Data: The New Oil?
“Do you allow the device to share your data with our company?” This message is often seen when you’ve bought a new phone, iPad, or computer. Maybe a lot of you click “Sure” and forget about it because of the excitement of your new tech. Have you ever wondered where your data went or how it’s used?
Data collection is an essential process for companies to gather, measure, and analyze data for a specific target, providing a precise outcome from the data. However, data collection is not only quantitative but also qualitative. For example, Apple uses Siri to collect qualitative data to improve their voice dictation. When you look at Siri settings, you can see a line of “Voice recordings are also sent to Apple for improvement purposes.” This is one place where your data went. Companies can use various techniques to collect different types of data. Qualitative data is still considered the most complex data to collect.
Why is qualitative data so hard to collect? Well, the answer is quite apparent. Unlike quantitative data (numbers), qualitative data can provide any information. It would be hard to collect data from questions such as “What’s your favorite NBA (National Basketball Association) team and why?” Such questions might be easy to answer for you, but they’re hard to store and quantify because everyone’s reasons will vary. Another example would be religion. It would be challenging for someone to collect data based on faith. One of my teachers, Dr. Christopher Mizel, asked us if it is possible to project why people believe in a particular religion. He kept a written/sound record of each person he asked, but every person answered it in a very different way. Everyone has another reason, opinion, or belief in religion. It is laborious and complex to these qualitative data into quantitative, but it’s not impossible.
Amazon collected all the user book reviews before, and it worked efficiently. But how did they convert qualitative to quantitative data? Algorithms. Amazon analyzed reader reviews and noted the frequency of a word appearing in the sentence. The algorithm would then count each time that word appears. The highest number can undoubtedly determine a pattern and help sort the reviews out. The dataset that amazon used would be called a second-hand data set.
Why is it secondhand and not first? Well, think about how data is collected and used. Amazon collects the reviews firsthand and reuses them for analysis purposes. Another example would be the renowned Matthew Fontaine Maury. He is credited for creating a map for the marines with the most precise details back in the 1800s. He used firsthand data collected by local fishermen and other seafarers to map the ocean currents and sea routes. The map would re then Maury’s map would consider as secondhand data.
Data collection can be complicated, but it can also be simple. An example of accessible data collection would be a survey involving only numbers. In contrast, collecting people’s qualitative answers from a survey would be considered hard. Moreover, if you collect or analyze data from an existing data set, the result of your data would be secondhand data. In a world of data, data collection is seemingly vital for every company, or even us.
