The Treasury is planning to build virtual desktops for mobile staff and make that standard across the organisation, said CIO Peter Alexander.
A tender is being finalised at the moment, with Alexander also testing technology from Citrix and VMware.
The virtual desktops will first be rolled out to 50 mobile workers and then the whole organisation. Alexander will supply a mix of devices to staff and then allow for bring-your-own-device (BYOD).
“We use iPads and iPhones as our default, but we also have laptops, Windows tablets that we’ve been testing, and Ultrabooks.
“The way this model works well for us is BYOD is not only much more possible but…if you are creating a virtual instance on a personal device or a Treasury supplied device, it doesn’t matter because all you are doing is getting a window into our back end. You are not actually storing content locally on the device, most of the time.
“If it’s classified content, that’s where we will insist on it being a Treasury supplied device because we will go through encryption and hardening of the device.”
When it comes to security, Alexander argued that virtualization is a “step above” containerisation. As the data is stored on a server or host computer, it reduces the risk if someone were to infect or tamper with the device itself.
“Containerisation is to put a fence or a container on your device and lock stuff in that container. Virtualization kind of does that, but only for that instance. [Data] is on the device for that session, then you log out and it’s gone, whereas a container still keeps it on the device.
“It does have some requirements like decent Wi-Fi or Internet connection. But, generally speaking, that is ubiquitous these days.”
Most of Treasury’s work applications will be virtualized such as its Microsoft SharePoint, Microsoft Dynamics, economic modelling, business intelligence, as well as its office productivity type tools such as email.
“The overwhelming benefit for us that we are looking to get is flexibility and productivity,” Alexander said. “Our people are always moving around, going to meetings and comparing models.
“It’s just an anachronism for where we are today in 2014 that people go to a meeting and take a folder full of printed notes with them. Really? Why on Earth aren’t we doing something more clever than that?
"They go to that meeting and take notes all over their papers and come back and someone has to try and transcribe them and capture that knowledge. If we’ve catch [those notes] electronically in the first place, we can suck those into our knowledge and information management systems and turn them into information that can be shared.”
Sentiment analysis not quite there yet
Alexander hopes to gain further insights into how Treasury can mine and use social media to create useful information on the cost of living, for example. However, this hasn’t proved to be effective so far.
“We got some interesting results from that, but they were a bit limited. We looked if it correlated against other metrics that we have, but it didn’t always. It looked like the sentiment was saying something was happening, but it just wasn’t happening.
"Whether that means there’s something in the measures, or what people say on social media has no relevance to reality. We couldn’t draw any conclusions because we just don’t know.”
Big data and machine learning a possibility
Alexander is looking at how Treasury could use sensor data and see if it can provide useful information that relates to economic situations.
“It’s about looking at massive unstructured data sets like being able to analysis out of utilities, or get a machine to run analysis over utilities, or traffic light information, crime statistics and come up with some correlation that tells you something interesting,” he said.
“We are still pretty early days in that kind of space, but we are looking around [in the market] at the moment. But there’s not a lot out there. There are lots of vendors that say ‘I’ve got this fantastic big data tool’, but really what they are trying to sell you is their business analytics tool.
"The fact that it can run over a gigantic data set is great, but it’s not going to do that machine learning, correlation of disparate and massive data sets," he said.