Seeing data through all the numbers
Well 2013 is over and 2014 is upon us. What does it mean? What are the trends going to be this year?
I'm not going to join in that game but I am going to spend a little time talking about something that's been on my mind for a while, namely Data Visualisation. This is the term used to describe graphics that communicate facts and figures.
These are used by a variety of "data people" from finance departments to marketing managers, journalists and beyond. This article will look at a few of the common tools and good examples to see how data visualisation is being used to change the way data is used to bring value to businesses and people.
Data visualisation isn't new. If you've ever looked at a weather forecast or heat map produced from the census then you'll be familiar with what data visualisation can be.
More recently, the growth of data analytics (I'll try and avoid saying Big Data here) has led to a parallel growth in the types of data vis used.
While many statistics are still best viewed on a map (as we all implicitly understand place), people are finding new ways to understand patterns in data. The Wordle on the previous page is a good example of turning text (that great unstructured source of information) into an easy-to-grasp patten analysis.
What about 'normal' data? Facts, figures and numbers. Well there are plenty of tools available and for a great run down, try this blog article by Sam Hampton-Smith and Brian Suda.
In my world of data products, I've become quite familiar with credit scores. If you've ever seen your own (or your business') credit score on products such as Experian Credit Expert or Noddle you'll remember that a score is often pinned onto a sliding scale or arc with the colours going from red (bad) to green (good). Note: I work for Experian so please remember, this is not a plug for my company and my views are my own.
Simple stuff. Now, what if someone wants to understand another factor of their life which, like a credit score, can impact the products and services that they can access?
Let's look at where you live. Can that influence how companies see you? Well, in the case of insurance, yes.
Insurance companies have been putting information about 'place' into their decisions for a long time. Is it in a flood plain? How high are burglary rates? Is it close to heavy industry?
This kind of information is often displayed on a good old map and today is linked using identifiers such as postcodes, co-ordinates or other unique identifiers.
So, if the insurance companies have all of this data and the tools to see it, can you get hold of it yourself to help you understand your likely bills if you move home (or more importantly, whether or not to move)?
Increasingly, this is possible. With more sources of Open Data such as climate & weather from the Met Office, crime stats from the Police, planning data from local authorities and house prices from the Land Registry, we are beginning to get a good picture of the effect that place can have on our lives.
But how is this being visualised? On property website Zoopla, you can compare the demographics of postcodes using their simple tool. As an example, there are nearly twice as many single parent families in an area of East London near the Olympic park compared to an area near Notting Hill.
Bar charts like the ones seen here are nice and simple and bring together different types of information to help you make a decision. The site goes further with crime stats, council tax rates and so on. Handy!
But what if you want to see everything in one place, there are more ways to do this. The excellent application Illustreets has attempted to bring a lot of the data I mentioned earlier together. It creates heatmaps based upon your selections.
A potential future for this kind of application is to put your own data in (such as energy bills, wages, work location and so on) to create a much more detailed picture of what your life could be like if you lived in place A or B.
What will be required though is a range of visualisation tools such as those seen on Plotly. Heck, you could even add social network sentiment about areas and see how neighbours are influenced by one another.
What's obvious though is that through the combination of the web, open data, cloud storage services and our own growth in basic use and understanding of data visualisation, we will all be able to make sense from the noise more quickly and easily. We just need the tools to do it!
Further Reading
For some excellent examples of data visualisation, check out the excellent book 'Information is Beautiful' by David McCandless (and online).
I'm not going to join in that game but I am going to spend a little time talking about something that's been on my mind for a while, namely Data Visualisation. This is the term used to describe graphics that communicate facts and figures.
These are used by a variety of "data people" from finance departments to marketing managers, journalists and beyond. This article will look at a few of the common tools and good examples to see how data visualisation is being used to change the way data is used to bring value to businesses and people.
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| A wordle of this blog |
More recently, the growth of data analytics (I'll try and avoid saying Big Data here) has led to a parallel growth in the types of data vis used.
While many statistics are still best viewed on a map (as we all implicitly understand place), people are finding new ways to understand patterns in data. The Wordle on the previous page is a good example of turning text (that great unstructured source of information) into an easy-to-grasp patten analysis.
What about 'normal' data? Facts, figures and numbers. Well there are plenty of tools available and for a great run down, try this blog article by Sam Hampton-Smith and Brian Suda.
In my world of data products, I've become quite familiar with credit scores. If you've ever seen your own (or your business') credit score on products such as Experian Credit Expert or Noddle you'll remember that a score is often pinned onto a sliding scale or arc with the colours going from red (bad) to green (good). Note: I work for Experian so please remember, this is not a plug for my company and my views are my own.
Let's look at where you live. Can that influence how companies see you? Well, in the case of insurance, yes.
Insurance companies have been putting information about 'place' into their decisions for a long time. Is it in a flood plain? How high are burglary rates? Is it close to heavy industry?
This kind of information is often displayed on a good old map and today is linked using identifiers such as postcodes, co-ordinates or other unique identifiers.
So, if the insurance companies have all of this data and the tools to see it, can you get hold of it yourself to help you understand your likely bills if you move home (or more importantly, whether or not to move)?
Increasingly, this is possible. With more sources of Open Data such as climate & weather from the Met Office, crime stats from the Police, planning data from local authorities and house prices from the Land Registry, we are beginning to get a good picture of the effect that place can have on our lives.
But how is this being visualised? On property website Zoopla, you can compare the demographics of postcodes using their simple tool. As an example, there are nearly twice as many single parent families in an area of East London near the Olympic park compared to an area near Notting Hill.
Bar charts like the ones seen here are nice and simple and bring together different types of information to help you make a decision. The site goes further with crime stats, council tax rates and so on. Handy!
But what if you want to see everything in one place, there are more ways to do this. The excellent application Illustreets has attempted to bring a lot of the data I mentioned earlier together. It creates heatmaps based upon your selections.
A potential future for this kind of application is to put your own data in (such as energy bills, wages, work location and so on) to create a much more detailed picture of what your life could be like if you lived in place A or B.
What will be required though is a range of visualisation tools such as those seen on Plotly. Heck, you could even add social network sentiment about areas and see how neighbours are influenced by one another.
What's obvious though is that through the combination of the web, open data, cloud storage services and our own growth in basic use and understanding of data visualisation, we will all be able to make sense from the noise more quickly and easily. We just need the tools to do it!
Further Reading
For some excellent examples of data visualisation, check out the excellent book 'Information is Beautiful' by David McCandless (and online).
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