Data Visualization 101 Recap – Top 5 Questions & Answers:
March 14, 2018
Seth Appel, RRD Global Outsourcing's VP of Productivity Solutions and Data Visualization expert, answers common questions from participants of the Data Visualization 101 Webcast Series.
1. Why should non-data professionals care about data visualization?
As data collection and preservation becomes cheaper, the range of business decisions required to be backed by quantifiable evidence will expand.
Because of this, over time we'll see the disappearance of "non-data professionals." Ultimately, everyone will become a data scientist of sorts and be expected to possess some degree of data fluency.
2. What advice do you have for organizations starting their journey to use data visualization as a management tool?
Start small and be realistic. I recommend organizations first select a management topic already data-rich as a pilot project. Ideally you can identify a specific management decision that would be better informed with data visualization. Resist the temptation to create a visualization just because it looks good.
You'll also want to make sure your final demo visualization looks spectacular. A good visualization is easy on the eyes, hard to forget and provides critical insight to viewers. And a final but critical step–share your product with good critics across the organization to get constructive and sharp feedback. A second set of critical eyes can really help reveal design challenges that need resolution.
3. What are the biggest obstacles to reaping the benefits of data visualization?
Based on my experience and real world observations for clients, by far it's the initial learning curve. Professionals who are data adverse find comfort in the old status quo which saw data as the exclusive domain of data scientists. A future in which everyone is expected to be data literate is, however, already here. The ability to capitalize on the power of analytics is fast becoming a key differentiator in the market place.
How does your organization get over this hurdle? Find ways to educate your teams on the power of this new tool for their projects. For example, circulate articles and topical whitepapers. Or arrange in-person training sessions or customized webcasts with data visualization subject-matter experts like RRD.
If interested, please email me at email@example.com or complete this form. RRD also conducts its Data Visualization 101 webcast frequently. Follow our LinkedIn page to get alerts on our next webcast event.
4. What tools do you recommend and what licensing costs are involved?
Personally, I'm an avid user and fan of R®, and specifically of R Shiny® and ggplot2®. The syntax of R allows one to manipulate massive data sets in moments. The data visualization possibilities in R are powerful and vast. And if that wasn't enough, R is open source so it's free. The one possible obstacle for some individuals, R is a programming language with a challenging and possibly long learning curve so it is clearly not for everyone.
Besides R, a simpler tool is Tableau®. It has plenty of data reporting capabilities without the vast range of analytical packages available in R. The good news–Tableau is fast adding more analytical tools to its software. Of course the big downside is the licensing costs.
Another option is Microsoft Power BI® which has lower licensing costs but a more narrow spectrum of creative possibilities.
Overall we are seeing the end of the Microsoft Excel® era in which simple spreadsheets and pivot tables were considered the tools of choice when tackling the data challenges of routine business.
I've curated my own list of data visualization training resources. If interested, email me at firstname.lastname@example.org–it includes blogs, books, films and some good TEDTalks recommendations.
5. What big changes should we anticipate on the horizon?
Right now top data scientists are working on a generation of tools that will render very advanced analytics easy to understand and user friendly for those with even just a rudimentary data education. For example, machine learning is considered advanced analytics and today most often requires the direct implementation of a data scientist. But new and emerging tools will soon render machine learning as common as pivot tables are today. Also off-the-shelf standard tools, like Tableau and Power BI, will increasingly offer capabilities to standardize and automate what is considered specialty and high-end analytics today. The challenge will be for managers and organizations at large to have the needed data fluency to capitalize on these emerging opportunities.
To learn more about RRD's Data Artistry and Data Visualization support services please contact Seth Appel, VP of Productivity Solutions at email@example.com
[End of Interview]
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