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Why big data needs visualization to succeed

Read how data visualization can provide a shared language that simplifies complex issues and increases mutual understanding.

Overview

Big data may currently be all the rage in technology, but its popularity is generating some pretty big expectations. According to the hype, big data can provide operational efficiencies, grow revenue with existing customers and open up new markets. As such, IT leaders who made early investments in big data architecture and analytics should be reaping the benefits by now, right?

Not necessarily. According to research by Gartner, 85 percent of Fortune 500 organizations will be unable to exploit big data for competitive advantage through 2015. In today’s business world, the data management process is often ad-hoc, requiring significant time and money to set in motion. As a result, a simple request for data to guide a business decision can turn out to be quite complex.

Background

Data visualization is a more transparent, intuitive and contextual way to view data—beyond just the numbers.

As the volume and variety of big data grows, data visualization becomes even more important to spur a collaborative dialogue between these groups. When dealing with large volumes of data, visually grouping together many data points can help business executives, functional leads and data scientists understand relationships in data, debate questions in real time and more quickly decide where to focus research. For example, think about how a recruiting team can pinpoint and justify targets for sales hires by using a network graph from a professional networking website. This type of data visualization quickly communicates a candidate’s position in the industry based on previous business relationships. Armed with this information, the recruiting team can move more purposefully toward hiring the best sales executives.

Analysis

Data visualization can only be a panacea if it is the end result of a well thought out, end-to-end data management solution, which is no simple feat in itself. The process includes capturing high quality data, enabling access to it and analyzing it, while simultaneously communicating the data at every step along the way. Accenture believes that companies should apply more rigor and industrialization to how they discover and use data to drive business decisions.

Experience shows that companies can achieve better results by combining business acumen with qualitative information gleaned from analytical tools. Used correctly, data visualization can help remove barriers to data comprehension by providing a shared language that simplifies complex issues and increases mutual understanding—no matter how big the big data becomes.

Recommendations

Accenture research shows that there are five steps companies can take to better visualize data:

  1. Make data as ubiquitous as possible. Every hour spent hunting down an orphan database is hurting the return on investment of your analytics team.

  2. Enable analytics teams with collaboration tools to share, leverage and build on their prior successes. This working space should be open to peer review from your organization, and business users should be encouraged to participate.

  3. Provide a portfolio of data visualization tools. Although it may be tempting to require employees to use legacy software, the right business intelligence or visual analytics tool for the right job will pay dividends in efficiency.

  4. Build data visualization skills by embedding employees with design backgrounds into analytics teams.

  5. Leverage and explore low-cost open source tools to help make visualizing large data sets easier.

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