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What is it?

Data can be analysed in different ways and the most simple method is to create a visual or graph and look at it to spot patterns. This is called visual analytics and is an integrated approach that combines data analysis with data visualisation and human interaction.

Data is produced at an alarming rate. In 1981 futurist and inventor Buckminster Fuller proposed the ‘knowledge doubling curve’ to explain the fact that the more knowledge we accumulate the faster we create more knowledge. Up until the end of the nineteenth century human knowledge doubled every one hundred years or so. By the end of the Second World War the total knowledge of mankind was doubling every 25 years. Today it is thought to be every 13 months and IBM have already predicted a point where our knowledge will double every 11 hours.

Now that’s a lot of data! Unfortunately our ability to collect and store that data is increasing faster than our ability to analyse it. And while there have been a number of tools developed to automatically analyse some of it, the complexity of the data and the questions being asked means that human beings still need to be involved to bring their creativity, flexibility and background knowledge of the situation to the process. Visual analytics therefore allows decision makers to combine human input with the enormous storage and processing capacities of modern technology to gain insight into complex problems using advanced visual interfaces to help them to make better decisions.

When do I use it?

The most appropriate time to use visual analytics is when you are trying to make sense of a huge volume of data and/or if the complexity of the problem you face could be assisted by some additional computational horsepower.

Visual analytics is therefore a useful tool when you need to attack large, complex and interrelated problems where there is a lot of data to analyse. Technology is clearly essential to analytics but technology will probably never replace human beings because it’s not yet possible for technology to get a ‘big picture’ grasp of the problem and what needs to be done from multiple different angles. Visual analytics seeks to take the best of human intellect and technology to combine them in a way that allows the technology to do most of the hard computational work while ensuring that it is solving the right problems and the end result is palatable and useful for the human being that will have to interpret it.

Technology can therefore amplify human cognitive ability by increasing cognitive resources, expanding working memory, reducing search time and enhancing pattern recognition capabilities across large data sets.

Using visual analytics when turning data into pictures and graphics would help tell a more complete story and help to reveal the patterns and trends hidden within that data, which could in turn aid decision making at all levels of your business.

What business questions is it helping me to answer?

Essentially, visual analytics can help you spot patterns in data and allow you to make vast amounts of data accessible and understandable to anyone regardless of whether they are a data scientist or statistician or not. It can help you to answer:

• Where are my best customers located?
  
• What is the profile of my best customers?
  
• Is my market share increasing or decreasing?
  
• Is there any connection between factor X and factor Y?
  

Visual analytics also allows you to answer these questions faster and provide the answers in a visual, more engaging way.

How do I use it?

According to the European project VisMaster, there are four separate stages in the visual analytics process – data, visualisation, knowledge and models. The first stage is the input and transformation of the data. Often the source data exists in different formats and in different locations so it first needs to be integrated before visual or automatic analysis methodologies can be applied. Other typical pre-processing tasks include data cleaning, normalisation and grouping.

The next step is to run the automatic analysis, which will often use data mining methods to generate models of the original data. Once a model is created you must then evaluate and refine the models. Visualisations then allow you to interact with the automatic analysis and play around with the data by modifying parameters or selecting other analysis algorithms. Model visualisation can then be used to evaluate the findings of the generated models. Alternating between visual and automatic methods is characteristic for the visual analytics process and leads to a continuous refinement and verification of preliminary findings. Remember, it is the combination of human and technology that makes visual analytics so useful. If something doesn’t look right to the human eye then it can be checked, refined or new analysis run.

Ultimately it is the user interaction with the visualisation of the data that is needed to reveal insightful information, for instance by zooming in on different data areas or by considering different visual perspectives. Essentially, knowledge can be gained from visualisation, automatic analysis, as well as the preceding interactions between visualisations, models and the human being doing the analysis. Thankfully there are many commercially available visual analytics tools on the market.

Practical example

Swedish medical doctor and academic Hans Rosling is Professor of International Health at Karolinska Institute. He is also a statistician, data guru and brilliant public speaker. If you want to see the power of visual analytics then I recommend you watch any of his really interesting, funny and engaging Technology, Entertainment, Design (TED) talks.

In one (http://www.ted.com/talks/hans_rosling_at_state), he talks about how his students often discuss ‘them’ and ‘us’ in terms of the developed world and the western world or developing world. So he asked them to define exactly what they meant by these labels. They had all learned about them in college and were confident they knew what they meant. Dr Rosling pushed for a specific definition, and one student suggested that the developed world was characterised by ‘long life and small families’ and the developing world was characterised by ‘short life and large families’. It was a neat and concise definition but was it true? Dr Rosling decided to test the hypothesis. Obviously in order to test such a theory an enormous amount of data was required to process.

He needed mortality rates per country, birth rates per country and all that data every year for decades. To look at that data in its raw form – perhaps in spreadsheets or databases – would have yielded very little in terms of insights. The human brain would not have been able to process such massive data sets and come up with any meaningful conclusions – and yet Dr Rosling did using visual analytics.

He found that the notion that his students held about the nature of life in different parts of the world was fundamentally flawed. He created a visual map that showed the correlation between ‘children per woman’ versus ‘life expectancy’ for countries across the globe and animated the chart to move through the years. Watching the visual animation of the data, viewers could tell that initially looking at data from 1950 the definition was largely accurate but by 2007 it simply wasn’t true any more. And yet here were young students being taught this definition as though it was still a hard, fast and accurate definition.

Granted there were still countries such as Afghanistan where that definition still held true but the vast majority of countries had significantly reduced family numbers and were living much longer than their grandparents.

That is the power of visual analytics: it allows mind-boggling data sets to become genuinely useful and can help us to change out mindset about what is really happening in our business.

Tips and traps

When creating the visual analytics make sure that the key strategic question the data is answering is stated clearly on the page or screen. This will help to focus the reader’s attention on the data’s purpose so they don’t get lost in the graphic or visual representation.

The key danger with visual analytics is that you can end up becoming obsessed with the visual part of the equation and slice and dice the data a thousand ways. Visual analytics is extremely useful for bridging the gap between the data and the insights but only when you stick to what’s needed when it’s needed. Just because a visual analytics program can present and manipulate the data a thousand different ways doesn’t mean you need to present and manipulate the data a thousand different ways. Stay focused on what needs to be answered.

Further reading and references

For more on visual analytics see for example:

• Tufte, E. (2001) The Visual Display of Quantitative Information, 2nd edition, Cheshire, CT
  
• http://www.visual-analytics.eu
  
• http://www.sas.com/visual-analytics