What is Data Visualization?
Data visualization refers to the graphical representation of data and information. By use of visual elements such as graphs, charts, and maps, data visualization tools provide us with an easy way of identifying and understanding outliers, trends, and patterns in data.
In the Big Data world, data visualization tools and technologies are good for analyzing massive amounts of information and facilitate the making of data-driven decisions.
Trends, patterns, and correlations may be difficult to detect and understand in text-based data. However, by use of data visualization software, these may be easier to detect and understand.
The data visualization tools of today go beyond the generation of standard graphs and charts like those provided in Microsoft Excel Spreadsheets. They can be used for the representation of data in more sophisticated ways such as dials and gauges, infographics, sparklines, geographic maps, heat maps, and detailed fever, bar and pie charts. Some of the images come with interactive capabilities, allowing users to manipulate them or drill into the data for the purposes of querying and analysis. There can also be indicators designed in such a way that they alert the user when an update is done on the data or when a set condition is met.
Read: What is Data Wrangling?
Why Visualize Data?
Data visualization has become a source of intelligence for modern businesses. The leading vendors in the area of business intelligence, Tableau and Qlik have put much emphasis on data visualization, and this has made other software vendors focus more on data visualization.
Our eyes tend to be drawn more to colors and patterns. It is easy for us to differentiate between a square and a circle, red and blue. We have been used to a visual culture such as art, TV ads, and movies.
Data visualization is a special type of visual art that can easily grab our attention and keep the eyes on the message. After seeing a chart, we can quickly identify trends and outliers. The following are some of the advantages associated with data visualization:
- Improved sight
With data visualization, you can get insights that you cannot get from the traditional descriptive statistics. If you have different datasets, it is easy for you to see their different trends by use of data visualization. This means that it is always good for us to visualize data instead of relying on descriptive statistics only. - Faster decision making
Every business is facing stiff competition in the marketplace. A business that is able to gather data quickly and act on it before the competition will be more competitive. Speed is of essence, and with data visualization, we can be able to analyze large quantities of data within a short period of time. - Discover emerging trends
Data visualization is a great tool to help you discover trends in both the business and the market. You can easily detect outliers that affect the quality of your product. After the identification, you can take the appropriate action before the problem becomes bigger. - Communicate the outcome to others
Once you have identified the trends underlying your data, it will be easy for you to communicate the insights to the others. In this step, you have to look for the best way to represent your data visually so that they may find it easy to understand.
Read: The Importance of Data Visualization
Data Visualization Techniques
Visualization marks the first step in getting to understand data. There are a variety of techniques that a data analyst can use to present data and show the correlations. Before visualizing data, you have to choose the right method to use. There is a visualization technique that is most suitable for the type of data that you have. This means that choosing the right visualization technique is key to ensuring that the data is understood.
Here are the common data visualization techniques that you can choose for your data:
- Charts
This provides the easiest way of showing the development of either one or more data sets. There are different types of charts that you can use to visualize your data. Examples include bar and line charts that can be used for showing the relationships between elements over time and pie charts that show the proportions of the elements of one whole. - Plots
With plots, you can distribute two or more data sets over a 2D or 3D space so as to show the relationships between the sets as well as their parameters on the plot. Plots are also of different types, with bubble and scatter plots being the traditional ones. For the cases of big data, data analysts normally use box plots since they can show relationships large volumes of various data. - Maps
Maps are widely used in different types of industries. They allow the positioning of elements on relevant areas and objects- building plans, geographical maps, website layouts, etc. Some of the common maps are heat maps, cartograms, and dot distribution maps. - Diagrams and Matrices
Diagrams are used for showing complex relationships between data and links and different types of data can be included in a single visualization. Diagrams can be tree-like, multidimensional or hierarchical.A matrix is a technique for data visualization technique that helps in reflecting the correlations between multiple data sets that are updated constantly like in streaming.
Basic Data Visualization Tools
Tableau
Tableau is one of the data visualization tools available in the market. Its users are provided with Drag and Drop functionality, help them to easily design charts, maps, matrix reports, tabular, dashboards and stories without any technical skills.
With Tableau, one can establish a connection to files, Big Data and relational sources to get data and process it. It also allows for real-time collaboration and data blending, which gives it some uniqueness. It is highly used for visual data analysis in academic institutions, businesses, and government organizations.
Before getting started with Tableau, you must first install it into your computer. You should download the Tableau desktop by vising the Products section page of www.tableau.com. The good thing with the tool is that it comes with a free trial version that can be downloaded and installed for free.
FusionCharts
This is a JavaScript library for data visualization. It is one of the best paid for market visualization libraries. You can use this library to create more than 90 different types of charts, and it can be integrated with a wide variety of platforms and frameworks providing you with much flexibility.
There is a great feature that has made FusionCharts the best tool for visualization. Instead of starting to create your chart from scratch, you are provided with a huge number of chart templates that you only need to load your data and have it customized to show the trends and patterns underlying your dataset.
Datawrapper
This tool is becoming a common visualization tool amongst media stations that use charts to present statistics. The tool provides a simple interface, making it easy for one to load their CSV (comma separated values) data and generate straightforward charts. One can also use the tool to create maps that are easy to integrate into reports.
Visme
This is another tool that can help you turn your boring data into more engaging content. It allows you to create engaging presentations, infographics and several other types of engaging content.
The tool provides you with already existing templates that you can use for your data. If you need to create your custom design from scratch, you can rely on content blocks. You will also be provided with millions of icons, images, and fonts.
Highcharts
This one requires a license for you to use it, just like FusionCharts. However, it comes with a trial-version and it can be used for free for personal tasks. You don’t need specialized training to be able to use this tool. The success of this can be attributed to its cross-browser support, meaning you can create and use its visual presentations from different web browsers, which is not the case with most new platforms.
Specialized Data Visualization Tools
These are the tools that provide a way of creating more sophisticated visualizations and at the same time, a way of doing analysis on the data. Let is discuss them:
Sisense
This is another visualization tool that comes with full-stack analytics capabilities. It is a cloud-based platform that provides drag and drops capabilities. It can also handle multiple data sources and it supports natural language queries.
Through its drag and drop feature, it is easy for one to create charts and other complex graphics and other interactive visualizations. It can be used for gathering multiple sources of data into one repository that is easily accessible and which can be queried instantaneously through dashboards. Such dashboards are sharable across organizations, ensuring that even the staff with minimum technical skills can get answers to their queries.
Qlikview
This is the greatest competitor of Tableau. It is used in over 100 countries and its users have liked it for its wide range of features and highly customizable setup. This also means that it will take much time for you to become familiar with the tool and use it to its potential.
Other than its great data visualization capabilities, Qlikview provides its users with business intelligence, enterprise reporting, and analytics capabilities and it provides a clutter-free user interface. It is commonly used together with its sister package called Qliksense which is good for data exploration and discovery. It also has strong community support and there are numerous third-party resources that can help its users know how to integrate it with their projects.
IBM Watson Analytics
This is a good tool for data visualization, made unique by its natural language processing capabilities. The platform provides capabilities for conversational data control together with strong data reporting and dashboard building tools. However, this tool is not cheap; hence, it should only be used for serious data analytics and visualization tasks.
Power BI
Power BI is a tool provided by Microsoft for free to provide its users and businesses with a way of analyzing and gaining insights from their data. It is a good tool for non-technical business users to aggregate, visualize, analyze and share their data. It provides an easy to use user interface similar to that offered by Microsoft Excel, and it can be integrated with other Microsoft Office tools, increasing its usefulness. This also makes it a versatile tool that users can start to use without the need for upfront training.
The tool was designed and developed to be used by small and mid-sized business owners. It also comes with a Power BI Plus which can only be used at a monthly subscription fee of less than $10.00 per license. The tool comes in different versions, including a web-based SaaS version (Software as a Service) called Power BI, a Windows 10 application that is downloadable and known as Power Bi Desktop and native mobile apps for Android, Windows and iOS devices.
Power BI comes with a number of tools that help it connect to a wide number of data sources including Microsoft products, Salesforce, and even several other vendors. The developers can easily change how the reporting tools and default visualizations look like, that is appearance, and even import new tools into the platform.
Grafana
This is a data analytics and visualization tool that supports more than 30 sources of data, including Elasticsearch, AWS, and Prometheus.
Note that when it comes to integrations, Grafana is better compared to Kibana, but every system works better with its data type. Grafana is known to be good for the various metrics it provides to its users. Due to this, Grafana has become the best tool amongst the various IoT data visualization tools.
With Grafana, you can visualize and compile into complex dynamic dashboards the various types of metrics data. It is also good for control and monitoring systems because of its ability to provide different admin roles.
Alerts and notifications can be generated based on a set of predefined rules. It has numerous perks that are good for faster data analytics, like making annotations, creation of custom filters and addition of metadata to various events on the dashboard.
Advanced Data Visualization Tools
Openheatmap
With this tool, you can transform your spreadsheet, most probably the one with geographical data, into a functional heat map with just a single click. Openheatmap expects you to have your data in a Google Spreadsheet, so your data in an Excel spreadsheet should be transferred into a Google Spreadsheet. This is a trivial process when you compare it to the results that you will get from the tool.
Leaflet
This tool is not good for complete beginners since it is a JavaScript library that you should incorporate into your data visualization framework. However, the good thing with this tool is that it is lightweight, only 33 KB. The tool does not simply create maps, but interactive maps that are good for use on mobile devices. Such a feature is not even available in some of the commercial data visualization tools, showing how powerful Leaflet is. If you like using the command-line tool or creating an Application Programming Interface (API), go for the Leaflet library.
Chartbuilder
This is a popular tool for creating charts developed and made available by Quartz, a financial news website, in 2013. Quartz had developed the tool to be used in-house by its journalists to make their news visual. However, the Chartbuilder tool is not much pretty on its own and it’s not easy for use by beginners. You are required to know how to download, install the tool and activate a Python script within it.
However, after the setup, you will only have to copy your data and paste it into the tool and then you will be able to generate graphics that you can modify on your style sheets. However, the tool has a downside in that it does not generate more interactive graphics like it is the case with other tools. The tool can only be used for generating static charts that are very polished within only a few steps.
Open Refine
Most people overlook data transformation, a process that is very useful when it comes to data visualization. This even becomes more useful when you are getting your data from different sources such as spreadsheets, logs showing long transactions from machine learning algorithms, etc.
Data transformation is the process of taking a set of disparate numbers and turning them into a set of relatable data. This means that there is the cleaning of data, transforming it and making it available to the external tools such as web pages. If you always find these tasks daunting, it will be good for you to use the Open Refine tool. It initially began to exist under Google’s flag but it now exists on its own. If you have a bunch of mismatched data at your disposal, then consider using this tool to refine the data.
Google Data Studio
The Google Data Studio is one of the Google Marketing platforms and it allows its users to come up with multiple views of their data and dashboards instead of simply creating one-time and publication-ready visualizations. However, it is a bit difficulty to use and one has to go through a learning curve before they can become an expert in using it. it can be easily integrated with the Google Analytics tool.
Plotly
This tool is more complex compared to Tableau and it comes with analytics perks. You can use this tool to create charts with R and Python, develop custom data analytics apps with Python. It can also be integrated with rich and open source libraries for R, Python, and JavaScript.
Kibana
This is a component of Elastic Stack that helps in turning data into visual insights. It was designed and developed to be used only on Elasticsearch data. However, Kibana is still the best tool for visualization of log data.
With this tool, you can create all types of data visualization techniques, including interactive maps, charts, histograms, and many others. This tool also goes past traditional dashboards for data analytics and visualization.
With Kibana, you can build advanced analytics, combine visualizations from various sources to explore the unique correlations between various insights then use machine learning features to unearth the hidden patterns and relationships between the data events.
Data Visualization in Python using Libarries
-
Matplotlib
This is a Python library used for creating 2D graphs and plots using Python scripts. It has a module called pyplot that makes plotting easy by providing features for controlling font properties, line styles, formatting axes, etc. It can be used for creating different types of plots including histograms, power spectra, bar charts, error charts, etc. It is used together with the NumPy library. This means that it is used on the NumPy arrays. The library was developed in 2002 by John Hunter. -
Pandas
This is an open source Python package that comes with many tools for data analysis. With Pandas, one is provided with numerous data structures that they can use for storage of their data as well as manipulation of the same data. The library provides numerous methods that can be invoked for data analysis tasks. Note that the Pandas library was built on Matplotlib. This explains why it comes with features for data visualization. Pandas allow you to create different types of visualizations from your data stored in a dataframe. The good thing with Pandas is that it allows you to load that is stored in various types of formats. -
Seaborn
This is a library used for data visualization and it is based on the Matplotlib library. With Seaborn, you can create pretty charts. The library is good at facilitating other data visualization needs such as mapping of color to a variable or the use of faceting. The Seaborn library works well with data stored in a Pandas data frame. The tool provides its users with a lot of flexibility when creating plots since the user can choose from a wide range of plotting styles. The library is good at mapping the features to your data in an efficient manner. -
Folium
In Python, geographic data is plotted using the folium library. Using this library, one can create the map of any place, provide the longitude and the latitude values of the area are known. Also, the folium library generates interactive maps, allowing one to zoom the map in and out after it has been rendered, a very useful feature. With the folium library, you are able to create various types of leaflet maps. Due to the interactive nature of the maps generated by the folium library, the library has become useful for use in dashboard buildings.
