Data visualization can help organizations make faster and better decisions. Here's how to incorporate it in your company's projects.
A 2017 survey by digital workplace platform provider Prysm revealed that 80% of organizations reported more accurate decision making when using data visualization tools, and 86% of companies said that data visualization enabled them to make decisions faster.
This news has important implications for big data and analytics, which are now both areas where companies expect to see direct business value quickly. For data analysts who are still toiling to deliver columnar or spreadsheet-style reports from big data, the implications are even clearer:
Business executives, line managers, and staffers still believe that a picture is worth a thousand words.
In other words, the more you can convert your reports into visual presentations that deliver immediate bottom lines, the more your users will perceive and derive value from your efforts.
SEE: Job description: Chief data officer (Tech Pro Research)
If you're in charge of big data and analytics projects, here are three things you can do now to maximize their use of data visualization in big data and analytics projects:
1. Adopt an approach where you visualize everything possible
Unless you have a project that is narrowly tailored so that it delivers data only (possibly a finance application), you should be asking yourself if there's a visualization product to deliver. This product could be as simple as a dashboard that gives the user a green, yellow, or red light to inform them how systems are running--or it could be a distribution of certain demographics across a district, a state or a country map to assist the marketing team in planning a campaign and targeting promotions.
2. Visualize the future
One of the most dynamic uses of visualization is what-if scenario modeling, which can show a projected situation in chart or picture form. A great example of this is advance weather forecasting that predicts an increased number of storms in a certain area over the next decade because of global warming. In this example, an analyst can develop a visualization that pictorializes a world map, overlays this map with projected weather storm patterns, and then adds to that layers of content that shows mission critical suppliers and transport routes. Planners can use this data to identify suppliers and trade routes that could be risky in the future, and make alternate plans to adjust for climate impact.
SEE: Turning big data into business insights (free PDF) (ZDNet/TechRepublic special report)
3. Train your analytics team for visualization
Companies can't deliver effective data visualizations unless their analysts are trained to work with data visualization tools and to look for opportunities to visualize data. This isn't a natural first step for most analysts, who are used to looking at standard text-based reports themselves. One way that CDOs, CIOs, and IT managers can attack this problem is to train data analysts to look for visualization opportunities and give them data visualization tools that make the job of visual report creation easier.
- Big data case study: How UPS is using analytics to improve performance (ZDNet)
- The Microsoft Graph is about to light up a new way for business users to search (ZDNet)
- Cheat sheet: How to become a data scientist (TechRepublic)
- Master the art of data visualization and presentation with this course bundle (TechRepublic)
- The best ways to sell your big data projects to the CEO (TechRepublic)