1. Automating Research Changes the Definition of Knowledge. “Numbers speak for themselves” can refer to data analysts’ subjective interpretations of big data, an inference about individual motivations from an outsider’s perspective based on behavioral data. Obviously, this is different from data in which individuals express their motivation directly. This kind of non-number data is at a disadvantage in the era of big data.

  2. Claims to Objectivity and Accuracy are Misleading As we discussed on Monday, I agree with this point. In a culture where number is tied to objectivity and accuracy, big data is considered objective. Considering data to be capta to some degree, then the argument for the objectivity of big data is misleading. In addition, the interpretation of big data is also non-objective and contains the subjectivity of the analysts.

  3. Bigger Data are Not Always Better Data The problem of representation- A large amount of data from a small number of people does not mean that the interpretation can be generalized to a wider population. The problem of transparency- Researchers do not actually have a clear understanding of how datasets are generated, especially due to the involvement of algorithms(which are black boxes) in big data.

  4. Not All Data Are Equivalent Visualizations based on relational big data are not necessarily a true representation of the real network of relationships. In the case of social networks, for example, a family member may be less frequently connected online than many friends - it does not mean that the individual is closer to the friend. Perhaps it is just because he/she spends a lot of time with the family - they simply do not need to chat online for communication. In addition, the frequency of online communication simply cannot represent the quality of the conversation.