Exploring Data Collection and Its Forms
In a world where we generate data faster than anything else, society must find innovative ways to collect and utilize said data. When it’s collected, data analysis is po ssible, and intriguing insights can be found. This organization of information has been coined “data collection.”
So, what is it, specifically? Data collection can be defined as, a method that allows for data to be gathered systemically and reliably, driving data analytics and research. The researchers use data collection to organize data sets so they can test their research project’s hypotheses. They must organize the information in a manner that fits their project to create ideal outcomes. These figures can be organized in two forms: primary and secondary data sets.
Let us explore these two forms more in-depth.
Primary data sets are classified as data amassed by the researchers for their project. For example, when students are studying for a doctorate, they must conduct and record their information in hopes of answering their hypothesis. Just as studying for a doctorate is intensive, so is recording a comprehensive data set.
Let’s move on to secondary data sets. These are interesting because essentially, they are primary data sets; however, they become secondary when they are used by a researcher not involved in the data collection process. Much of the time researchers will use secondary data sets to expedite the process and if they need to use a variety of data sets. Secondary data sets are pre-existing data sets and are often reliable resources such as government census data.
However, just as primary data sets have their downsides, so do secondary ones. Their availability and reliability don’t always fit the needs of your research project. The data set was likely recorded with a specific purpose in mind, and it often does not correspond completely with your project’s intent. There is usually an overlap between the purpose for the data set and how a researcher will use it — this simply means that more than one data set will likely need to be accepted.
Now that we’ve discussed the downsides of both primary and secondary data sets, let’s cover the difficulties of data collection as a whole.
As mentioned earlier, data is one of the fastest generating things on the earth, and as such, privacy is about as thin as a veil. Yet, privacy is still important to many people around the globe. People want the benefits that data analysis has to offer yet retain their privacy. Oftentimes data collection disregards privacy, to a certain extent. Not only can the gathering of data encroach privacy in some cases, but it is also generally quite expensive and extensive.
Let’s use the assembling of census data as an example. With most country’s populations increasing, the amount of money required to reach all of them also increases considerably. And despite humongous efforts to reach everyone within the country, not all are possible to locate, some with the intent to not be found. These groups of people may include nomadic societies and illegal immigrants, respectively. So, although census data is highly useful when collected correctly, it is not always feasible to record. Such is the case with many theoretical primary data sets.
Data collection is a quintessential factor in the success of big data analytics — which is the future in its own regard. Both forms of data collection, primary and secondary data sets, have their unique benefits and disadvantages. Primary data sets’ advantages are secondary data sets’ disadvantages and vice versa. Deciding which form to use in your research project is reliant on your time and money constraints. No matter the choice, data collection will help to achieve success for the research project.
Sources:
Masters, Daniela. “How to Use Benefits Data to Design a Joined-up Approach.” Reba, Generali, 7 Feb. 2019, https://reba.global/content/how-using-benefits-data-can-help-employers-design-a-joined-up-wellbeing-approach. [1]
