Best Practices in Data Visualization (Essay Sample)

Best practices in Data Visualization
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Best practices in Data Visualization
Data visualization is a graphical representation of information that makes the reporting process more exciting and easier to understand (Maltese et al., 2015). This reporting technique provides an easier way to understand trends, patterns and outliers in data. However, for the data to provide in-depth insights through visualization, it should meet some data quality dimensions like accuracy, consistency, completeness and validity (Maltese et al., 2015). This is vital because low-quality data is likely to produce confusing visualizations, hence frustrating the end user. Data visualization makes it easy to depict trends and patterns in data (Chen, 2017). About 2.5 quintillion bytes of data are produced by humans every day, constituting big data that can be well reported through visualizations (Wang et al., 2021). Therefore, it should be noted that data visualization helps the end-user understand the significance of data as it communicates information clearly and efficiently.
The primary purpose of visualization is to summarize extensive data in well and easily understood visuals to avoid looking through thousands of rows in a spreadsheet (Maltese et al., 2015). It is difficult for data to communicate the meaning without reporting it through visualizations. Charts and graphs, therefore, are essential in communicating the information without much struggle. Visualizations are critical because the human brain can grasp more information from just looking at a chart other than reading through a whole document of figures (Chen, 2017). This reporting technique shows the connection within a more complex dataset to be explained by words. This makes the audience quickly understand the outcomes of the data without doing much reading. Through visualization, data analysts can present their argument and assist the management make decisions (Maltese et al., 2015). Visualizations are meant to emphasize the important aspects of data, record the information, and create a blueprint.
In data visualization, it is crucial to understand who the target audience is, what is the better chart that can convey information efficiently and how the information should be presented to emphasize the essential aspect of the data (Maltese et al., 2015). It is important for the visualization to create a story that anyone can easily follow. The audience should interpret the meaning, pattern and trend from the visualization without much struggle. The audience's preferences should guide the decision of visualization in terms of formatting, graph type and the dissemination mode. The era of presenting more than 100-page narrative report is long gone, and for that matter, viewers only want the executive summary and brief visual reports. If the audience will not read more than two pages, the report needs to focus on highlighting the main aspect of the report (Maltese et al., 2015). If visualization is not designed to communicate to a specific audience, its presentation will only be futile.
Deciding what type of visualization that is the best to communicate a particular message is an essential practice in data visualization. There are many types of graphs, tables, and charts available to visualize data, but the choice of what visual to represent on which data can affect the audience's effort to comprehend the data (Maltese et al., 2015). The usage of appropriate visuals, for that matter, makes the data represented not only accurate but also easy for any audience to understand. When presenting correlation and distribution, data analysts can consider using scatter plots (Maltese et al., 2015). If the data has less than seven categories, it is vital to consider using pie charts or doughnut charts while patterns and change over time in data can be presented using line charts (Chen, 2017).
How the visuals are presented is crucial in helping the end-user grasp more information from one visual display without further computations. When the user looks at the analytics dashboard, they should get the essential information needed (Maltese et al., 2015). The analytics dashboards should be clear and easy to understand than presenting complex dashboards with unnecessary information. The number of widgets on the dashboard should be at most 6 for consistency, with the first 3 to 4 widgets presenting the most critical information that can be understood just with a glance (Maltese et al., 2015). This should be done in an uncluttered design for the user to focus on the most vital information rather than just a messy and confusing dashboard.
The process of reporting undergoes various stages before it officially relays information to the intended end user. Some of the steps in a reporting life cycle include the scope, gathering data, processing the data, report creation, validation, report distribution and maintenance (Wang et al., 2018). The scope of reporting is very vital in offering a problem statement and what problem the data is intended to analyze. The scope also explains the report's audience, thus giving the data analysts a clear picture of how to visualize the data to target that user. The scope also helps to explain the structure of the data to be collected to collect and manage the data (Wang et al., 2018). It is imperative to explain, in the scope, how and where the data will be accessed for better planning and budgeting. The scope will also determine the software to be used, ensuring the report runs quickly and ensuring the data is up to date.
Adequately gathering and managing data is a crucial aspect of reporting. Data collection serves as the start point of the data reporting life cycle (Wang et al., 2018). Data collection allows room for creating values that do not exist but are essential for decision making. One way of gathering this data can be through purchasing the data generated externally.
Another means can be creating data internally, either manually or using technology. The data collection component helps to check for available reference materials for better data standards. During the data collection stage, date stamps are necessary for validating the data in the future (Wang et al., 2018). At the point of data collection, the data analysts will determine how to report the data depending on whether the data is recurring or is one-time data.
After gathering the data, it should be kept clean ready to be processed to develop insights for businesses. The data processing stage is critical because it incorporates inductive reasoning to create valuable data (Wang et al., 2018). At this stage, the data analysts can investigate some cases that do not qualify to be included in reporting. This cycle helps in making a summary and identifying the patterns that exist in the data. This component helps to check for missing data against the business processes. If the data is missing very critical information, then the accuracy of the reporting will be compromised, and this is supposed to be documented on the report output (Wang et al., 2018). At this stage, productivity is increased, decisions made, and more accurate and reliable information developed from the data.
Report creation is important because it displays the data focusing on business intelligence. At this stage, the data can be formatted in terms of background, titles, colour, among other things (Wang et al., 2018). During report creation, font's choice is crucial because it can affect the legibility of text, hence detracting it from the intended meaning. It is crucial, therefore, to stick to more basic fonts across all kinds of views. At this level, the audience should be considered depending on their experience, skills, or level of education to comprehend the reports and make data-based decisions (Wang et al., 2018). If the data analysts understand the end-user of the report, then at this point, they should only include actions that can communicate clearly to the end-user without much struggle.
During report creation, various definitions and terminologies are used. It is a good practice for the report writers to include a definitions page or an appendix that can further explain these terminologies (Wang et al., 2018). All the assumptions made on the report can be included in the appendix, and the inclusion-exclusion criteria applied. This information at this stage is vital in reducing misconceptions and confusions among end-users who may not have been part of the initial report creation. When creating filters in the report, it is vital to label the filters appropriately. This should aid the end-user to understand how to interact with the report (Wang et al., 2018). Consistency in mapping the information at this stage essential to avoid each user developing their elucidations of the report. When the information is ambiguous, the users are likely to develop their assumptions based on their experiences which is likely to distort the information.
Another best practice when creating a report is to consider converting it to a PDF document before disseminating it to avoid its alteration. If the report is regarding health information, there should be security in terms of access (Wang et al., 2018). For highly sensitive information, the report can be locked so that only approved end users can access and view the data. This component of data creation should take into considerations visualizations. Visualization can take the report to the next level if properly done because the human brain is able to grasp a lot from a chart than it would grasp through perusing through texts and spreadsheet (Wang et al., 2018). Data visualization is essential because i...