Every minute, email users send 204 million messages, Google receives over 4 million search queries, and Facebook users share 2.46 million pieces of content. According to Cisco, annual global IP traffic will pass the zettabyte (1,000 exabytes) threshold by the end of 2016 and will reach 2 zettabytes per year by 2019.
Organizations of all sizes and across all sectors are drowning in data. As a result, they are searching for tools that can help them mine and make sense of it, which is why IDC predicts that the big data and analytics market will reach $125 billion worldwide this year.
Data visualization has emerged as a hot topic in the tech industry (and beyond) as a way to help organizations understand and act on their data, but there are a number of misconceptions surrounding it. While data visualization is certainly useful, it is only useful as a component part of data analytics. If the visualization of your data is all you have, chances are that you are missing a significant piece of the puzzle.
Data Visualization versus Data Analytics
Let’s start by looking at the difference between visualization and analytics, and how the former plays into the latter.
Data analytics is the science of examining raw data for the purpose of drawing conclusions about that information. It is used in many industries and disciplines to direct decision making and scientific understanding. In contrast, data visualization is a general term describing technology that allows executives and other end users to “see” data in a format that makes information easier to understand. Data visualization takes raw data and puts it in a more user-friendly and appealing package. While visualizations can inform decision making (by making data easier to understand), they do not necessarily produce new insights – that is the role of analytics.
Without the analytics component, visualization tools are essentially dressing up the same old data and insights in new outfits. Just because you can see the same old data set in 40 different ways, or in a prettier format, doesn’t mean you need to. For example, a heat map, scroll map, and overlay report all show different views of the number of customers who are not converting to a website. While you might be able to understand where people are clicking and where they aren’t, these visualizations do not provide insight into the “why,” which is what really matters. The dataset is incomplete and does not reflect the big picture.
Paralysis by Analysis
The struggle to glean value from big data has sent the technology into the “trough of disillusionment.” A Gartner report found that the “gravitational pull of big data is now so strong that even people who haven’t a clue as to what it’s all about report that they are running big data projects.” With so much of one type of data and so many ways to slice and dice it, organizations are now spending more time analyzing and finding meaning in their data than they are spending making decisions and testing from it. This is paralysis by analysis.
What we need in order to overcome this paralysis is not more visualization tools. (A pivot table by any other name is still a pivot table). Rather, it is new sets of qualitative data that capture new aspects of the customer journey, such as sentiment and emotion.
Vast amounts of time and effort are funneled into behavior-based analytics, which measure things like how many users are on a site or app, the average number of pages a visitor views during a session on your site, or bounce rates. However, these metrics do not tell the full story. Maybe a customer leaves a site because of a negative emotion, but traditional analytics cannot understand what caused those emotions. Or perhaps a customer did not find what he or she was looking for. The retailer may be able to know what pages customers dwelled on and the clicks they ignored, but they still doesn’t have insight into the customers’ intent.
Human behavior is far too nuanced and complex to reduce down to clicks and keywords. Real behavior analytics requires capturing data from the entire human experience, such as body language and inflection.
Armed with this new, more complete and robust data set, organizations can begin to make real headway with their data and gain a meaningful understanding of their customers. This is how companies can move beyond paralysis by analysis. We don’t need more visualizations – we need more substance.
Collin Sebastian is Chief Product Officer at YouEye. He is responsible for all aspects of YouEye’s product and marketing vision and strategy. He is addicted to white boards, chess, and baseball, and does a horrible Sean Connery impersonation far too frequently.
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