Data Science Now: The Impact of Data Visualization

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Featuring Scott Berinato

From October 13-14, 2021, Apra will host Data Science Now with a collection of high-impact virtual sessions. The event will address all aspects of successful data science integration in development offices including innovation, maximizing your toolset, communication, collaboration and goal setting. The education will focus on methodologies, tools and resources for conducting analysis and creating data-informed strategies and products among other things.

We spoke with Data Science Now keynote and author Scott Berinato to learn more about his upcoming session, “The Competitive Imperative of Good Charts” as well as the concept of data visualization, key steps to implement it within an organization and more.  

What is data visualization? Why is it important for prospect development professionals?

Technically, data visualization, or dataviz, is an information design technique that represents statistics visually. 

But that's a narrow, boring answer. I like to say dataviz makes the abstract actual. It turns 28% into a picture. It converts decisions into paths. It changes (29.955162846, -90.058999764) into a pin on the French Quarter on a map. Dataviz is our most powerful tool for making sense of the statistical (and sometimes conceptual) world around us because, at its core, vision is what the mind does. It's our best tool for forming and telling the story of what was, what is and what may be.

I like to say that learning to do a little better with dataviz is no longer a nice-to-have skill. It's a must-have skill. The world is awash in data, and that data comes at us in high volume and at high velocity. We need dataviz as the interpretable layer to help us make sense of this increasingly complex, data-driven world. Those who invest in it — individuals and organizations — will do better because others will be able to see what they mean. I call this the competitive imperative for dataviz.


 

Dataviz is our most powerful tool for making sense of the statistical (and sometimes conceptual) world around us because, at its core, vision is what the mind does.


 

What are the advantages of data visualization?

Processing capacity and narrative. The mind's ability to grok and make sense of pictures is unfathomably faster than its ability to make sense of 6,000 columns and rows on a spreadsheet, or even this paragraph of text. It makes trends and ideas instantly accessible. It helps us form stories about data so we can form our own opinions, make better decisions and persuade others. I'll have many examples (some funny, some serious) that show just how much more powerfully dataviz conveys ideas than raw data or even text can. More prosaically, if we do it well, others notice and better remember us and our information.  

How can organizations begin to implement dataviz in prospect development campaigns?

First, commit to improving. It's not as hard as you think and won't take as long as you think to get a little bit better, and getting a little bit better will have as outsized impact. Second, practice. Like any skill — guitar, cabinet making, selling — there will be failure that informs your improvement. I often suggest practicing on old material you won't be using in public-facing situations. Or, grab something off the internet and use that. Third, build a team of informal advisors, data types, design types and tech types that you can turn to with questions and for help. That's a start.

  What strategies do you suggest for figuring out what visualization consumers actually need to see, even when they have difficulty communicating that clearly?

My approach to dataviz is that context is everything. There is rarely one right visualization, but rather many options with benefits and drawbacks. It's the chart maker's job to understand the context in which the chart will be used to make it as effective as possible. This involves talking through the context with someone and taking notes on what you're saying and hearing, followed by sketching out possible solutions. I'll be talking about this in detail and showing examples how even spending 15 minutes with setting context can completely transform charts.

  What are simple techniques that people who struggle to give presentations can follow?

This will also be a core topic in my talk as I walk through some of the powerful presentation techniques that can keep audiences engaged and make good charts even better. Simplicity and clarity are important (and they're not the same), but so are some unexpected techniques like creating drama and using building blocks. I'll show some of these techniques in action at the event.

Data Science Now takes place online, October 13-14. Learn more and secure your spot today.

 

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