Data visualization is defined as a graphic representation of information and data. In this process, the elements are represented visually with the help of charts, graphs, and maps. Data has always been an unprecedented source of information and knowledge. The production of data resources has significantly increased over time. American Multinational Corporation conducted research that highlighted the growth rate and pattern of data production over time. The study revealed that the digital format of data is growing at a significant rate of 200% every year. As per estimation, the data resources are estimated to account for 40,000 exabytes by the end of the year 2020. IBM had conducted another study that revealed that data is produced every day on an average of 2.5 quintillions. Such massive growth has created several challenges related to storage, scaling, processing, and analysis of this data. Existing solutions are ineffective in tackling this data amount. It has also become a huge problem to analyze this huge data. Therefore, new technology or approach is required to handle these data resources effectively. This is where data visualization services have now come into play and every organization is implementing it for analyzing the data effectively.
Importance of data visualization techniques in business
The trend of using data visualization techniques for business optimization is not completely new, but it has improvised over time. Earlier, the data resources were used to analyze the performance of the business operations, whereas now analysis of the data resources allows the organizations to drive their business operations. Organizations are now considering data analysis and associated techniques as an important component of their business strategies. To include data analytics into the executive end of the business, organizations need new data architectures. This requirement has generated several opportunities for IT and analytics professionals. Careers such as data scientists and big data developers have gained considerable popularity nowadays.
Recently, cloud computing has gained a lot of popularity in the market and every business organization wants to avail its service. Cloud computing allows the user to perform the required parallel computation operations using distributed cloud infrastructure. The users can create clusters of computational systems on the cloud. The in-built libraries and statistical tools of cloud can be used to perform the majority of the analysis operations. Additionally, external data analysis tools and solutions can be used in the form of parallel computing operations. Apache Mahout and GraphLab are some of the key examples of using cloud computing where data visualization techniques are highly used. The Apache Mahout aims to build scalable machine learning libraries using Hadoop and the MapReduce.
The main principle of data visualization should be “collect once, use many times”. Initially, the data was collected considering a few parameters related to a single purpose. Whenever a new question arose, new data was collected. With the help of big data and data visualization techniques, this trend has ended. It allows the data to be reused for more purposes than what it was collected for.