I find this post very thoughtful and consistent and agree with all the arguments. Indeed, data collection is an integral part of the work of almost all business organizations and sometimes even individuals. This fact is primarily associated with the availability of technologies for data collection, as well as the benefits that this approach brings. However, it is worth noting that the idea of ​​a variety of methods for collecting information sounds throughout the post, which, however, is not directly discussed.
As noted in the post, collecting data is required for informed decision-making. In addition, technology has made it possible to make evidence-based research and practice available to all people, which has significantly improved the quality of health care, education, and other areas. However, one of the thoughts in the post is that data collection methods are still limited. One can find at least five references to data collection methods in the text, such as active and passive approach, dividing employees into groups, getting feedback, using social networks, and artificial intelligence software. All these processes are available for different categories of people and organizations and have different purposes. For example, I absolutely agree with the idea of ​​social networks and media accessibility that most organizations use for marketing purposes. However, as noted in the post, all of these methods are still not perfect and require refinement and selection.
The problem that is described in the post is the need for mechanisms and programs for collecting classified information and storing it. Widespread technologies are not applicable in this case, since the data must be accurate and comply with government requirements. In addition, as noted by Zhou et al. (2018), adaptive and efficient mechanisms for collecting data in heterogeneous networks are not yet sufficiently developed, which is also probably a problem for choosing a method. Therefore, for these purposes, the available techniques of observing, census, and artificial intelligence are used. Compared to the collection through feedback method described earlier in the post, this process is noticeably more complicated. Hence, one of the main messages of the post is that while data collection is essential for modern organizations, the methods used for it are more critical, as the wrong choice can lead to a lack or distortion of information.
Moreover, it is also worth noting that the post does not mention the long-term data collection goals. The text considers feedback, which allows managers to evaluate the effectiveness of a project or learn from mistakes. However, it is also important to note that analyzing the organization’s data over months or years of operation, even if it is purely quantitative, shows the big picture and the company’s flaws. For example, an increase in sales during one ad campaign and a decrease in sales during a second may demonstrate a preferred marketing strategy. Another example is long-term scientific research, which more clearly traces the influence of any factors on human health throughout life. Nevertheless, since long-term goals require the accumulation, storage, and analysis of large amounts of data, data management is equally important as data collection. Consequently, I find the post informative and consistent as it provides many facts and examples. However, the most important is that this post encourages thinking about the methods and purpose of data collection, which are only indirectly mentioned in the text.
Reference
Zhou, D., Yan, Z., Fu, Y., & Yao, Z. (2018). A survey on network data collection. Journal of Network and Computer Applications, 116, 9–23.