10 tips for implementing visualization for big data projects
November 1, 2018
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Organizations need to keep users and design at the forefront when launching data visualization efforts, according to experts. Find out why colors and sizing matter.
big data tips
Big data tips- Effective visualization for big data techniques need to go beyond just painting pretty pictures for management. Experts say enterprises can improve their results by considering layout, designing iteratively, engaging users and understanding business needs.
“The key is to tailor the specific visualization to its data, context and audience — not to blindly follow any visualization rules,” advised Aaron Kalb, vice president of design and strategic initiatives at Alation, a data catalog provider. Kalb and other experts in the field had the following 10 tips for organizations embarking on data visualization projects:
1. Keep the user in mind. Use color, form, size and placement to inform the design and use of your visualization, said Dan Gastineau, visual analytics practice lead at Aspirent, an Atlanta-based management consulting firm.
Aspirent uses color to draw attention to the aspects of the analysis that it wants users to focus on. Size effectively communicates quantity, but too much use of different sizes to communicate information can become confusing. Instead, size is employed selectively in places where consultancy team members want to emphasize a point. Form determines the shapes used to present an analysis: for example, whether to use line or bar charts to present certain types of information. Placement of objects is as critical to effective communication as the objects themselves, Gastineau said.
2. Tell a coherent story. Speak to your audience and keep design simple and focused. Minute details like colors to the number of charts can help ensure that a dashboard tells a coherent story. “A dashboard, much like a book, needs design elements that keep the reader in mind and does not simply force fit all the data one has access to,” said Saurabh Abhyankar, senior vice president of product management at MicroStrategy. The design of dashboards will be a factor that drives adoption.
3. Prepare to design iteratively. Work in ways to elicit frequent feedback from visual analytics users. Data exploration sparks new ideas and questions over time, and making it more pertinent over time and over adoption makes users smarter.
Solicit and incorporate feedback from your recipients to improve the experience. Building a quick proof of concept, getting feedback quickly and iterating tends to lead to a better result, faster, said Nick Mihailovski, lead product manager for Data Studio at Google. Even incorporating surveys and forms into polished reports can help ensure that the result of visualization for big data efforts indeed aids the intended recipient.
4. Personalize everything. Make sure that the dashboard reveals personalized information to the end user, and make it relevant. Ensuring that the visualizations are responsive in design to the devices they’re on and offering offline access to end users will take it a long way. Engage your audience, and propagate a data culture by using well-designed and interactive visualizations to make analytics engaging and fun, Mihailovski said. It should also be intuitive for employees to access, visualize and share their reports with live, dynamic data.
5. Start with the analysis objective. Ensure that the data type and analysis objective informs what visualization type is chosen. “People often take a backwards approach by seeing a neat or obscure visualization type and then trying to fit their data to it,” Mihailovski said. A simple table or bar chart may sometimes be most effective for visualization for big data projects.
6. Keep governance in mind. This might take work, but it’s important that end users trust the data. Gather all the help you need from a technology, process and people standpoint to ensure that the data is vetted and accurate, Abhyankar said.
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Article Credit: TechTarget
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