With the advent of more and more information technology, the data economy is growing exponentially. Visualization provides an accessible way to see and understand data graphically rather than in text format.
Images can often convey information faster and more effectively than words. Big data visualization techniques transform data into a graphical format. Based on the information, companies can improve products and services, increase productivity and security, and boost conversions of any business metrics in principle.
The types of Big Data visualization
The visual representation of the results of big data analysis is of great importance for its interpretation. Human perception is not limitless, and scientists are still researching ways to improve the presentation of data in the form of images, diagrams, or animations. Depending on the purpose and the data, you can choose the graphics that best suit them. It is better to avoid variety for variety’s sake and choose on the principle that the simpler, the better. Here are ways to visualize data:
One of the easiest ways to present data is to show it as a diagram. It varies from bar and line charts to pie charts, showing the relationship between elements, comparing and contrasting them. Among the most common are:
- Bar Chart – values are presented as a vertical or horizontal bar. Such graphs scan for quick viewing, analysis, and comparison of information.
- Line Chart – like bar charts, they help to show data in a compact and accurate format. They are made up of lines to make it easier to analyze peak and downturn moments, such as time or money.
- Pie Chart – such diagrams allow you to compare parts of a whole. For example, components from the same category.
There are also diagrams like this, although they’re less common:
- Waterfall Chart
- Funnel Chart
- Area Chart
Cartograms, heat, or dot distribution maps allow visualizing plans, geographic or construction projects in different industries. For example, by geographically organizing data to keep track of business, monthly sales, and see the geographic location of customers.
- Heat Map shows the relationship between the two indicators and provides rating information. Rating data is shown using different colors or shades. Ratings can be from high to low or from poor to excellent.
- Cartogram is a schematic geographical map on which individual areas are indicated by graphic symbols, depending on the magnitude of the depicted indicator.
Plots allow visualizing datasets in 2D or 3D space to show the relationship between datasets and parameters in a graph.
- The histogram shows the distribution of the data over some time. The continuous variable on the X-axis is divided into discrete intervals, and the amount of data in that interval determines the height of the bar.
- Scatter plot – data visualization that shows the mutual change of two data items. Can be used if there are many data points and you want to highlight similarities in the data.
- Bubble plot – scatter plot, only the markers are bubbles.
Each element in the cloud has a certain weighting factor. It correlates with the font size. In the case of text analysis, the value of the weighting coefficient directly depends on the frequency of use of a certain word combination. This allows the reader to get an idea of the key points of any large text or set of texts in a short time.
Diagrams allow demonstrating complex relationships between data and include different types of data in a single visualization. They can be in different forms like:
- Hierarchy – looks like a flowchart. It shows the structure of the organization and the relationships within it.
- Tree – this kind of chart can show a genealogical tree and illustrate the structure of a family.
There also can be anatomical diagrams, circuit diagrams, and much more.
The value of good data visualization
Our culture is visual, from art to television and movies. This is the type of information that people process best. Data visualization is a kind of visual art form.
In addition to processing by the brain, visualization has such advantages as focusing on different aspects of data, reducing information overload and holding attention, clarity of data, highlighting connections in information. And, of course, aesthetic appeal.
Big data visualization removes unnecessary noise, leaving only important information. That allows to quickly and easily notice and interpret relationships. As well as identifying evolving trends that would not attract attention in raw data. In this way, the search for answers to specific questions can reveal unexpected correlations, and companies can thus gain a competitive advantage and make important business decisions.
Generally speaking, graphical representations require no special training to interpret, reducing the likelihood of misunderstandings.
Curious examples of data visualization
Teradata’s project, The Art Of Analytics, looks fascinating, presenting data most unusually. The idea of the project is to explain research based on big data in the form of artistic images to a wide audience. The experts were able to explain complex things in simple language, expressing the mathematical relationships in the form of art. Instead of graphs and numbers, bid data is represented by an abstract picture. There are a total of 20 studies in picture form. We will show some of them.
Information about who, when, and how much you talk to on the phone, what SMS and MMS you send goes into the database of the cellular operator, or any organization that has access to them. Such information is a classic example of big data. Its volume is enormous.
So dots: phone numbers.
Ribs: calls, where long ones are long calls and short ribs are short calls.
A line connects two points. Whoever has access to this information knows if you have a phone, how often you use it, who you call, and who calls you. This is how cellular carriers can tailor rates and programs to the needs of subscribers and track users’ habits and preferences.
It shows the movement of money between different companies with different colors. The dots are the companies themselves.
The lines are the number of transactions. The more lines, the more successful the company.
A big company will look at a large number of connections and cooperate with them, saving itself from risk. Smaller connections are businesses that can attract the attention of various support funds without affecting the entire market or putting the economy as a whole at risk. So the tax department can use analytics to check a company for illegal financial transactions. More than 60 million records of 670 thousand companies were used to visualize the data. There are 3,883 points and 3,943 lines in the count.
Stars and Stripes
One of the most interesting illustrations. It shows the relationship between drugs in clinical trials and their negative side effects.
The large dots that form the star points are the medication prescribed to patients. The nodes in the center are unwanted side effects.
All data processing consisted of several steps, including the use of the Aster MapReduce function, intelligent text analysis to extract side effect names, and a graphing function.
Single Salt Sampler
This is the type of analytics used in food science. And for whiskey fans, it also offers an interesting way to explore. If you like the flavor profile of one brand, you can try others that are similar to it. Or discover other types of flavor combinations.
The dots are single malt whiskey brands.
Ribs are the degree of similarity between whiskies. The thicker and darker the line, the more similar the flavor characteristics.
The visualization consists of 86 single malt brands grouped into 12 flavor characteristics.