User:Louise Yang/sandbox

Data culture is the principle established in the process of social practice in both public and private sectors which requires all staffs and decision-makers to focus on the information conveyed by the existing data, and make decisions and changes according to these results instead of leading the development of the company based on experience in the particular field. These data might include but are not limited to: general economical or social trends in the market, sales volume of products, or even performance of staffs pointing to their efficiency and productivity. In general, to build data culture, the departments and organizations have to let the data talk itself, and trust the steering of statistics.

History
The idea of data culture has been under the spotlight of business field since the beginning of the 21st century, and is gaining popularity in recent years. Although first introduced in a scientific approach, the idea is now associated with both the science field and social sectors.
 * In 2000, Geoffrey C. Bowker clearly conveyed the idea of "local data culture" in his academic paper in terms of biodiversity.
 * In 2014, Microsoft made a series of announcements and their intention to build data culture in everyday life through their services including Office 365, Azure and SQL Server.
 * In 2015, Microsoft organized a series of workshops about data culture in alliance with Hortonworks and KPMG UK, offering data analysts and other professional working in the field of Big Data an opportunity to understand the data culture of this gigantic company and help them build their own data culture in private sectors.
 * The Data Power Conference 2017 was held in Canada at Carleton University, Ottawa between June 22nd & 23rd, 2017.

Participants
Participants are both producers of data and people who can contribute to the data culture by making influential changes.

Data Analysts
Data analysts serve as an important part in the establishment of a data culture, as they often receive first-hand material and raw data, and the way they connect all the components together can determine the efficiency of communication between ordinary participants and decision-makers. Also, they are responsible for the analysis of information conveyed by the data.

Decision-makers
Decision-makers are those who apply changes and determine the direction of development in a company. In this case, they would make important decisions according to the trends and information highlighted by the data produced either in their own companies internally or statistics of the target market they want their corporations to aim at.

Microsoft
The Microsoft team under management of Satya Nadella highly depend on data to drive both important market decisions and their daily behaviours. Microsoft focuses on data visualizaion, and they advocates that participants and employees should have the right to access data of the company. They are using tools such as the Power BI to have individual workers involved in the game and contribute themselves to the future of the company.

Capita
Capita is a British agency helping clients in both government departments and enterprises to understand themselves better, using advanced techniques of data analysis. Established in 1984, they advocate their clients to build their own data culture with relative database in their own field of career.

Socrata
Socrata is a US-based company which serves both public sectors and the civil society. They help companies and organizations reach open data from the federal government in order to either improve the working progress of the government or assist social groups that are lack of resources. Their core value is tied to Open Data, and they tend to focus on corporations which are in need of funding in order to process data analysis. The cloud-based service they provide allow government departments to communicate with the public through publishing their official data.

Future Development
While establishing data culture, it is important to notice several highlighted chracteristics.

Firstly, data do not stay isolated, but is related to the context in which they are produced.

Data privacy
Some companies still consider it is significant to keep the data private in the executive level. Although it is possible for all staffs in a company to produce and process the data together, it is restricted for data to be free from the approach of participants on the elementary level.

Rationalism
A rational method of building or expanding an enterprise is an opposite approach to an empirical one. The decisions of rationalists are often evaluated according to their personal spirit and their existing cognition of the world. In terms of this approach, decision-makers depend on logic rather than social phenomena as their evidences to make changes, therefore conclusions led by data are sometimes ignored, especially when new trends emerge and the logical system of decision-makers are challenged.

Reference

 * McQuiggan, J., Sapp, A. W., Safari Books Online (Firm), & ProQuest (Firm). (2014). Implement, improve and expand your statewide longitudinal data system: Creating a culture of data in education (1st ed.). Hoboken, N.J: J. Wiley & Sons.


 * McArdle, G., Lauriault, T. P., & Kitchin, R. (2018). Data and the city. Abingdon, Oxon;New York, NY;: Routledge.

General Ideas
I want to create this new page referring to data culture. A general concept I have for data culture is an approach of communication with application of data. Data culture can be built in either business or education environment, when people involved in this particular group learn to narrate issues, state opinions and interact with other members, no matter to support or to persuade by data/statistics. The creation of data culture is an unavoidable path in modern society with the occupation of data everywhere, and the development of data culture in institutions will have positive influence on the balanced and digitized development of individuals and entities. I think I will have a more comprehensive idea about data culture after studying the course contents for Week 8. I will also add some real cases and positive practices of data culture in either academic or business fields.

Reliable Sources (Bibliography)

 * Patil, D., Mason, H., Safari Books Online (Firm), & ProQuest (Firm). (2015). Data driven: Creating a data culture. Sebastopol, CA: O'Reilly Media.
 * Torbeck, L. (2011). Data culture. Journal of Validation Technology, 17(4), 12.
 * Torbeck, L. (2011). Data culture. Journal of Validation Technology, 17(4), 12.

Is everything in the article relevant to the article topic? Is there anything that distracted you?
The article has a clear structure, and all the examples and cases mentioned in this article have close relationship to the topic itself, therefore nothing was really distracting except for minor grammar mistakes. For example, a sentence framed as "When visiting a business's website often install cookies onto users' devices.", which really confused me grammatically without its subject.

Is the article neutral? Are there any claims, or frames, that appear heavily biased toward a particular position?
This article covers both the benefits and concerns that dataveillance brings to the society. In addition, strategies focusing on counterveillance issues are also explained at the latter part of the article, so I consider this article as fairly neutral, and this article is carefully on balancing all the viewpoints from both sides.

Check a few citations. Do the links work? Is there any close paraphrasing or plagiarism in the article?
The links provided in both the citation part and the reference part works. As far as I retrieved, no sentences in this article violates the definition of paraphrasing by plagiarising.

Is each fact referenced with an appropriate, reliable reference?
The statements and facts provided in this article are referenced with appropriate and reliable references, such as published journal articles and significant articles which has ISBN or ISSN numbers. Also, some articles come from famous magazines and journals, which add to the reliability of this article from other positions other than the academic field.

Is any information out of date? Is anything missing that could be added?
Most of the information are up-to-date. Over half of them are published/retrieved in the recent 5 years, and only 2 of the 14 sources are published before year 2000. However, I consider it important to add some examples and cases to help readers understanding when bringing in some new concepts, such as instances of predictive policing or cases operated by Acxiom. Also, the history of dataveillance and its position in modern society should also be highlighted in the article.

How is the article rated? Is it a part of any WikiProjects?
According to the Talk page of this article, it is rated as a C-Class one. This article is consisted in three WikiProjects, WikiProject Espionage, WikiProject Internet and WikiProject Mass surveillance. Unfortuanately, no discussion was held about this article on the Talk page.

How does the way Wikipedia discusses this topic differ from the way we've talked about it in class?
When we studies Dataveillance in COMS 2200A, a large amount of information about data brokers are talked about, which should be an important part when referring to this topic, so as to expand the topic in terms of the price of data, and risks of of the emergence of data brokers.This part is not covered in detail in this article. Also, many examples were offered when we talked about dataveillance in class. Comparing to class materials, this article tends to be a general introduction of dataveillance with no further investigation into this topic.