There was a time when data visualization was considered a fancy art. Times have changed, and today we are drowning with data of all shapes and form. Gartner predicts a 650% growth in enterprise data in five years of which 80% will be unstructured. Can you imagine dealing with the massive amounts of data to derive insights?
Visualizing data is becoming a mainstream big data analytics component where traditional art of visualizing data meets data science and predictive analytics. Today, 22% of all big data analytics revenue comes from software solutions and this is expected to rise drastically as a new breed of data visualization startups compete for the main stage.
The question remains, how you as digital leader, business or marketing analyst, business user or data scientist will stay on top of your data? How can you make sure you go from the raw data to data mining to the business outcomes in shortest time?
I would recommend adding data visualization skill set to your portfolio. It does not matter if you focus is marketing, customer analytics, or customer support. Data Visualization can help you be on top of the structured and the unstructured data sources. You can quickly understand the flow of information, identify key segments, outliers, significant values or other insights.
Here are some of the steps to follow to develop data visualization skill set:
1. Pick your platform: It can be an overwhelming task to find the right data visualization software as there are many options. If I were you, I would pick the most popular and most widely used solution such as R (open source), Tableau or Pentaho and then stick with it. It will save you ton of time as every day a new solution may popup and it could lead to distraction.
2. Get trained: Since you have already identified the tools in the previous step, I would suggest visiting the vendor’s website, as they will have the best learning resources for their tools. For open source languages such as R or python, Coursera, Udacity or Datacamp have good programs.
I like the convenience of learning from my home office. Coursera offers a 4-week data visualization course from University of Illinois and Udacity offers a more advanced 7-week course.
3. Practice the art: In his book Outliers, Malcolm Galdwell introduced the 10,000 hour practice rule to become a world class expert. Some studies argue this may only work for certain domain. However, data visualization is certainly a domain where you have to practice to become good at building insightful visuals. There is no shortage of practice problems and competitions. Just pick one dataset every day and build a visual using your favorite tool.
4. Learn from the best: One of the fastest way to shorten the learning curve is to learn from the best examples. Few years ago, I wrote a post where I shared the 20 top data visualization examples. I received a ton of positive feedback and bunch of social shares so I decided to take the idea a bit further. I just finished writing my latest ebook called the “20 Powerful Big Data Visualizations”. My goal was to find the best and latest data visualizations projects and share why these stand out from the pack. Instead of just sharing the visuals, the book also provides details of the analysis so you can think analytically.
Here is what you can learn from this 25-pages of content and visual rich ebook:
- How to win data visualization competitions like MIT pros? (page 8)
- 30 data visualizations you can stick to your wall. (page 7)
- What do Alaskans crave more than taxidermy? (You will be surprised!) (page 11)
- Two factors that lead to massive social change. (It’s not what you think.) (page 15)
- Free apps to shorten your flight time and work commute. (page 23)
- Proof: 5.6 magnitude man-made earthquake. (Get ready for an even bigger one!) (page 20)
…and many more plus you’ll also get FREE access to my exclusive big data, digital marketing and analytics newsletter.
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