And that got me thinking about data visualization. No, seriously, it really did. I have been puzzled by the vibe surrounding data visualization for some time. To some it is just another technical fad, the latest bandwagon onto which people are climbing. But it’s a lot more significant than that; it represents a step change in the way that people interface with business applications. But what puzzles me about it is that it’s not new; so why all the excitement now?
Data visualization is simply this: the presentation of information in visual form. So, when you look at a chart showing the value of an index over a 10-year period you are experiencing the use of data visualization techniques. Data visualization is just about as far from being a new and radical concept, as the wheel. People have shared information using visual images for thousands of years. In fact, it is now well known that people process information better when it is presented in visual form than when it is presented in written or numerical form.
But it isn’t just the concept behind data visualization that isn’t new, we’ve actually been employing it in business for years. Think about business analysis techniques like data flow diagrams and entity relationship diagrams. And think about the charting features that have been available in Excel for over 10 years. In the asset management industry, we have been using data visualization techniques for well over 10 years; for example, the presentation of performance history as a line chart, and the presentation of portfolio holdings as a pie chart.
So, once again, why all the fuss now? I think there are 3 main reasons.
- Volume of data storage
Businesses are no longer constrained by restrictions on data storage. The cost of data storage reduces year-on-year and it has been estimated that the world's capacity to store data has doubled every 40 months since the 1980s. This means that 1) businesses can store much more of the same information than used to be the case, and 2) they can store much more detailed information. For example, 10 years ago data storage constraints placed severe restrictions on the ability of asset management organizations to calculate and retain daily performance metrics; that is no longer the case. But as the volume of data has increased, and as the granularity of that data has increased, the need for more sophisticated data mining, data analysis, and data presentation techniques has arisen.
- Multi-dimensional analysis
As well as being able to store more data and more detailed data, businesses are also increasing the complexity of information. This is because they are moving from one- and two-dimensional information to multi-dimensional information.
To illustrate this, consider a performance return. It’s one-dimensional in the sense that it tells us how an investment is performing at a particular point in time, which is of limited use. To enhance our understanding of how well an investment is performing we want two-dimensional information, we want to see performance returns over time so that we can see how the current performance return compares with returns we have achieved in the past. While we can easily understand a single performance return when presented as a number, we find it much easier to read a 4-year performance history when it is presented as a line chart, rather than as a table of 48 monthly returns.
Now consider an attribution analysis. This requires the presentation of multi-dimensional information: we need to show if an asset class was overweight, underweight or in line with the index; we need to show why we made that weighting decision; we need to show if that asset class performed well or badly; and we need to show these things for all of the asset classes in the portfolio. If that sounds like the presentation of complex information, that’s because it is. And the best way to present such complex information is not as a table of numbers, it requires data visualization techniques more sophisticated than line charts and pie charts.
The demand from investors and regulators for more transparency and insight from asset management organizations has increased in recent years and this has resulted in more analysis and presentation of multi-dimensional information, which is driving the need for data visualization techniques.
- Operational complexity
But probably the most significant change that is occurring is in the use of data visualization techniques to help businesses cope with increased operational complexity.
Asset management organizations today have to process more data and more complex data, while achieving higher levels of automation and straight-through processing. And that has led to the use of data visualization techniques to present information about the status of complex operational processes in ways that make it easy for business users to identify issues that need exception handling. For example, some applications now contain dashboards to show the status of workflow processes and heat maps to highlight data integrity issues.
And so returning to my recent walking experience. My difficulties with the instructions that were alleviated when I started using my GPS app made me realize this: the business landscape is more complex than it used to be and business users now need visual frames of references to help them navigate their way through the complexities of operational processing, value creation, and client management. And that’s why data visualization techniques, which have been around for decades, are now a hot topic in asset management. And system vendors need to ensure that their solutions contain strong data visualization features if they are not to become obsolete.
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