Analysts must strike a balance between focusing on substance and focusing on style in order to achieve the perfect data visualization strategy.
One of the strongest and most vibrant trends in analytics at the moment is data visualization. Hal Varian, chief economist at Google, said five years ago that "The ability to take data - to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it - that's going to be a hugely important skill in the next decades..." and it looks like that that prediction is spot on. The success of tools such as Tableau and others demonstrates the appetite in the market.
Data visualization is as old as measurement and analytics. We've always needed to see the data. Some of the greatest examples of data visualization are classics. Charles Minard's 1869 depiction of Napoleon's march on Moscow is often considered the world's first and finest infographic.
Dr. John Snow used mapping techniques in his analysis of cholera outbreaks in London in 1854 and it's widely credited with being the first geo-spatial analysis of its type.
The principles of data visualization are also well established. Edward Tufte published his seminal work The Visual Display of Quantitative Information in the 1980s and, more recently, Stephen Few published his book on Information Dashboard design in 2006. So data visualization is nothing new, but now it's all the rage. The growth in the demand of data visualization is the inevitable consequence of the growth of the amount of data we need to consume, digest, and extract value from. Typically, we are rich in data and poor in insights. Tools like Tableau and others give us the opportunity to explore and present our data in ways that we have struggled to before. But with opportunity there are also risks and challenges.
One challenge I characterize as "Style Over Substance." This is the executive eye candy. Visual analysis is a process of investigation leading to a conclusion or outcomes. At the end of it all, there has to be a punch line. With the technology at our disposal today it's easy to come out with beautiful looking dashboards or displays that lack any analytical rigour or have low informational value. It might look good, but what it is actually telling you?
Equally, another challenge is "Substance Over Style." Great analysis can often be obscured by poor presentation. This is a challenge that has been typical over the years, as analysts aren't necessarily the best presenters of their work, in either written or visual formats. We've all sat through presentations in the past full of dense charts wondering what the punch line is going to be. That's not a criticism but a feature of the ways that we've traditionally educated and trained people in the past. As Hal Varian pointed out, we now need to empower analysts with visualization and communication skills. In terms of data visualization, this is developing an understanding of design principles so that the tools are used to best effect.
These more rounded "visual analysts" are going to become hot properties. Like in all emerging analytics fields, such as digital analytics over the past decade, good practitioners are going to be in high demand. This will result in the "build vs. buy" debate for organizations adopting visual analytical technologies. Do I take my existing talent and train them up or do I buy people in who already have the experience and expertise? There is no ready answer to that question, as each organization's circumstances are different. My personal preference in the past has been to "build," as you generally know what you're building versus knowing what you're buying. However, I recognize that it's not always possible to do that.
Another challenge I think in the world of visual analysis and data visualization is overcoming the risk of "Interest Over Action." This is more of a technology challenge. Great insights may be delivered (substance delivered with style), but how easy is it to take action as a result? The other marked trend at the moment is toward marketing automation and workflow, and visual analytics needs to be able to fit into that type of ecosystem. This is about technology interoperability and whilst it can be relatively easy to get data into these systems, it's also increasingly important to understand these days how to get the insights out for further use elsewhere.
A common reason for technology adoption can be because everyone else is doing it. That's not necessarily a bad thing. Trends can exist for a reason and I think the trend toward visual analytics and data visualization is a positive one. But adoption is one thing and exploitation is another. Will your visual analysts make Charles Minard proud? That's assuming that you're a fan of Charles Minard in the first place.
Image via Shutterstock.
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Neil Mason is SVP, Customer Engagement at iJento. He is responsible for providing iJento clients with the most valuable customer insights and business benefits from iJento's digital and multichannel customer intelligence solutions.
Neil has been at the forefront of marketing analytics for over 25 years. Prior to joining iJento, Neil was Consultancy Director at Foviance, the UK's leading user experience and analytics consultancy, heading up the user experience design, research, and digital analytics practices. For the last 12 years Neil has worked predominantly in digital channels both as a marketer and as a consultant, combining a strong blend of commercial and technical understanding in the application of consumer insight to help major brands improve digital marketing performance. During this time he also served as a Director of the Web Analytics Association (DAA) for two years and currently serves as a Director Emeritus of the DAA. Neil is also a frequent speaker at conferences and events.
Neil's expertise ranges from advanced analytical techniques such as segmentation, predictive analytics, and modelling through to quantitative and qualitative customer research. Neil has a BA in Engineering from Cambridge University and an MBA and a postgraduate diploma in business and economic forecasting.
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