Introduction
The essay discusses datafication, which is the process of collecting and analyzing data on a given phenomenon to assess existing patterns, trends, and correlations. The process makes data more manageable, understandable, and hence useful in drawing information for decision-making. The content of the essay includes the identification of data that was collected, how it was collected, why it was necessary, and the choice of the scheme of data.
Collected data
Data is collected from phenomena of interest in its entirety to assess any possible patterns or deviations that may arise from the process. In this case, data was collected from a website where Manga, the Japanese, Chinese, and Korean anime series that were published from 1976. All pages from all magazines were considered as individual sources of data, and this included the noted 1,074,790 unique pages as data sets. Being massive data, they needed a supercomputer to convert the pages into tangible data. All the pages were converted into ranges of entropy for the Y-axis, while the X-axis represented standard deviation on a range of greyscale.
Need for datafication
As noted, data is needed for decision-making and understanding a given phenomenon. Unless the massive anime pages published since 1976 were condensed and converted into comprehensible form, it would be impossible to draw any meaningful information. One of the interests in this process of datafication was to understand the styles used by the artists when designing these pages. This basis would inform the reference of readers and possibly the expectations of the readers. It was possible to understand certain cultural aspects informing the decision of artists to design the magazines as they did. Perhaps this might partially explain why he magazine was successful for that long period. The specific need for datafication is to take control of the data that organisations have “evidence-based management” (LBI Software, 2017), and help to “spot the patterns, trends and relationships in political, economic, social and environmental relationships” (Analytics Training, 2017). The central goal for datafication is to convert data into useful data that can be used to make decisions relating to the business.
Data collected needed to be able to guide decision-making on the chosen subject. Decision-making is based on understanding the data. Datafication is useful in the management of data because it is measured and can inform various decisions of interest to an organisation. Datafication makes it easier to visualise large data from trends and patterns formed. For example. Bersin (2017) observed that datafication is nowadays concentrated towards human resource management, where various aspects of employees are recorded and visualised. An example that was given is that of retention, where it was noted that mid-performing employees were likely to stay with their employers even when their compensation was cut to about 90%. Besides, salesmen who were more informed and experienced with the products they promoted performed better than new workers, implying the need for training and the choice of workers. About the datafication of Manga magazines by Manovich, the decision on the greyscale style to use for the magazines was made. The data can also be used to understand the culture of these people regarding artistic styles. A user can understand the preferences of Chinese, Koreans, and Japanese on artistic styles. Should there be a plan to produce a new magazine targeting the regions, such styles can be used to inform the decision on what customers are likely to prefer.
Process of datafication
The process starts with the identification of how available data can be converted into a form that can be referred to, stored, and made accessible. In the example of Manovich, access was needed from all sources, which were the pages from the Manga magazine. The next step involved the conversion of the same into a form that can be visualised and, if possible, manipulated. In this case, software and a supercomputer were used as tools that were necessary to extract the page into the form of a greyscale range and standard deviation. Using these standard criteria, each page was uniquely identified. All pages were then cumulatively formed to provide the desired information.
Analysis is the next step that enables all data collected to be condensed into meaningful information that can be relied upon in decision-making. It was possible to see the trends, patterns, and relationships that existed in all pages that were identified. The trend can then be used to identify several correlations that exist, and this is useful in the prediction of various decisions of interest. In such a case, the prediction could be where to place images when designing the magazines for those regions. The goal for this case was to enable the ease of visualisation of all the pages. Analysis of this data is used to inform various decisions about the stylistic design of such magazines. The implication is that any decision made would be backed by evidence from the data collected from the primary source. It is a decision based on all data collected rather than a single page or a few such.
Choice of scheme data
The scheme data that was chosen was grey-scale entropy and standard deviation. These were the simplest tools that were used to identify each page used in this analysis. The distribution of all pages based on this schema was needed to visualise existing visualisations. The schema was chosen because of the need that was needed, which in this case was artistic style choices and any variations existing among them. The main goal was to visualise over a million pages on a single page. Greyscale was needed to identify them individually and compare them to other pages. It is necessary that the correct scheme is chosen for ease of analysis and visualisation of desired information.
Conclusion
Datification is a new trend that is gaining momentum in many industries. Organisations need to control their data to make decisions. With huge data available, information is needed from the process to guide various aspects of an organisation. The process involves the choice of a scheme of data that can be used to achieve the intended objectives. Conversion of everyday processes into meaningful data is useful in providing information to direct an organisation.
References
Analytics Training. (2017, October 14). What is Datafication?
Bersin, J. (2017, October 15). The Datafication of Human Resources.
LBI Software. (2017, October 15). 1. What Does the “Datafication of HR” Mean to You? More importantly, What Should It Mean to HR Leaders Today?
