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How to Build Smart Cities That Work


Big Community was privileged to have a quick chat with Matthew Hurford, Systems Engineering Director – ASEAN / ANZ CTO at NetApp, a leading data management company that has been seeing strong growth in the APAC region. Their Data Fabric vision helps customers simplify their data management strategy in the hybrid cloud era, while Matthew champions their strategic solutions, helping the customer and their sales teams derive and understand their true and unique value. The digital disruption and data era is playing right into the hands of NetApp.

Matthew shared that today, interaction with government is one of the keys to building good smart city infrastructure. He believes there are 3 important components to that strategy.

Government interaction plays an important role, while applications and technologies supporting the infrastructure are needed too. “Connecting citizens directly with the government through technology and smart platforms, and a range of new applications needs to be developed. My kids (and) what they expect and how they want to interact with government has drastically changed from when I was younger and used to walk in to a shop-front that was a government department. Now you can interact very much digitally”, he shared.

The third important key he shared to making smart transport and smart cities will be the real time data decisions that drive better outcomes and better services to its citizens.

“Broadly they are the three things; connecting with government more intelligently, digital platforms, redesigning and rethinking how we build fundamental services inside cities, and then with the IoT component, which is building that real-time data decision to provide better service outcomes”.

He shared that NetApp is very much involved in these three steps. “We see ourselves as being a cloud data management company. And we have a very holistic view for how we think smart cities should be developed. Its very much around the concept NetApp has had for quite some time, which is around breaking down silos. If you look at where NetApp has innovated in the past, its been breaking down siloes of data to create better outcomes or business opportunities.”

Having smart cities that don’t deliver the required data and information, is running a big risk of letting the whole eco-system down. There needs to be a solid foundation of analytics, processes, and data gathering to make sure that eco-system is dependable and worthy enough to be used by these smart cities citizens.

“NetApps vision around this is that there (are) multiple steps in the data collection processing, analysis and therefore archiving of the data. And we see the first step is in ingesting data. Now that can be from a number of sources particularly in an IoT driven world. You know we’re collecting data from censors and as an example we are working with some cities that are using cctv. We have some customers in India where we are collecting cctv data and doing real time analysis that may be looking for threats, it may be looking for population flows or traffic signals and that kind of optimisation.”

He adds that once this data in ingested, they need to make real time decisions quickly. That information is traditionally moved into data lakes. Here’s where the flaw is revealed where the architectures of the past are proving to be ill-suited for current consumption and different types of architectures will be needed in the future.

“What I mean by that is the architectures weren’t built for rapid analysis of small files (or)small pieces of information quickly. And where object storage or HDFS and Hadoop architectures were being used in that space, they still play a role,” he said but argued that we need something faster for smart cities to perform at its peak.

“I think that smart cities are absolutely built on flash storage architectures. We need to be a feed because a lot of the decision making process and a lot of the applications that are going to drive smart cities, are going to be built around AI, machine learning, even deep learning algorithms. These are very data hungry environments, so we need to be able to feed the information very quickly, often with a lot of data.”

Though there doesn’t need be a lot of data being ingested, training, testing and deploying the algorithms into these environments quickly is where the most crucial part of achieving a workable smart city lies.