5 Ways of Conceptualizing Data

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Data can be viewed as a measurement of numbers, and characters that are set in a way to understand a certain subject. However, there are many different ways to view data; such as conceptualizing data. These are five ways of conceptualizing data. They all have positive and negative points to each technique. Although they are different, they all bring up questions and concerns with data collection and what happens with the information afterward. Another concern is what is the goal with the data that has been collected depending on the category. The five ways of conceptualizing data are technically, ethically, politically and economically, spatially/ temporal, and philosophically. Typically viewed by critical data scholars, they have all of these ways of viewing data because it is important to see the different ways that data can be viewed and to see if there may be any bias. Not only is it important to see if there is any bias, however, it is also important to understand what all the data will mean in the bigger picture. The way that this is normally done is by understanding raw data, then placing them into categories that will help with the better understanding and creating new knowledge.

Technically

Technically viewing data concerns the knowledge about the quality of data, if it is reliable, if it is authentic, if it is valid. It is also about knowing how the data is structured, shared, processed, and analyzed.[1] There are views about the concerns around data such as the representativeness, how it is uncertain, the reliability of it, the chances of any errors, the likelihood of any bias, and around the measuring of the research design and the execution of it.[2] There are also questions around if this form of scientific technique is going to bring the data that is wanted and needed.[3] Other reliability concerns go with this technical view about data such as Quixotic reliability, Diachronic reliability, and Synchronic reliability. Quixotic reliability concern is where there is one observation method which produces unvarying measurements.[4] Diachronic reliability is the stability of an observation through time. Lastly, Synchronic reliability is the similarity of observations within the same time period.[5] With it being technology, there are many different ways that errors could arise, such as, missing data, mistakes, misunderstaning's, bias’, and uncertainty.[6]

Ethically

The ethical view of data is more about the idea of why the data is generated, and what use the data is going to be placed in. There are concerns around how the data will be shared, protected, traded, and to how they are employed”.[7] This also deals with the issue of sensitivity. Some data is low when it comes to sensitivity, such as the traffic. However, some are a lot higher, such as speaking to survivors of crime.[8] With the sensitivity scale, there comes privacy issues, how someone may be treated, and the issue of human rights.[9] It is helpful to know that some companies have a data protection act and have privacy laws.[10] Other components that add to the category of Ethics are the question of equality, fairness, justice, honesty, respect, entitlements, rights, and care of the information that is provided and towards those that give the information.[11] The honesty, respect and the care of the information can also be misinformed to the subject that is giving the data willingly. Causing ethical concerns for how long the information will be kept, or what the information will be used for. This is an ethical concern in the exchange of the subject and the researcher.[12]

Politically and economically

politically and economically viewed data is seen to how the data could be viewed or theorized as public goods, intellectual property, political capital, and how they are traded and how they are regulated.[13] Economically there are many decisions when funding data researching, as well as investing in data researching. Data could be used to manage goals and raise the profits and values to those that invest in it.[14] Such as the multi-billion-dollar data marketplace, where many companies are trading and using that data to help themselves make a profit. It is positively effecting due to the production of knowledge.[15] The more that the company knows about what the people want, and how to market to them, the more that they may profit and gain off of the data, due to them giving what the people want. However, there still is the political side to this. Although the data can make a profit and is economically great, there is also the competition which want to influence opinions and make the data terrain greater.[16] It is also political because the difference between publicly good data, which is shared with anyone that can have access to it, is much different than business data. This is because business data is wanting to keep the data that they have found and use it to their advantage, such as the “production of knowledge.” [17] The publicly good data is free to anyone that wants to view it, which would not be helpful in any way to any business strategies or marketing.[18]

Spatially/temporal

Spatial and temporal views data around technical, ethical, political, and economic Regime with the production of the data.[19] The way that the terms spatial and temporal can be viewed is around how the data is developed and changed across time and space. Although, depending on the time and where this data is being collected, the process, the analysis, the storage of some information, yet not of others will be different, just due to a time frame and area will be different than others because of the different history that has happened and the different geographical locations. As noted the process of taking in data changes over time, however, they are never sudden changes. These changes happen slowly over time due to different laws that come in place around how data is handled or protected, the different forms of organizing, the improvements around administration, if any new technology has formed, when the methods of data sorting have changed, along with the methods of sorting the data, the geographical statistics that vary and the new techniques of statistics.[20] Not only does the geographical location change how the assemblage of data is taken is, but it can also be different depending on the person due to how they manage the data, or how they produce it.[21]

Looking over data temporally can bring forth either questions or patterns depending on what the data is about. An example of this is looking at graphs that have time in them. They present inclines and declines in a pattern about the data over time.[22] Spatial data, on the other hand, looks more towards the geographical sense in the data. The information that is gathered could be about the location, the size or the shape of a particular object. A system that uses spatial data is GIS (Geographical Information System) [23]

Philosophical

Philosophy brings forth views around the areas of epistemology and ontology. In this view of data, there is no interpretations, opinions, importance, or relevance of the data that has been found and processed. The data is simply measured for what it is. Which brings forth to how it is viewed. The data that is viewed philosophically is also viewed in an objective way which means that the data is fixed in some way to prove a specific point. Although the data may be truthful, how the data was provided and how it is placed makes the difference. The data is also viewed in a realist view such as how things truly are. No information is changed, everything is the way that it is and is seen for that.[24] This view also brings up issues around property rights.[25] Who would own what and who can have the right to take things.

References

  1. (Kitchin, p.12)
  2. (Kitchin, p.13)
  3. (Kitchin, p.13)
  4. (Kitchin, p.13-14)
  5. (Kitchin,p.14)
  6. (Kitchin, p.14)
  7. (Kitchin, p.15)
  8. (Kitchin, p. 15)
  9. (Kitchin, p. 15)
  10. (Kitchin, p.15)
  11. (Kitchin, p.14)
  12. (Jacob)
  13. (Kitchin, p.16)
  14. (Kitchin, p.15)
  15. (Kitchin, p. 15)
  16. (Kitchin, p.16)
  17. (Kitchin, p.16)
  18. (Kitchin, p. 16)
  19. (Kitchin, p.17)
  20. (Kitchin, p.17)
  21. (Kitchin, p.17)
  22. (Whitney)
  23. (Rouse)
  24. (Kitchin, p.17-19)
  25. (Liu, p.61)

Works cited

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  • Kitchin, Rob (2014). "Conceptualising data" (pdf). The data revolution: Big data, open data, data infrastructures & their consequences. London: Sage. http://www.uk.sagepub.com/upm-data/63923_Kitchin_CH1.pdf. 
  • Metcalf, Jacob, Emily F. Keller, and danah boyd. 2016. “Perspectives on Big Data, Ethics, and Society.” Council for Big Data, Ethics, and Society.
  • Rouse, Margaret. (2013). "What Is Spatial Data? - Definition from WhatIs.com." SearchSQLServer. TechTarget.
  • Liu, Hong. (2016). "Philosophical Reflections on Data. " Philosophical Reflections on Data. Science Direct.
  • Whitney, Hunter. (2014). "It's About Time." It's About Time: Visualizing Temporal Data to Reveal Patterns and Stories | UX Magazine. UX Magazine.

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