The adage, a picture is worth a thousand words, aptly captures the essence of big data visualization. Human beings naturally find it easier to find patterns in pictures than in rows of data. Data visualization becomes dispensable when it comes to data that is so large that it has to be managed using several data servers.
What Is Big Data Visualization?
Big data Visualization is , as the name suggests, a visual representation of big data. Visualization techniques vary depending on the goal of the illustration. It could be as simple as line charts, histograms and pie charts or a bit complex like scatter plot, heat maps, tree maps, etc. Visualization of big data can also be done in 3-Dimensional graphs, based on the use case.
When they say, “Data is the new oil“, the data set can be imagined as crude oil and big data visualization as refined oil. In the world of big data, it is often the case that the breadth, let alone the length, of a data set, cannot even fit on a single screen. We need big data visualization software to make any sense out of such mind-boggling amount of data.
It allows you to make observations and gather insights. Representing data sets in the form of charts and graphs makes a huge difference in the usability of the data set. Even people who are good with SQL queries prefer a visual format to make observations instead of a tabular format.
Why Is Data Visualization Important in Big Data?
Generally, when big data analytics and algorithms are applied to data sets, the results are meant for the decision makers. Other colleagues or top executives would appreciate it if one can reduce time or effort that goes into decision making. It makes their life a lot easier when they are provided with insights in easily consumable format.
The best part of big data visualization tools is that they are capable of capturing data sets in the visual format without loss of accuracy. One can control the factors like accuracy, precision, level of aggregation that is required to serve the purpose.
Another major benefit of visualization is the ability to show all the information in a single place. It enables you to create dashboards and reports, which are packed with insights, that can be shared across the organization.
We need visualization of data in every industry - airline, IoT, energy, media and entertainment, automotive, sports, manufacturing, the list is endless.
Given the huge scale of data, big data visualization comes with its own set of challenges. Online Big Data Courses provide great resources for building strong big data fundamentals. It will help you make informed choices while choosing appropriate big data visualization techniques.
What Are the Types of Big Data Visualization?
1. Line charts
Line chart, also called a line graph or line plot is a common chart. It is used to represent changes in one variable against another, typically the time. The data points are connected by lines. It is used for identifying trends and relationships between two variables. For example, the below chart depicts the sales numbers of three employees.
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2. Histograms
A histogram is used to represent the frequency distribution of data. It groups data into logical ranges and depicts the count of how many data points fall into each of those ranges. It allows one to understand the nature of frequency distributions. The distribution may be categorized as symmetric, right-skewed and left skewed. For example, how many people are between each range of ages is shown in the following histogram.
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3. Bar chart
Bar chat, also called a bar graph, is used for depicting categorical data with rectangular strips/bars. The length of the bars shows the value or quantity of a variable. The bars might be vertical or horizontal. For example, the below shows how many people like which kind of movies.
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4. Pie charts
Pie chart depicts the information in the form of “pie slices”. The “slices” are in proportion to the relative sizes of data. Above example can be represented in the pie chart in the following form.
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5. Heat Maps
A heat map uses two-dimensional representation of data in which colors represent the values or ranges. It provides a quick visual summary of information. Below is an example of heatmap of temperature-variation data across an year in 4 US cities.
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6. Scatter plot
It uses dots/points to show values for numeric variables. The position of the dots against both the axes indicates the value of that particular data point. Below is an example of tree height plotted against the girth of the stem.
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7. Tree map
This type of chart represents hierarchical data in the format of nested rectangles. The size and color of a rectangle represents the value of that category or variable. It helps to depict part-to-whole relationships in a complex data set.
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8. Word cloud
Word cloud or tag cloud is a representation of word frequency in a data set. The larger the word appears, the higher the frequency of that word. This is used for textual data analysis and summarization. Below is a sample word cloud of jargons commonly used in the big data industry.
One can also define big data visualization categories in the following manner.
It is a representation of data against time period. For example, gantt charts, timelines, etc.
It represents data in tree format. One root node at the top and branches originating from the root. For example, tree map, flow charts.
It is used when one wants to show connections between various unrelated data sets. Word cloud and matrix charts are examples of network type of visualization.
Geospatial is a special category in which location data is one of the variables. The variables are plotted against the location variable. Demographic charts, density maps are examples of this category.
Big Data Visualization Tips for Beginners
- Start with defining the audience and the requirement of the report. Big Data visualization can capture several insights, but what makes the most impact on the audience needs to be identified at the beginning.
- Data can be represented in multiple formats. Deciding the right kind of visualization helps the decision maker to arrive at a decision faster. The visualization should capture all the key insights yet be simple to absorb.
- Providing labels in the report gives meaning to the visual. Some points to keep in mind are - check the alignment of labels, check all required labels are present, add titles that capture the essence of the chart and add legend when needed.
- Make use of multiple colors in the chart, if it makes it better for the viewer to grasp. Colors can be used to encode information without explicitly adding any more elements to the graph. The color scheme should be such that the viewer need not struggle to find the patterns depicted in the graph.
Opt for courses like KnowledgeHut Online Big Data Courses which will provide a sound foundation of big data and help you understand how each step in the big data analytics journey functions. Starting from data preparation, applying analytics and lastly, creating dashboards with insights. This way, as a professional, you can make the best use of the available data.
There are many tools available in the market for data visualization in the big data world. Along with these tools, there are languages like R and python that data professionals use for complex analytical functions and plotting. These functions can also be embedded into big data visualization tools for applying to large data sets. Many tools can be categorized as big data analytics visualization tools rather than just visualization tools.
We have mentioned a few noteworthy visualization tools for big data.
1. Tableau
Tableau is a well-known visualization tool. Tableau Desktop can collect data from multiple data sources, which can be either on-premises or in the cloud. Users can get started easily without much initial effort. It is built for data professionals as well as professionals from other fields. It supports drag-and-drop functionality, interactive dashboards/charts and the ability to connect to multiple data sources simultaneously. Natural language queries are a major plus point for people with limited DBMS knowledge. An interactive dashboard helps immensely with story-telling, say, in front of stakeholders.
It provides support from data preparation, doing analysis and creating reports for sharing amongst colleagues for better decision making. Tableau provides Saas model as well as on-prem model. It has a mobile app too for analysis and viewing.
2. Microsoft Power BI
Power BI is a powerful tool for data visualization of big data. It is strongly backed by other products Microsoft Power Platform. Along with Microsoft’s evolving ecosystem, AI capabilities and numerous data source connectors makes it a prominent visualization tool in the industry. One can even use it to create a real-time chart/dashboard by streaming data.
3. Looker
Looker, a Google product, is reinventing big data visualization and analytics. It has its own proprietary data modeling language called LookML. It provides an abstraction on top of a SQL database. It makes it maintainable and increases reusability. But it comes with initial investment of time and effort. Due to its steep learning curve, it is not so popular in the industry. One can see Looker as a SQL Query Builder engine with highly flexible access control as well as permission management. It falls behind in data visualization features when compared to other prominent visualization tools.
4. Sisense
Sisense is another hugely successful big data visualization tool. It allows customization as per client’s needs. It uses parallel processing of data which reduces the resources required by a large factor. It has a drag and drop interface to setup analytics workflow as well as for creating dashboards. The reports can update in real time as new data is ingested in the system. It has a useful mobile app which can be used for accessing data and dashboards. Sisense provides embedded analytics functionality that allows reports to be embedded in other applications like Zendesk or Salesforce.
There are few open source big data visualization tools though not as capable as enterprise ones.
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Conclusion
Big data visualization is a powerful way to create the most impact for an organization. Putting accurate and meaningful reports in front of the decision makers will help them make the best decision for the future. It is critical to pick the best big data visualization tool that suits the organizations need. Big data analytics and visualization must go hand in hand to create maximum impact.
