Data visualization may be defined as the graphical representation of information and data in a pictorial or graphical format in the form of charts, graphs or maps.
- The different Data visualization tools provide an accessible way to see and understand trends, patterns in data and outliers.
- Analyzing massive amounts of information and making data-driven decisions require Data visualization tools and technologies.
- The idea of using pictures is to understand data has been used since a long time. Some common types of data visualizations are Charts, Tables, Graphs, Maps, Dashboards.
Data visualization is actually another form of visual art that grabs our attention and keeps our eyes on the message. When we see a chart, we quickly see trends and outliners. If we can visualize something, we can internalize it quickly. It is basically a kind of storytelling with a purpose. If we stare at a massive spreadsheet of data, it is difficult to see a trend. Therefore we understand the importance of data visualization. The uses of Data Visualization as follows:
- Data Visualization has proven to be a powerful way to explore data with presentable results.
- The primary use is the pre-processing portion of the data mining process.
- It also supports the data cleaning process by finding incorrect and missing values.
- For variable derivation and selection means to determine which variable to include and discarded in the analysis.
- Data Visualization also plays a key role in combining categories as part of the data reduction process.
One of the most easiest ways to show the development of one or several data sets is a Chart. Charts may actually vary from bar and line charts that show the relationship between elements over time to pie charts which demonstrates the components or proportions between the elements of one whole.
Maps are popular ways to visualize data used in different industries. They allow to locate elements on relevant objects and areas, like, geographical maps, building plans, website layouts, etc. Among the most popular map visualizations are heat maps, dot distribution maps, cartograms.
Plots allows us to distribute two or more data sets over a two dimensional or even three dimensional space to show the actual relationship between these sets and the parameters on the plot. Plots also vary, like, Scatter and Bubble plots are some of the most commonly-used data visualizations. When it comes to big data, analysts often use more complex box plots that help visualize the relationship between large volumes of data.
Diagrams are commonly used to demonstrate complex data relationships and links and include various types of data on one visualization. The diagrams can be hierarchical, multidimensional, tree-like. Speaking of Matrices, a Matrix is one of the most advanced data visualization techniques that can help to determine the correlation between multiple constantly updating data sets.
Along with the demand for data visualization and analysis, the tools and solutions in this area develop fast and extensively. Novel 3D visualizations, immersive experiences and shared VR offices are getting common alongside traditional web and desktop interfaces. Here are certain categories of data visualization tools for different types of users with different purposes.
Tableau is considered as one of the leaders in this field. Newbies and professional analytics companies like Statista rely on Tableau tool to derive meaning from data and use insights for effective storytelling. Along with a user-friendly interface and a rich library of interactive visualizations, Tableau is known for its powerful capabilities. The platform provides large integration options including My SQL, Teradata, Hadoop and Amazon Web Services. This is an amazing tool for both occasional data visualizations and professional data analytics. The system can easily handle any type of data, including streaming performance data and machine learning insights, and also allow to combine visualizations into dashboards.
Tools for coders include more sophisticated platforms that not only allow to visualize data but also add data analytics features to the applications.
Similarly, Sisense is another data visualization tool having some full-stack analytics capabilities. This cloud-based platform has a drag-and-drop interface which can handle multiple data sources and also supports natural language queries. But, it can be a bit complicated for rookies though.
IBM Watson Analytics is widely known for its NLP capabilities. The platform literally supports conversational data control alongside strong dashboard building and data reporting tools. But, IBM Watson Analytics is expensive and works best for large-scale data visualization and analytics tasks.
The increasing demand and growing adoption of connected technology provides a lot of opportunities for the companies and organizations. To deal with large volumes of multi-source often unstructured data, businesses search for more complex visualization and analytics solutions. This category includes Microsoft Azure Power BI, ELK stack Kibana and Grafana.
Power BI is exceptional and widely-used because of its highly intuitive drag-and-drop interface, short learning curve and large integration capabilities, including Salesforce and MailChimp. Along with that, it is not much expensive. Owing to the Azure services, Power BI became one of the most powerful data visualization and analytics tools that can handle literally any amount and any type of data.
First of all, the platform enables us to create customized reports from different data sources and get insights at a couple of clicks. Along with that, Power BI is strong enough to process streaming real-time data and therefore easily handles big data analytics. Finally, it is not only fully compatible with Azure and other Microsoft services but also can directly connect to existing apps and drive analytics to custom systems.
- In today's marketplace, Data visualization is proven to be a valuable addition to any presentation and the quickest path to understanding the data.
- Besides, the process of visualizing data can be both enjoyable and challenging.
- However, with the many data visualization techniques available like, charts, maps, plots and others, it is easy to end up presenting the information using a wrong tool. Understanding the appropriate techniques and concepts is important.
- To choose the most appropriate visualization technique we need to understand the data, its type and composition, the information that we are trying to convey to our audience, and how viewers process visual information.
- Data Visualization tools like Tableau, Sisense, Plotly, Power BI and others make the the task easier by saving time and effort. For more such articles and learning concepts, click here.