What Is Visual Analytics and How Can It Help Your Business?
The word "visual analytics" has been gaining popularity, both in data science and business communities, for its increasing usefulness in understanding and relaying information. While it's closely related to data visualization as a concept, visual analytics is slightly different in that visual representation of data isn't just the final product but an integral part of the entire process.
Put simply, visual analytics is the means of exploring and analyzing data sets entirely within a visual interface. Rather than using solely numbers and text to represent information, visual analytics uses symbols, colors and even animations to manipulate and display data-based information.
Visual analytics can be as simple as color coding text or cells in a spreadsheet, or as complex as rendering a 3D model of the world. Oftentimes visual analytics takes the form of an interactive data dashboard. It's truly whatever helps you and your audience more quickly and clearly understand and convey the desired information.
Why Visual Analytics Can Help Your Business
Visual analytics helps your business by showing trends and other relevant data-based insights as quickly and clearly as possible.
It's important to remember that despite our highly evolved forms of verbal and written communication, human brains evolved to process visual information roughly 60,000 times faster than text. Leveraging this fact by converting information into easily digestible visual form can help you process and communicate information faster than the competition—saving your business time and money.
If you don't think your business needs visual analytics consider the following statistics:
The Wharton School of Business found that data visualization could shorten meetings by 23%.
According to Bain & Co, companies with advanced analytics capabilities are 5x more likely to make faster decisions than their peers.
Poor and unused data costs US businesses roughly $3.1 trillion a year.
Put it all together and what becomes obvious is that better data visualization and better visual analytics are not only a business improvement, but a business necessity— uncovering hidden earning potential while streamlining insights and key benchmarks.
It's truly the miracle tool of modern times.
What Type of Questions Does Visual Analytics Seek To Answer?
Visual analytics is used to answer virtually any question where data intersects with actionable insight. Typically these will be uniform data sets with finite values, such as sales or marketing data—as opposed to something open ended like names, or survey responses.
A couple types of questions that visual analytics can answer:
Supply Chain Visibility
Visual analytics can help store or warehouse managers easily oversee inventory by location, or product type. Color codes can help show high and low inventory levels, while map overlays can help show exactly where those items are located, merging disparate data sources into one stream.
A visual distribution of store inventory by items in store (source: Qlik)
Visual analytics is great for boosting ROI on marketing campaigns, by illuminating data of each customer phase and their life cycle. Quickly see customer drop off points, geographic or demographic trends, or what campaigns are delivering the highest engagement, all without needing to search through large sets of numbers.
One of the main benefits of visual analytics is being able to help easily show data distribution. In the case of financial analysts, bar charts or scatter plots can show breakdowns of individual loans or loan applicants within a larger bundled portfolio.This is particularly useful in a portfolio with very diverse inputs as the distribution of risk and margin provide a much clearer picture than a mean or median would with only one value.
Key Methods to Quickly Increase The Effectiveness of Your Visual Analytics
One of the most effective techniques in visual analytics and data visualization is the use of "pre-attentive attributes". This refers to information we process immediately without needing to think to understand it.
The use of pre-attentive attributes is so common, you come across them everyday without realizing. A red octagon sign means stop. Green stock tickers mean prices are up. Multiple chili peppers on a menu denotes spicy.
Using pre-attentive attributes is extremely effective for helping to streamline information, and is incredibly useful for business. Here are the main ones:
Shapes. Often used in visual analytics, with different symbols representing different figures. Think circles representing cities on a map, and stars representing state capitals.
Size, Length and Width. Using the example above, large cities on maps tend to be larger circles than small circles. In the instance of a bar chart, length and width is always associated with a corresponding change in quantity.
Color. Referred to both in hue and intensity, color can quickly show what direction something is moving (often spreadsheets have negative numbers in red) and by how much, with the saturation of a color on a heatmap or chart often corresponding to a numeric threshold.
Position and Grouping. By clustering or grouping data points, you can make it easy to instantly identify data by type or source. A venn diagram would be the most basic of these.
The scatter plot above uses color, size and distribution to show the frequency and amount of tips relative to bill totals. (Source: Row64)
Visual Analytics by Time
Within visual analytics, there's both descriptive analytics — which attempts to find the cause behind something that's already occurred, and prescriptive analytics and predictive analytics, which aim to create action plans based on the insights from past data.
The main difference between prescriptive analytics and predictive analytics is that prescriptive analytics is based on creating a solution for the present state (for instance a reactive marketing campaign operating in real time) and predictive analytics attempts to create a strategy for an event that has yet to occur (for instance a product to market launch).
Interactive vs Static Visual Analytics
One of the most popular developments in visual analytics is the use of interactive data visualizations. Like their name suggests, these allow the user to drill down, manipulate or segment their data in real time, all within a visual interface.
Examples of this include clicking on a state or county to zoom in on a map, highlight a section of a pie chart to reveal its sub sections, or highlight specific cells by data type or source. Interactive visual analytics are very common on data dashboards.
Best Tools for Visual Analytics
While visual analytics can come from anywhere or be made by anyone, they tend to be associated with software that does much of the work for the user. Many of these programs provide much of the same output, with the main factors being their integration with the cloud, the level of detail in their graphics, and how they are used across a team.
While both visual analytics and data visualization are growing fields,
Tableau. Tableau is arguably the name in visual analytics, with several courses being taught dedicated to its specific software. Tableau can create most any type of data visualization — including scatter plots, Ganntt charts, tables, and maps — and can work both for data visualization, as well as an interactive data dashboard - including use as a CRM.
Qlik. One of the newer visual analytics platforms out there, Qlik offers less complex data visualization options than Tableau, but is well suited for a business professional that wants a streamlined platform to quickly view KPI progress or inventory levels. It also requires less in-depth training than Tableau.
Sas Viya. Sas Viya is great for users who aren't interested in creating custom data visualizations but want a suite of AI, analytics and data management, all presented to them in an easy to view dashboard. Sas Viya is cloud-native making it a great option for enterprise business looking to unify insights across teams.
SiSense Fusion. SiSense is one of the newer companies in the visual analytics space, and is making a name for itself as a no-code predictive analytics platform. Their cloud based data platform is designed for users with no data science background who want to easily look at the same data insights.
Row64. What sets Row64 apart from other platforms is the speed and scale of its software. By borrowing a page from the world of gaming and 3D modeling, Row64 harnesses the power of your computer's GPU to be able to render millions of points in real time. While not a true data dashboard, the ability to render visualizations from the largest of spreadsheets, Row64 is one of the only platforms equipped to handle the size of modern data—with raytracing capabilities far beyond what anyone else is offering.
Published: Jul 26, 2022 5:00pm UTC