The increasing access to Big Data in this world of connectivity has opened up the possibility of gaining deep insights that can help businesses take relevant, strategic decisions that can spur growth. No wonder then that the Big Data market is expected to grow from USD 28.65 Billion in 2016 to USD 66.79 Billion by 2021, at a high Compound Annual Growth Rate (CAGR) of 18.45%, according to MarketsandMarkets Research.
The growth in Big Data in itself is not as significant as the ability to cull insights and forecast future trends. As a result, Zion Market Research expects the global predictive analytics market to grow at a CAGR of 21 per cent from USD 3.49 billion in 2016 to approximately USD 10.95 billion by 2022.
Given the complexity of data, data visualization tools have become critical to presenting the data in ways that can help understand the dynamics between data elements better. Charts, videos, infographics and even virtual reality and augmented reality presentations are being used for more engaging and intuitive insights.
The visualization tools convert numeric algorithmic outputs into images that help in an intuitive understanding of the depth and range of the data in an easy-to-interpret format. Therefore, the visualization is not merely an aesthetic representation but meaningful one too.
R Shiny “ A Tool that Shines
Traditionally, data analysts and data scientists would write algorithms/build models in collaboration with web or with BI tool experts. However, it would cause delay and losses, in addition to limiting the scope of visualization. On the other hand, along with providing rich and useful data exploration experience, drag/drop and point/click interfaces make its use relevant and elegant.
There are several data visualization tools available in the market, some of which provide as many as 24 different ways of representing data, including pie, line and bar charts. The second advantage is being able to build dashboards that enable many users to access different stories from the same dataset based on their requirements.
Among them, the OpenSource solution R Shiny stands apart despite it being a code-based tool developed for R. creating the actual dashboard does not impact speed. It provides an infinite number of applications that can be built with no restriction to the type of visualization suited to the specific requirement.
Some of the advantages of R Shiny include:
Easy to Share
It is easy to share the dashboard even with users who do not have R Shiny as it generates a URL.
Malleability
The versatile dashboard can display the data as per the user requirement since it works on the principle of building blocks. Its integration with JavaScript library gives developers access to a large variety of visualisation options that are customisable to different needs.
Industry-/Function-Agnostic
Most visualisation tools are best suited for market and sales projections. The granularity enabled by R Shiny lends itself suitable to being used in any industry or for any function. For instance, a call taxi service provider in the Far East has used it to understand the utilisation of its services by the hour, in each neighbourhood. This enabled them to know where the demand peaks at what time and how to cope with it, improving its fleet utilisation.
In a semiconductor manufacturing company, it enabled performing a health check of the manufacturing facility using the data generated by the sensors.
Interactive
Most visualisation tools generate static graphs, allowing only zooming in and out features. But in R Shiny, it can be used to simulate scenarios and project outcomes.
Automation
Built as a statistical analysis and data wrangling tool, data manipulation steps can be automated and the data steps can be reused later.
Coding Skills
What may seem like the flipside is the fact that R Shiny needs coding skills and knowledge of databases and cloud. The cost of development of the code also may offset the cost-benefit of it being an OpenSource tool. However, there is no limit to the number of databases and APIs that can be connected as well as the community support. The granularity, range, depth and variety can be used to truly benefit from the Big Data for taking decisions beyond just sales projections.
