Concepts of data visualization:
Data visualization brings data from computers to life. It provides a graphical representation by bringing shapes and colors to display quantified information, hence aiding humans in perceiving and understanding data to make informed business decisions. With data visualization, people can delve into a wealth of information, discover patterns and keep track of their business KPI’s; they can spot opportunities, detect abnormalities and generate insights.
However, only focusing on the decoration of data without capturing the essence of its details and what it is conveying could become misleading. Edward Tufte, who is a pioneer in the field of data visualization, gave guidelines for good data display. His guidelines are summarized by: induce the viewer to think of the data; avoid distorting of what the data has to say; make large data sets cohesive; reveal data at several levels of details; be closely integrated with the statistical and verbal description of the data (Tufte, 2001).
Data visualization tools:
Data visualization tools make it easy for people to build data layouts interactively and share their work with others. They are designed to replace the use of dispersed spreadsheets by combining several data sources and producing a cohesive reporting. According to research conducted by Aberdeen Group, users are more satisfied with interactive visualization tools more than traditional BI systems due to their accessibility of data, ease-of-use of analytics, and job role relevance to analytics (Lock, 2016). These tools like Tableau or Power BI usually have a desktop and a Web version. Some of these tools have advanced analytics capabilities (for example integrating Tableau and Power BI with R to build predictive models like regression analysis). Creating and publishing dashboards is another important feature which allows users to share data layouts with others. There are also other IT features related to data governance, implementing user-level security, scheduling refresh rate and embedding dashboards into business applications.
Users build reports following three main procedures: data accessibility, data preparation, and data display. Data accessibility is about getting data from a single or multiple sources. This is done either manually by extracting data, or by connecting to a data source whether it is in a database or real-time data (feeds). Data preparation is about manipulating data and making it ready for visualization. Users can define relationships between data sets, combine variables, create time series, etc. Data display is about building reports. Interactive visualization tools offer dynamic drag-and-drop features with assist display of the recommended chart. Users can change the type of visualization and tune charts’ aesthetics. They can select filters, add more layers, add trend lines and cross-tabulate variables. They can perform descriptive analysis, correlation analysis, and predictive analysis.
Globally connected organizations:
Data visualization tools are pivotal in helping globally connected organizations to access and manage knowledge to share across functions and locations/markets. These tools allow IT professions to decentralize data governance by making decisions on who has access to what, at the same time they empower users across organizations to become more self-reliant and try new things while learning from other experiences.
“Bringing local and global data together can help teams see and understand business performance faster than relying on static reports” (Arellano, 2016). Tools like Tableau and Power BI help organizations to become more agile and buoyant to change, by discovering information faster, and identifying trends and learn about emerging markets. Teams in different locations can monitor the activities of their organization and test new business approaches that they haven’t tried before in local markets. They can also stay tuned and become alert to any potential threats, for example keeping an eye on a competition that hasn’t entered their local market.
By Rabih Soueidi
Arnello, P. (2017) ‘Making Decisions with Data – Taking an Agile, Value-Based Approach to Designing Analytics’, Brist, 7 January. Available at: https://www.birst.com/blog/making-decisions-with-data-taking-an-agile-value-based-approach-to-designing-analytics/. (Accessed: 12 December 2017).
Lock, M. (2016) Data Visualization Tools: Key Selection Criteria. Available at: https://3u8npt2xyr3u2fgvai49zopv-wpengine.netdna-ssl.com/wp-content/uploads/2017/08/standard-pdf-12287-KB-visual-edge.pdf. (Accessed: 12 December, 2017).
Tufte, E. R. (2001) The Visual Display of Quantitative Information. Graphics Press, Cheshire, CT.