It has been said that “a picture is worth a thousand words.” And today, in the era of big data, when businesses are inundated with information from varied data types and from on-premises and cloud-based sources, that old saying has never been more relevant.
Sifting through information to understand what matters and what doesn’t is becoming more difficult. Visuals make analysis much easier and faster, and offer the ability to see at a glance what matters. What’s more, most people respond far better to visuals than text—90 percent of the information sent to the brain is visual, and the brain processes visuals at 60,000 times the speed of text1. Those points make a strong case for the use of data visualization for analyzing and conveying information.
Data visualization is part of many business-intelligence tools and key to advanced analytics. It helps people make sense of all the information, or data, generated today. With data visualization, information is represented in graphical form, as a pie chart, graph, or another type of visual presentation.
Good data visualization is essential for analyzing data and making decisions based on that data. It allows people to quickly and easily see and understand patterns and relationships and spot emerging trends that might go unnoticed with just a table or spreadsheet of raw numbers. And in most cases, no specialized training is required to interpret what’s presented in the graphics, enabling universal understanding.
A well-designed graphic can not only provide information, but also heighten the impact of that information with a strong presentation, attracting attention and holding interest as no table or spreadsheet can.
Most data-visualization tools are capable of connecting with data sources such as relational databases. This data, which may be stored on premises or in the cloud, is retrieved for analysis. Users can then select the best way to present the data from numerous options. Some tools automatically provide display recommendations based on the type of data presented.
A graphic should always take into consideration the data type and purpose. Some information is better suited to one type of graphic over another: for example, a bar graph instead of a pie chart. But with most tools, the user has a wide choice of visual analytics options, from common charts such as line graphs and bar charts to timelines, maps, plots, histograms, and custom designs.
Data visualization is not a new concept. The paintings on the walls of Lascaux Cave could be considered a form of data visualization, telling hunting stories from many thousands of years ago.
High technology has introduced new visual options. But even modern data visualization involves telling a story.
For business intelligence, it can be a story that tracks a company’s performance across key indicators. How does the company compare to competitors? It can be about how an email or product marketing campaign is doing based on metrics. Is the campaign on track to reach its goal? Or it can be a story about what’s happening with data sources.
The story can cover yesterday, today, or tomorrow. The possibilities are unlimited.
Data visualization can help tell the story, conveying complex issues clearly. It can play a key role for identifying the significant information from the noise, including outliers and anomalies.
It can help you with your growing volume of data. Visual interaction with large data sets can simplify analysis, revealing new business insights.
Data visualization can help you do all that—if you have the right tool. So what should you look for? A number of factors should be considered.
So look for a smart data visualization tool that comes with enhanced analytics fueled by embedded machine learning.
A tool with that capability should have the power to help you with all the steps in analyzing and conveying information, starting with data preparation. Traditionally, preparing data for analysis has been a manual process, often time-consuming, frustrating, and prone to errors.
Consider a tool that can automate data preparation by collecting information from one or more sources and consolidating it. This accelerates the process and reduces the chance of errors. The tool should also be able to augment your analysis by recommending new data sets to include in the review for more accurate results.
You want an interactive data visualization tool that lets you quickly and easily ask questions and receive answers, to search for what you need and get to the data directly. Natural language interfaces that make it possible to interact with your data sources in human language can achieve that goal. The interfaces can also be used to modify requests and data set parameters.
And it should be a tool that gives you a choice, allowing you to decide on the best graphic for presentation or automatically making a recommendation based on data results.
In addition, without any advanced skills, including knowledge of coding, a user should be able to access predictive analytics and forecasting in one click to determine patterns and forecast future outcomes and trends.
Imagine proactive, personalized analytics offered with a mobile data visualization application. That capability is available in a tool with machine learning.
You can have a personalized assistant that understands what you need, when and where you need it. For example, it can determine what business report and graphics are required for your business meeting in New York. It can translate speech to text for mobile voice-based queries and alert you when new data is available to analyze while you’re travelling.
You won’t have to be chained to your desk to analyze information. Your analytics can be on your phone or tablet wherever you go.
With machine learning, uncovering what drives your business, understanding data behavior, and discovering hidden insights to make better decisions can be automatic.
You want a data visualization tool with features to keep things moving smoothly because the last thing you need is a solution that slows down your analysis and presentation—that creates barriers.
Look for ease of use. For example, point-and-click or drag-and-drop features, as well as the capability to see your data visualized automatically or to highlight one graphic and automatically see related information in other graphics, save you from doing those tasks manually. You want a tool that lets you quickly and easily add information or make edits, such as changing layouts to present new insights.
In the past, IT was often responsible for business analytics. Today, sales and marketing managers or other nontechnical users have taken over the job in many companies. Yet, if the tool is difficult to use, requiring an in-depth knowledge of SQL or extensive scripting for data preparation, IT could still be involved in the process, handling a flood of help requests.
Why waste time going back and forth with IT for answers? Choose a data visualization tool designed for self-service—one that has an interactive environment with guided, step-by-step navigation and built-in functionality so that customization is not required.
Consider a self-service tool that incorporates artificial intelligence (AI) and machine learning into the analytics to make certain tasks easier, particularly for users who are not analytically savvy.
The end result? From sales and marketing managers to business analysts, end users can handle the business analytics on their own, minimizing IT involvement.
Your data visualization tool should have prebuilt connections to load and integrate data from a wide variety of sources—making data sets easy to blend and helping you quickly decide what really matters. And it should be designed so that it can be accessed across your enterprise and shared with your coworkers anytime, anywhere.
Many companies have an analytics ecosystem featuring multiple tools: one for production reporting, another for management reporting, another for discovery, and so forth, which can be expensive, require a variety of skill sets, and create compatibility issues. A better solution? Choose a data visualization tool that connects with a platform designed to address all business analytics tasks.
With some projects, you may want to do everything yourself. With other projects, a little or a lot of automation may come in handy. So go with a data visualization tool that offers the flexibility to easily switch between human and machine.
Flexibility can also be a key factor when it comes to the technology environment. What type of solution do you need? Cloud? Desktop? On premises? Mobile? A combination? Today? What about tomorrow?
Some tools limit your choice, offering just a desktop version and only for data visualization. Others provide a range of solutions incorporated into a comprehensive business intelligence platform to make sure that you’re covered today and tomorrow as your environment and business needs change.
Imagine what a data visualization tool can do for your business intelligence and organization. There’s a tool out there for you.