Analytics plays a significant role today in driving business success. It uses explanatory and predictive modeling based on extensive use of statistical analysis to drive decision making. Business analytics helps organizations in answering critical questions such as, Who are my most valuable customers? What are my most important products? What are my most successful campaigns? Why is this happening? What if these trends continue? What will happen next (that is, predict)? What is the best that can happen (that is, optimize)?
One of the most valuable means through which to make sense of big data, and thus make it more approachable to most people, is through data visualization. Data visualization is way finding, both literally, like the street signs that direct you to a highway, and figuratively, where colors, size, or position of abstract elements convey information. In either sense, the visual, when correctly aligned, can offer a shorter route to help guide decision making and become a tool to convey information critical in all data analysis.
Data, especially large amounts of data can be overwhelming and difficult to wrap our head around. Through data visualization, key values can be unlocked in the form of visualization from massive sets of data. In that way, users would be able to identify patterns and other emergent properties in the data and can formulate new insights. They aims to tell a good story by translating the data into a form that would be easy to understand. The noise would thus be eliminated from the data and eventually, the useful information would be highlighted. However, it is not as simple as taking the data and placing them in a graph and making it look better. It’s an act of balance between the form and a function. A plain graph can be to boring to catch the attention or make a point; the most impressive visualization could take away from the data or it could speak volumes. It is important to realize that visuals and data have to work together to convey a message.
One of the major challenges of data visualization is that many organizations do not have the in-house knowledge or capacity to extract the data they have and visualize it in a clear way. The result of this is that it becomes hard to see the correlation and relationships that are not already known whereas that is the primary benefit of effective data visualization.
Our experts will make sure that the way the data is visualized, Clearly reflects the purpose of underlying insights. We believe this to be a prime element of a good dashboarding. For example, a graph that can be perfectly suitable for discovering correlations often proves insufficient to signal trends and vice versa. Our expertise in Data Visualization results in an ideal tool that gives you proper insight into your organization’s most relevant management information. These insights also lend themselves perfectly for commercial purposes, such as sales pitches due to their visual attractiveness.
1 . A Market Survey Company had conducted a survey regarding the brand awareness of 5 major airlines in order to determine the market value of these 5 EAP airlines in comparison with the others. The MR Company wanted to develop dashboards from the survey data collected across the globe for its 5 major airlines in order to introspect the Brand and advertisement awareness along with Brand Image across various attributes country wise. They also wanted to understand the flying patterns of their customers and measure the key factor influencing the preference of the customers. Dash boarding is a unique way of approaching customer satisfaction problems via interactive visualizations and filter actions that enable the viewer to cross tab different attributes across any parameters. It improves the efficiency to derive inferences from a non-systematic data in a comparatively less time. This implementation resulted in convenience of displaying different relations of KPI’s that influenced the customer preference.
2 . A Market Research Survey was conducted in accordance with the study objective to capture data from Singapore and Malaysian population. This data was pulled from Confirmit and then exported to Tableau to derive key outputs graphically. The captured data had encapsulated more than 2500 variables within 62 questions that were studied individually and jointly, to extract knowledge on how much customers were willing to spend on insurance premium annually, predicting insurance brands they can consider in future and their awareness about various products and brands in the genus of Insurance. The dataset comprised of both Open Ended and Close ended survey responses. The Study favored a very modicum time frame, as the data was refreshed weekly.