big data

7 Things To Keep In Mind When Implementing Visualization For Big Data Projects

Powerful visualization for big data methods needs to go past simply painting lovely pictures for administration. Specialists say endeavors can enhance their outcomes by thinking about the format, designing iteratively, engaging clients and understanding business needs.

Aaron Kalb, vice president of design and strategic initiatives at Alation, a data catalog provider, expressed:

“The key is to tailor the specific visualization to its data, context, and audience — not to blindly follow any visualization rules’’.

Kalb and other specialists in the field had the 7 tips for organizations embarking on data visualization projects:

1. Keep the user in mind. Dan Gastineau, visual analytics practice lead at Aspirent, an Atlanta-based management consulting firm, said:

’’Use color, form, size, and placement to inform the design and use of your visualization.’’

2. Tell a coherent story. Address your audience and keep the design basic and centered. Minute points of interest like colors to the number of outlines can help guarantee that a dashboard recounts a sound story.

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Saurabh Abhyankar, senior vice president of product management at MicroStrategy, stated:

“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.’’ The design of dashboards will be a factor that drives adoption.

3. Prepare to design iteratively. Work on approaches that inspire lots of feedback from visual analytics users. Information investigation sparks new ideas and questions after some time, and making it more appropriate over the long haul and over selection makes clients smarter.

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4. Personalize everything. Ensure that the dashboard uncovers customized data to the end user, and make it applicable. Guaranteeing that the representations are responsive in design to the devices they’re on and offering offline access to end users will take it far.

5. Start with the analysis objective. Make sure that the data kind and analysis objective informs what visualization type is chosen. And as in words of Mihailovski:

“People often take a backward approach by seeing a neat or obscure visualization type and then trying to fit their data to it. A simple table or bar chart may sometimes be most effective for visualization for big data projects.’’

6. Keep governance in mind. This will probably take some time and effort, but it’s imperative that end users trust the information. Abhyankar expressed:

’’Gather all the help you need from a technology, process and people standpoint to ensure that the data is vetted and accurate.’’

7. Understand the business.
Being aware of the trends in the business is essential to enable users to apply the most recent measurements and examination to drive better business choices. Diverse dashboards should be imagined, remembering the end user. Management, analysts, IT and business clients will get an incentive from various kinds of visual examination investigations.

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