A new white paper from Datavail, Most In-Demand Job in America? Data Visualization!, explores the reasons data visualization is so valuable, so difficult to do well, and so hard to find in the skills marketplace. In this post, we’ll discuss what good data visualization looks like. What are the best practices in data visualization? Why is good data visualization so important to organizations? And how do you get the data visualization capabilities that you need to stay competitive?
What Does Good Data Visualization Look Like?
Good data visualization is in the eye of the beholder. It’s good if it works well for viewers or readers, so it must be designed with end user needs foremost in mind. A good place to start is by looking at the reports and other information that end users are currently using and mapping those to the functionality available in a Business Intelligence (BI) system.
Other characteristics of good data visualization are that it is:
- Fast. One of the main reasons for using data visualization is to communicate important insights more quickly than analyzing tables full of big data. So it’s important that data visualization works faster and delivers insights more quickly than, say, ratio analysis. Many BI systems operate with real-time data and deliver alerts as events are happening, not three months later.
- Clear. Data visualizations should make patterns more obvious, not harder to see. Clarity is often accomplished by highlighting or accenting critical information and obscuring or eliminating irrelevant data.
- Authoritative. The accuracy of data visualization recommendations can easily be tabulated and then graded with confidence intervals when presented. If your Data Visualization (DV) system is getting it right only half the time, it is no better than chance. Something’s wrong. Usually, the problem is the DV system is only getting half the data it needs to generate reliable visualizations.
- Clever. A data visualization system should consistently show you things you had not thought of, or make you see your operations in different ways. By making it easy to play with visualization techniques, data visualization software gives 360-degree awareness to operations.
- Engaging. DV has been accused of being bells-and-whistles and exploding color graphics. And that is partly true. Color, texture, and visibility all play a part in making data analysis more readily understandable, patterns easier to spot, and decisions quicker to make. The fact that data visualization is fun encourages its use and development.
- Shareable. In a world of dislocated employees and contractors coming together in cyber teams to accomplish enterprise management, data visualization has to work not only for the team but for their suppliers and customers as well. Good data visualization works on a smartphone or tablet or notebook or desktop system, on premises, in the cloud, and wherever there is a signal.
- Actionable. What do you want the viewer to take away from the visuals? Do the visuals make that point? Do they provide enough information for the end users to take confident actions?
Why is Data Visualization Important?
A teacher can tell you the Earth is round, but once you see a globe you achieve a whole new awareness of the Earth. The first photographic visualizations of the Earth seen from space as a blue-and-white ball in the black universe are credited for getting almost everyone in the world to recognize that we’re in this together.
Data visualization has the uncanny ability to not only quickly answer important questions such as how much an interest rate hike would slow housing sales in different cities, but it also suggests new insights or areas of inquiry that may be more important, like “Look at the size of the housing bubble in San Francisco! What’s behind that?”
“Accurate data visualization helps reduce costs, speed processes, spot patterns, and generate consensus. Collecting the data is the easy part; putting it into use is the hard part. According to Forrester, only 33% of companies are leveraging the data they collect to inform any type of useful insight. This white paper is about why your organization needs data visualization and how to not be one of the 66% of organizations that fails to leverage the data you are already collecting.” — Most In-Demand Job in America? Data Visualization!
Data visualization is behind breakthroughs in medical imagery, reductions in infant mortality and genetic sequencing, the fabulous returns of hedge funds, and even the science of message targeting through social networks – it’s not going away any time soon.
Best Practices for Data Visualization
The principles of data visualization have been essentially the same for a long time: Make it easier to see the most important information. The practices have changed with the use of computerized analysis, computer graphics, animation, and big data systems. Best practices now include security and shareability as well as clarity.
Here are some of the best practices in data visualization according to the Congressional Budget Office, tasked with helping the U.S. Congress to visualize the financial repercussions of various schemes, scenarios, and legislation:
- Know Your Audience. Good data visualization is in the eye of the beholder. Therefore, the wants and needs of the target audience have to be the starting point. What information are they after? What will help this audience understand these numbers more easily?
- Show the Data. Users want to see the numbers. Narrative is not enough. In most cases, it is too slow. A graphical representation of a complex dataset can lead to instant awareness of patterns or trends, which is usually what the target audience is after.
- Reduce Clutter. In addition to presenting the most important information in the clearest fashion possible, it is often necessary to get rid of irrelevant information or to unbusy the presentation so the pertinent patterns stand out more. A classic example is removing hash marks from an X-Y diagram when the exact amounts are unimportant and only the trend line matters.
- Integrate Text and Graphics. When you present the text information in a table adjacent to the graphic, it increases the work the user must do to interpret the results. An example is a roadmap that forces you to use a key to make sense of the map. To the extent the key can be incorporated into the graphic it will speed comprehension. Other benefits of integrating important information inside the visualization are that it makes the visualization more self-contained, more understandable out of context, and easier to share.
- Use Good Tools. The field of data visualization is fairly new and changing rapidly. Every year, new tools are released and software gets major upgrades. The capabilities are stunning. “If your tool doesn’t let you create the visualization you want, get a different tool,” says Peter Fontaine, Assistant Director for Budget Analysis and author of Telling Visual Stories About Data.
How to Improve Your Data Visualization Capabilities
Demand for data visualization architects grows every year, making it now the most in-demand job skill in the United States. Some companies have taken to partnering with educational institutions to support a curriculum in data visualization. These partnerships are not expected to dent the shortage of data analysis experts any time soon. Another way to get the talent you need is through a managed services provider.
“Contracting with a managed service provider allows organizations to gain the skills they are having trouble finding while leveraging the capabilities of their existing staff. Some companies offload the routine monitoring, ticket processing, and backups so their highly-trained staff can focus on things such as data visualization improvements. Others take advantage of the specialized experience the resources of a managed services partner offer, and engage the use of their data visualization experts – at a reasonable cost compared to that of a full-time data visualization employee.” — Most In-Demand Job in America? Data Visualization!
Getting better tools will improve your data visualization capabilities. If you have not seen the features in the new cloud versions of Oracle BI, IBM Cognos, or SAP BusinessObjects, it might be time to consider an upgrade. The ability to deliver data visualization to any device, anywhere, anytime has greatly improved the shareability of visualization engines.
You can also improve your data analytics with training. Datavail has experts in all BI systems and provides custom training for your team. Learn how to make better use of Oracle Business Intelligence Enterprise Edition (OBIEE), Oracle Business Intelligence Applications (OBIA), Hyperion, and more. The training can include any combination of personalized instruction, instructional videos and course materials created by Datavail content experts.
Business intelligence systems have the potential to revolutionize enterprise management However, it’s not easy to set them up or get them to deliver exactly the intelligence your team wants in a format they can use. If you’d like to improve your business intelligence data visualization capabilities, contact Datavail today. Datavail is a specialized IT services company focused on Data Management with solutions in BI/DW, analytics, database administration, custom application development, and enterprise applications. We provide both professional and managed services delivered via our global delivery model, focused on Microsoft, Oracle and other leading technologies.
For additional resources please download, Storyboarding in Oracle Analytics and Data Visualization and Discovery in Oracle Analytics.
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