in 2014 is the advance of the big data computing platform. More and more, what businesses need is immediate access to insights from big data. That means interactive querying, interactive processing, and the need for a platform that is suitable to deliver this real-time, operational type of use for big data.
Another one is the link between big data and the cloud. The cloud is a big data deployment option, but it will also become a new source of data. As organizations expand the use of big data beyond their on-premises systems, they’re going to find a lot of very interesting data in the cloud that they can leverage for their big data projects.
The last one, which is another extremely important contributor to big data, is the Internet of Things – the industrial Internet, if you want to put it that way. This is a time where more and more objects are connected, and this trend is going to amplify. The cost of sensors is going down; pretty soon you’re going to be able to get sensors that are connected to the Internet for probably less than a dollar apiece. And that means more and more objects providing more and more data points that need to be harvested.
Would you say that most businesses are prepared for these changes?
Most are not prepared. Some forward-thinking organizations are prepared, or are at least doing what they need to do to be ready, but clearly there is still a long way to go. The Internet of Things is going to create some very interesting challenges in terms of storage capacity, real-time streaming, and tapping of the data in terms of delivery and operational data to applications.
IBM: Warren Tomlin, Practice Leader, Canada
How is big data going to change the way we live in the next five years?
I think the trend we see most powerfully is how big data in the cloud can be leveraged using analytics and cognitive computing. We [IBM] see a medical trend, a city-level trend, an education trend, a guardian angel trend, and a retail trend.
What do you see in the medical field?
Very few clinicians have access to the tools to crunch data, and frankly, there’s more big data than we can use. So how do we use cognitive computing and behavioural computing to crunch the data and look for trends, behaviours, and insights, without putting that burden onto the clinician or the researcher? We can then tailor the drug therapy or the drug treatment to individual patients at the DNA level.
And what about education?
Where I think the medical field would use just massive amounts of big data and collaboration, I think the classroom would use longitudinal data. From kindergarten through to graduation, we would take many data sets that students [provide] – everything from attendance records, aptitude tests, and other test results – and start to tailor their education based on these longitudinal events, over these many years, to very specific lesson plans for the student.
The trend we’re predicting is how it could work; how it will be implemented will probably vary from city to city, state to state, country to country. With the proper provisions for privacy and security, I think [the security issues] could be overcome, especially if the benefit is so important.
You mentioned retail?
The idea there is the return to shopping local. How do top performing retailers over the next five years leverage technology in-store and at a local level to create an unparalleled user experience? Our belief is that advanced technologies like augmented reality, real-time reviews, and third-party reviews will give us an unparalleled experience in the retail outlet that is quite different than online.
The retail industry is under such fundamental transformation that not leveraging mobile, social, and other technologies will be opportunities squandered over the next five years. Savvy retailers that work both the physical store and the online together will be more successful than pure digital. They’ll bridge the experience locally and online.
How about cities?
This is really building on some of the trends that we’re seeing globally around open government and digital government, where governments are starting to expose – in a good way – their public data for citizens to interact with. For example, we’re working in Brazil, where citizens can report areas that are inaccessible for people with disabilities. And I think we’re seeing that data in the cloud and analytics make it easier to live in cities, and also that the city will be able to recommend to you, “Hey, here’s an event coming up that you may have an interest in.” It lets citizens connect with governments, and governments are able to connect more with citizens.
The fifth prediction is “A digital guardian will protect you online.” That’s really interesting – can you explain it in further detail?
We’re getting to a point where so much of our lives is digital. We think there will be an online guardian that has a 365-degree view of [online] events and transactions. It will be persistent, so it will always monitor all our accounts and all our behaviours – it’s looking for intrusion, looking for deviance from our normal behaviour. We see a time in the next five years where we’ll be able to have preferences, and the guardian angel will come back and say, “Is this what you intended? Is this your behaviour?” If yes, no problem; if no, then action is taken. It’s a very persistent way of protecting your online identity.
What will IBM be doing in the big data sphere in the coming years?
One of the common threads related to big data is around how the computer will be more cognitive. We can’t program enough machines to handle all the data. We have more data than we can possibly use. So how does the machine learn? How can we get the machine to learn what’s important in big data, and what’s not, and look for correlations and patterns that would otherwise take forever to find?
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