Making Sense of Your Data with Visualization Tools:
Big data sets are being tapped by everyone, from big-box retail outlets to your local grocer. Marketers are leveraging data to upsell you on hair styling products; pharmaceutical companies are examining patient data to test new drugs; recruiters are scouring social media data for top talent; even online dating services are crunching numbers to form romantic partnerships.
In fact, research firm Research and Markets values the data visualization market at more than $4 billion, and
Gartner reports that there will be an estimated 30 percent compound annual growth rate of visualization-based data discovery tools this year.
But before an organization can act on its data, it needs to make heads or tails of it. This is especially true if a
department plans on basing key business decisions on data. Suddenly, all those bits and bytes must be packaged in such a way to convince senior-level execs that it’s worth selling to a particular target demographic, investing in a new product, or hiring a new engineer.
Fortunately, data virtualization tools can help by converting cold, hard numbers into highly visual maps and charts. It’s what Cole Nussbaumer describes as “creating a-ha understanding of data without the need to have a PhD in physics.” Nussbaumer would know. She’s a data visualization expert who holds custom and public workshops on the art of storytelling with data.
These days, organizations need data visualization tools to process multiple streams of disparate data and turn them into something that even a layperson can comprehend. What’s more, data visualization tools must be able to dig deep
What to look for in a data visualization tool
Today’s data visualization tools provide everyone from data analysts to line-of-business leaders with a technology for designing graphs and charts that tell a compelling story. “Some of these solutions have been designed in a way that it’s extremely easy for even novices to start creating something that’s not too complex,” says Enrico Bertini, professor of computer science at NYU who teaches a course in data visualization.
Here are the key components you should look for in a data visualization tool:
Interactive graphics – Look for a variety of visuals such as box plots, heat maps, graphs. The greater number of graphic types available, the greater the likelihood that users will be able to make sense of the data without needing programming knowledge or specialized skills.
Easy report building – A Web-based, interactive interface should allow users to easily preview, filter or sample data prior to creating visualization.
In-memory processing capabilities – In-memory computing tools enable companies to process data more quickly. Enterprises can crunch and retrieve massive amounts of data stored in a computer’s main memory rather than on a disk-based storage system.
User-friendliness – Whether you’re dragging and dropping to create views or rearranging a dashboard, you should be able to do this with a few clicks of the mouse. If you have to start programming, or tie up your IT department, then you’re using the wrong tool.
Sharing capabilities - Data visualization is often a collaborative approach involving a number of teams across departments. To ease this process, it’s important to select a tool that allows users to easily drag different views into the dashboard, easily add to them, and then point and click to share a live and interactive dashboard right on a web page. Greater collaboration is also possible when users can distribute their visualizations via mobile devices or web portals.
Multiple deployment options – Many organizations prefer cloud-based technologies because they’re easy and affordable to use, eliminating the need for investments in hardware and additional manpower. Others, however, may want to make better use of existing in-house Web-based applications with an on-premise solution.
Danger signs
For all their perks, data visualization tools still present their fair share of challenges. For starters,
organizations need to determine whether they have the in-house talent needed to manage these tools. At the high-end, users require knowledge in programming, stats and information architectures. At the
1. Identify your target audience. Will your heat map or graph be viewed by numbers-oriented finance folks? Or is your marketing department more likely to respond to a stronger visual aesthetic? Always make sure you determine who will be seeing your visualization, what they’ll want to do with the
information and how they’ll want to see it presented.
2. Keep it simple. All those arrows and highlighted text are what experts refer to as “chart junk” – bells and whistles take up precious space and fail to add any real value. Always be sure to carefully edit your visualization by identifying not only what you need, but what you don’t need based on your audience’s needs.
Security, however, is a much more legitimate concern. Says Nussbaumer: “Anytime you give free access to data, there’s a danger that you’ll have people manipulating it or who won’t know what they’re doing with it.”
10 Best Practices
Data visualization tools themselves are still relatively new. All of which means many organizations are still struggling to determine how best to use them, who should be using them and how to get the most out of them. Here are 10 best practices every organization should have in place to reap the most value from data visualization.
In addition to decluttering your visualization, it’s important to strip down the message you’re conveying. What is the story you wish to tell with your data? Is it likely to resonate with your audience? It’s smart to tell a compelling story with bread-and-butter graphs, bar charts and line graphs that have to educate your audience on how to read a chart. “When you’ve taken the time to think about the data and the story you want to tell with it, it should be the data that stands out, not all of the glitz that’s thrown on top,” says Nussbaumer.
3. Make it safe. Strict user provisioning, which determines who can and cannot access a visualization tool’s data, is one way to ensure security. Another approach is to play with visualizations in the
sandbox rather than a live production environment. If mistakes are made, they can easily be cleaned up in a sandbox without impacting the business.
4. Check for quality. No matter how sophisticated the data visualization tool, if your data isn’t clean and of high quality, you’re not going to tell an accurate story. For this reason, it’s critical that organization invest in quality assurance tools and measures. Incomplete account information, duplicate CRM records, unstructured open data sets – they can all stand in the way of accuracy. Master data management (MDM) technology can help by delivering a single and trusted view of the customer across the enterprise.
6. Don’t get overwhelmed. With data flowing in from social media channels, sales transactions and third-party data brokers, it’s easy to get overwhelmed with information. “Oftentimes, people experience analysis paralysis where they’re trying to look at everything and there’s no clear story or they haven’t taken the time to create their story,” warns Nussbaumer. To prevent such an occurrence, before you start fiddling with your visualization tool, think of what story you might want to tell.
Narrow down the narrative and then sift through the data for supporting evidence.
7. Offer training. For many, the ability to weave a great story from bits and bytes is a natural talent. But that doesn’t mean you can’t teach your IT professionals and business-line leaders to use data visualization tools effectively. In fact, a little bit of domain knowledge, such as a background in finance or marketing, can go a long way towards honing your skills. In fact, even Harvard is now offering courses in data visualization to its students.
8. Pick the right tool. No amount of schooling or natural talent can help an organization overcome poor tool selection. With so many solutions out there, it’s critical that companies take the time to select one that’s right for their needs. Questions to ask: what kind of learning curve are you
comfortable with? What quality of data are you working with? How quickly do you want to be able to flip through different visualization templates?
9. Encourage curiosity. Once you’ve created your visualization, continue to ask questions of your data. These follow-on questions can lead you down different paths, and eventually brand-new insights. Better yet, encourage recipients to ask questions of the data for a fresh pair of eyes.
10. Be willing to rethink it. Humans’ eagerness to embrace all things visual can backfire. For
example, some visualizations can be misleading or riddled with errors. For example, the Washington Post once published a map of the world’s most and least racially tolerant countries. Despite a
number of flaws identified in the Post’s methodology and data, the map went viral and gained fast acceptance as truth. For this reason, users must always be open to the fact that their visualizations may be error-prone, and that it’s never too late to vet a map or graph for miscalculations. “It’s so easy to be misled by data,” warns Bertini. “Everyone in the field who is serious about visualizations knows that this is a big problem.”
Organizations that are figuring out how to make the most of big data are organizations that have
embraced data visualization tools. That’s because these powerful solutions provide organizations with a powerful means for teasing out actionable insights. The best part: if carefully selected, these tools can democratize data, enabling users of varying skills and backgrounds to tell a compelling story.
