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How To Work Through “Analysis Paralysis” And Understand The Difference Between Data Analysis And Data Visualization

Collin SebastianIn this special guest feature, Collin Sebastian, Chief Product Officer at YouEye observes that the more graphs, maps, reports, and dashboards we use to slice and dice our data, the more time we spend analyzing — and the less time we spend actually making decisions. There’s a solution to this paralysis, and it begins with understanding the difference between data analysis and data visualization.​ Collin brings over 12 years of marketing and product leadership experience to YouEye, including four successful exits. As Chief Product Officer, he oversees the strategies for product development and market positioning, as well as the day-to-day operations for both teams. Prior to joining YouEye, Collin served as Director of Marketing at Ubiquiti Networks and held management roles at several high-growth startups.

Cisco estimates that annual global IP traffic will pass the zettabyte (1000 exabytes) threshold by the end of 2016, and will reach 2 zettabytes per year by 2019. The amount of data is exploding at an astounding pace, as more and more people come online and use the Internet in new ways.

Data is playing an increasingly important role in the way organizations make decisions, but the sheer volume of data can make this difficult. In fact, 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’re running big data projects.”

IDC predicts that the big data and analytics market will reach $125 billion worldwide this year, as organizations actively seek out tools to help them handle the deluge of data. Data visualization has gained momentum as a result, but there are a number of misconceptions surrounding it.

Data analytics is the science of examining raw data for the purpose of drawing conclusions about that information. Data visualization, on the other hand, is a general term for technology that allows executives and other end users to “see” data in a format that makes information easier to understand.

Data visualization is only useful as a component of data analytics. Visualizations can inform decision-making by wrapping data up in an easier-to-understand, more attractive package. However, they do not produce new insights. A pivot table is still a pivot table, no matter how many different ways you dress it up, and the same old data in a new outfit doesn’t get you very far.

For example, you can slice and dice the data surrounding click rates in countless different ways (heat maps, scroll maps, overlay reports, etc.), but no matter how you slice and dice it, you are still seeing the same limited picture. Visualizations do not yield insight into why people click or don’t click, which organizations need to gain true understanding into their customers and take action.

All these tools for visualizing analyzing and visualizing data has led to analysis paralysis. Organizations are spending more time analyzing and trying to find meaning in their data than they are making decisions and testing from it. They are stuck. Overcoming this paralysis does not require more visualization tools, but rather, new data. New sets of qualitative data, which capture new aspects of the customer journey, is essential to moving forward.

Most existing data analytics and visualization tools are behavior-based, meaning they measure things like how many users are on a site or app and bounce rates. The problem is that these metrics do not complete the puzzle. You may know the number of times a visitor came to a site and how long they spent there, but what does that really tell you? Why did they leave? If they had a negative emotion, what caused that? Traditional analytics do not factor in emotion, and thus, fail to deliver on the full promise of big data. Human behavior is complicated and nuanced. It can’t be reduced down to clicks and keywords. Companies need new, more comprehensive data sets that include the human element — like sentiment, emotion, intent, body language, inflection and more — if they hope to gain meaningful and actionable insights into their customers. It’s time to focus less on the surface, and more on the substance.

 

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Comments

  1. Definition of Analysis Paralysis:
    Thursday.

    The Devil’s Data Dictionary
    http://bit.ly/devilsdata

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