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4 posts from August 2016

24 August 2016

5 reasons to use virtual reality for data visualisation

  VR-image

Virtual reality, through its ability to immerse us in new environments, could be the next big step in data visualisation efforts

The world is exploding with data.

It’s now pouring out of every home, office, computer, mobile, machine and human being. Data has become so vast and unwieldy that we’ve upgraded its status to ‘big data’. The visualisation of this data therefore plays an increasingly important role to help us break down its complexity.

Virtual reality is a new medium that offers a lot of potential for data visualisation. By immersing ourselves in the data, we can take advantage of the greater space on offer, more natural interactions, and analyse multi-dimensional data in a visceral way.

By living in a world of data rather than being a spectator in it, studies have shown that the effectiveness of the visualisation is multiplied. This leads to ‘demonstrably better perception of a datascape geometry, more intuitive data understanding, and a better retention of the perceived relationships in the data.’ 1

Many will argue that the 3D spaces and experiences that VR brings can be emulated on a 2D screen. This is true to some extent but the 5 differentiating reasons for using virtual reality for data visualisation purposes are:

1. Less distractions 

By focusing your entire field of vision, you can hone your concentration on the objective, whether that is to help archaeologists visualise the location of objects of interest during an excavation or to take users on a guided tour of 21 years of the Nasdaq.

Through being ‘present’ in the data you can also get a true sense of scale which is difficult to achieve when viewing the data on a desktop screen.

2. More space

With a 360 degree sphere of space to use, there is a lot more real estate to display data such as in the below proof of concept by Bloomberg of its virtual reality trader terminal.

Bloomberg-on-oculus

 

Source: Virtual reality headset Oculus Rift meets the Bloomberg terminal (Quartz)

3. Multi-dimensional data analysis 

We primarily use our sight to analyse and interpret data, but what if we could also use our hearing? Through data-audio relationships, we could understand the significance, subject, and location of a particular data point through the loudness, type and direction of the sound, for example.

By using multiple senses, we can enhance our ability to process data with more dimensions. While it may be a bit radical to talk about taste and smell in a data visualisation context, it is not outside the bounds of possibility to ‘feel’ data. This is technically achievable right now with haptic feedback gloves.

4. Greater bandwidth for processing data 2

Much like a computer, our optic nerve is capable of transferring information at about 1 MB/s. When we simply read words on a screen we’re only using 0.1% of this capacity. Naturally, this would have improved with visualisation techniques that have been developed over the years, but at the end of the day this is still about reading information from a 2D screen.

Virtual reality immerses you in a stimulating 3D world that engages your brain and enables you to fully utilise your optic nerve’s bandwidth.

5. More natural interaction

In the real world we interact with objects directly with our hands. This allows us to connect with the environment around us and get a better idea of the objects we’re dealing with. For a long time, we’ve used keyboards and mice as conduits for this interaction. Through virtual reality, we can return to a more natural way of interacting - by physically pushing buttons, moving windows around and manipulating data streams (such as in this VR assisted biological specimen analysis). That is in addition to being able to walk around and through these data worlds.

Through these benefits, we can improve employee efficiency, conduct a deeper analysis of data more easily, and make faster decisions.

There are still some obstacles to overcome before data visualisation in virtual reality really takes off. The resolution of the headsets needs to increase so text is comfortably legible, and eye strain and nausea are still an issue for a segment of the population. One of the key challenges is not building a VR data visualisation just for the sake of it - we need to design useful visualisations which take advantage of VR’s strengths and offer an intuitive way of interacting, analysing and manipulating the data.

With the hardware developing and our understanding of this new technology improving every day, it’s only a matter of time before we see data being usefully visualised in virtual reality.

For real-life insight into the many ways data is being collected, analysed and used, come along to the ‘Our Lives in Data’ exhibition hosted by the Science Museum in London which is proudly sponsored by PwC.

This blog was originally published by Jeremy Dalton here.

 

References

  1. Donalek, C., Djorgovski, S., Davidoff, S., Cioc, A., Wang, A., Longo, G., Norris, J.S., Zhang, J., Lawler, E., Yeh, S. 2014. Immersive and Collaborative Data Visualization Using Virtual Reality Platforms. URL: http://arxiv.org/ftp/arxiv/papers/1410/1410.7670.pdf. Accessed in August 2016.
  2. Michael D. Thomas. 2014. Using virtual reality to understand big data. URL: http://www.sas.com/ro_ro/news/sascom/2014q1/virtual-reality-big-data.html. Accessed in August 2016.

 

 

23 August 2016

Artificial intelligence – “Practical parenting for your AI engine”

The world has become very excited about the idea of artificial intelligence. We dream of the panacea of the “machine that learns everything”… the “machine that never forgets”… the “machine we can always rely on”.

But how realistic is this?

I am often musing over the real world. It is full of ideals, great ideas… but sadly many fail to materialise. The practical realities of life always seem to have an impact - and usually not in a good way.

As I pondered the practicalities of artificial intelligence, I started to reflect on the realities of intelligence. I didn’t have to look far before I started to worry.

My children have grown up to become worldly-wise people, but as I look at their journey through life, I recognise that they haven’t always been fed the best information to make the right decisions.

I imagined my newly built artificial intelligence engine. It's beautifully written and eager to learn. I just need to press the “button” and very soon it can guide me through the complexities of my business world. The stress of complex decision making will soon be gone.

But now the dilemma begins. Where do I find the facts to feed it? What if it gets “in with” the wrong crowd? What if it decides that “evil” is a fun and exciting thing?

Suddenly I'm visualising my children again. Should I have stopped them from playing outside? Should I have selected their friends for them? Have I inadvertently skewed their views of the world and made them narrow minded?

I'm sure that I exercised a few of these controls over their early lives, but now they are free and roaming their world. They're now free agents, who must apply the essence of the controls I hopefully passed on to them and choose carefully what they learn and who they learn from.

Maybe this is a good end point, but I don’t have time for my new artificial intelligence engine to learn so dramatically from its mistakes, or should I call this “experience”.

So, where has my musing taken me?

Well, I'm seriously worried about how I educate my learning engine. I'm worried about how I control what I expose it to. And I'm wondering about how I check that what I feed it, is in fact right.

Suddenly my artificial intelligence engine has become my child and I need to be a good parent. I need to love and protect it from bad things. And as every parent knows, this will not be an easy task.

I feel the need to write a new book… “Practical parenting for your AI engine”.

I'm sure you'll agree… this is something for us all to think about.

 

15 August 2016

The intangible side of data

Big data architecture. Self-service analytics. User interactive visualisation tools. All this and more – lately the world has been obsessed with data and technology but what does it really mean?

We often emphasise on the processing power and analysis tools used but that does not suffice to help end users. Information can often get lost in translation when the message is so long and details complex but as consultants it is our job to help our clients distil that message and make insight simple and easy to absorb.

The data has more or less always been there but beyond crunching numbers, it is about embedding a new way of thinking and that is so much more than technology. Good data analysis should be tool agnostic and focus on the behavioural impact rather than the ‘physical’ output.

For that we need to focus on the intangible side of data - the nature of users, their interaction with it and its impact.

The people

More often than not we hear about creating ‘data-centric organisations’ when in fact we should rather be focusing on ‘user-centric data’. It does not matter the scale, depth of analysis and complexity of methods – if there is not a match between the message and the recipient’s ability to interpret it, it will get lost. Thus, we should be focusing on producing insight that is comprehensible to the people receiving it.

The journey

Good insight takes people on a journey. Data is a way to explore and understand the world around us better and so it should be an interactive experience, not a one off transmission.

Technology is science but storytelling is art. Telling a compelling and coherent story is the key to relaying insight and so we should be putting ourselves in their shoes and walk their walk to understand exactly what are the issues that the users are facing and drive them through the noise.

The purpose

Data should act as a starting point not an end answer – does it pass the ‘so what’ test?

Ultimately, is should be a behavioural trigger, whereby users either can make decisions based on it or change the way they act to correct some outcome. As such selecting the analysis that can enable action is crucial to successful insight.

Where to next - UX as a state of mind for data analytics

It is no longer about big data – the focus is now on small data that is fit to the user’s needs and increasingly shorter attention spans. For that we need to adopt UX as a state of mind for data analytics rather than as a separate discipline.

If we can do this then we can reduce the struggle derived from combining complex technology, messy data and change-resistant stakeholders in order to deliver successful transformational projects that will stick with our clients.

10 August 2016

A carnival of data and performance

PwC_R_SouthAfrica_Jhb_R_CW_0002

I spent last weekend watching a carnival of sport on television and like any sporting enthusiast will tell you, it’s been heaven. In my previous career I was captain of England’s Rugby Sevens team, so the feeling you get when all the preparation is over and the competition begins is very familiar to me – a heady mix of nerves, excitement and adrenaline.

These days, I’m more involved with data and analytics than a rugby ball but any big sporting event reminds me that the two aren’t so far apart. Data and analytics have become are a huge part of sport; every element of an athlete’s training – from what they eat to how fast their heart beats – is monitored, analysed and optimised. When I was playing rugby it felt like everything was measured, from my body temperature to where I was on the pitch at any given moment; every part of my training and every move in every game was watched, analysed and understood. Nothing was left to chance.

We have the capacity to collect data on just about any aspect of a sporting event if we want to. We can learn about the level of noise that fans produce and the amount they eat and drink; the organisers can see how well the transport infrastructure has worked. And, of course, each athlete can learn everything there is to know about how well their body is performing.

Technology can deliver a huge amount of data – in my day we used to watch our matches back on VHS tape but now rugby players wear a microchip in their kit which means their own moves can be coded by analysts, isolated and tracked.

In a world where the margins for success come down to milliseconds and millimetres, data matters. But it can never substitute the human effect and the figures don’t always tell the whole truth. That’s why the best coaches – those that make the best use of the data that’s collected – will always make a difference. The same argument applies in the business world; data and analytics help management make better decisions, if used wisely, but they aren’t a substitute for judgement.

In sport and in business, data can only get you so far. Analytics will make an athlete’s preparation as efficient and thorough as possible but it can’t win the race for them. One player in a team might have a terrible game in terms of their own personal data but still win the match for their team. Intelligent use of data will help you reach the levels you have to attain to be the best, but the rest is always up to you.