Data visualization will be most effective by identifying qualitative versus quantitative data, selecting the right tool and visual types, and ensuring that the information presented is easy to grasp and digest.
The value of big data is unquestionable, but the volume, variety, and velocity of information assets can make extracting actionable insights a challenge. Many organizations are overwhelmed by their business intelligence, with experts stating a 70% to 80% failure rate for corporate business intelligence projects. Because these corporations are unable to connect crucial dots and receive little return on the investment of resources, much of the data that’s collected goes to waste. In these cases, there is a very real risk of falling behind more data-savvy competitors.
As the three Vs continue to increase, these issues will only grow for enterprises that can’t fully leverage their data to generate real value. It’s not enough to simply accumulate information; businesses must be able to make better sense of what they have. Data visualization is central to this endeavor.
Beyond the Basics
Research shows that human beings can process visual data 60,000 times faster than they can process text-based data, making visual information more easily comprehensible and, as a result, actionable. Data visualization exists in many shapes and forms in today’s market, and it continues to expand. While the most common tools are spreadsheets, charts, and graphs, there are a number of other ways to present data in a visual medium.
Infographics illustrate data with images and intuitive graphics that emphasize key points. Slide-based visualizations can simplify complex concepts by breaking them down into digestible chunks that are easier for laypeople to understand. Interactive visualization allows users to browse data on multiple scales and bring various pieces of information to the forefront for more thorough analysis and comparison. Each visualization tool uses a different assortment of data to provide a diverse range of insights.
No matter which visualization technique is used, the aim should be to tell a story with the data.”Don’t just project the idea that you’re showing a chart,” presentation expert Nancy Duerte advises. “Project the idea that you’re showing a reflection of human activity, of things people did to make a line go up and down. It’s not ‘Here are our Q3 financial results;’ it’s ‘Here’s where we missed our targets.'”
Storytelling is integral to data visualization, giving data analysts a target and providing end users with the ability to make decisions based on a comprehensive collection of facts. Companies need to understand the type of data that they are sifting through and what type of story, message, and purpose they are looking to tell.
Leveraging Data Visualization Effectively
Companies already own a wealth of insights in the form of documents, log files, emails, social media posts and other data points originating from the Internet of Things (IoT). Some of this data provides information that can be used to forecast future circumstances, while other pieces of data serve as evidence for the feasibility of a plan or provide answers to specific questions.
Unfortunately, while most companies don’t have a problem amassing the information they need, they may not know how to take full advantage of their data. The problem is that although most executives see analytics as a tremendous opportunity, too many “don’t know what to do with it,” says Dave Dimond, chief technology officer at EMC’s Global Healthcare Business.
Data visualization will be most effective by identifying qualitative versus quantitative data, selecting the right tool and visual types, and ensuring that the information presented is easy to grasp and digest. These best practices can help ensure that data analytics are being displayed effectively:
- Designs should be intuitive and usable by data professionals, executives, staff, and outside parties.
- Visualizations should be simple and uncluttered, displaying only the most pertinent information.
- Elements should be labeled and explained in plain language.
- Real-Life Use Cases
- Data visualization offers companies of every industry the ability to uncover the obscure or otherwise overlooked insights enshrouded in their data.
The healthcare industry can make great use of visualization as patient data continues to grow year by year, day by day, and even minute by minute. Trends for treatments and diagnostics can be identified to determine what’s working and what’s not, and providers can better assess how and at what level to initiate care.
Providers can also gain a better picture of gaps in care and take appropriate measures. They can track patient health and assess it using indicators such as color codes, symbols, and images to simplify record keeping and collaborative care. HealthCatalyst, for instance, initially launched an Enterprise Data Warehouse (EDW) to improve its capabilities around managing individuals with heart failure. Clinical dashboards were created to analyze patient data and develop “best practice interventions.” This proved to be a success and led to the development and deployment of another EDW-powered dashboard for ambulatory population health management. The proven value of big data and data visualization for health organizations allowed HealthCatalyst to recognize the capabilities it can apply to multiple usages across its services.
For consumers, U.K.foundation Nesta utilizes visualizations to delineate the most sought-after job skills from millions of online job ads. Data visualization also provides information about trends over the past few years, helping job seekers decide where to focus their skill-building efforts.
On the political front, CNN Politics launched a data-driven app in 2016 that tracked polling, delegate, voting and fundraising data in real time. This helped voters understand who was winning during the campaigns and gave insight behind the visualizations and stories with personalized alerts and notifications that delivered relevant data to the target user. This provided a seamless user experience that aimed to drive traffic and increase subscription, among other metrics, for CNN.
Increasing Data Visualization Adoption
A study by GE Global Innovation Barometer 2016 found that 61% of senior executives use big data to inform decision making. Although that figure is up from 53% in 2014, it’s still below where it could, and more importantly should be. This reality isn’t lost on business leaders – 41% of companies admit that their ability to drive actionable insights from their analytics and data visualization could use some improvement.
Identifying the Sources of Hesitancy
This phenomenon can be explained by the tendency of organizations to be less than eager to break old habits and adapt to present and future circumstances. In other words, companies who are currently behind in digital innovation may not see the value in making use of big data and data visualization tools.
Additionally, the sprawling, multifaceted nature of big data is often intimidating to nontechnical professionals. Better storytelling requires, even more, data, deepening existing misgivings. Even when users aren’t overwhelmed by the construct of big data, a significant portion of them simply don’t believe the hype about data visualization. In many cases, the promises of visualization appear too good to be true, especially if they have been let down by software in the past.
Different Audience, Different Data
The demands of data visualization change depending on the audience. Making it work for everyone means demonstrating how the visualized data solves a specific problem, identifies a previously unknown consideration or otherwise impacts the target audience.
Management, which consists of senior staff, executives, board members, and other decision-makers, is looking for overviews that offer strategic insights and drive business decisions. Department heads, middle managers, and other staff need to understand how data affects their day-to-day working life and be spurred to action by those findings. Outside parties such as partners, shareholders, customers, media entities, government bodies, and regulators want data that’s specific to their inquiries and platforms, requiring a tailored approach to visualization.
Identifying opportunities to improve operations and revenue while staying one step ahead of the market are never-ending priorities for business leaders. Informed decisions in the current, ever-changing business environment demand quick access to relevant data and accurate interpretations of the information that’s gathered.
The future of big data is inextricable from data visualization. After all, data is only as useful as its presentation. Executives, leadership, and staff across the organization must be able to access and analyze data on the fly, allowing them to plan and act in real time. Visualization tools facilitate more accurate analysis, forecasting, and decision-making. This, in turn, impacts and improves operations, distinguishing a company’s ability to react quickly and effectively in the fast-paced market of today and into the future.
Choosing the right tools is especially important to match specific needs, and there is a lot of gray space for organizations who do not see value in such analytics. With data visualization, these gray areas can be filled in with color and value.