Dataveillance is the practice of monitoring and collecting metadata.  The word is a portmanteau of data and surveillance.  Dataveillance is concerned with the continuous monitoring of users’ communications and actions across various platforms.  For instance, dataveillance refers to the monitoring of data resulting from credit card transactions, GPS coordinates, emails, social networks , etc. Using digital media often leaves traces of data and creates a digital footprint of our activity.  This type of surveillance is not often known and is inconsistent. Unlikesubversivity , where individuals willingly monitoring their activity, dataveillance is more discrete and unknown. Dataveillance may involve the monitoring of groups of individuals. There exist three types of dataveillance: personal dataveillance, mass dataveillance, and facilitiative mechanisms . 
Unlike computer and network monitoring , qui collects data from computer networks and hard drives, monitors and collects dataveillance data (and metadata) through social networks and various other online platforms. Dataveillance is not confused with electronic surveillance. Electronic surveillance refers to the monitoring of oral and audio systems such as wire tapping. additional, electronic surveillance depends on having suspects  On the other hand, dataveillance can use data to identify an individual or a group.  Often times, these individuals and groups have some form of suspicion with their activity. 
The types of dataveillance are separated by the way data is collected, as well as the number of being associated with it.
Personal Dataveillance: Personal dataveillance refers to the collection and monitoring of a person’s personal data. Personal dataveillance can occur when an individual’s data causes suspicion or has attracted attention in some way.  Personal data can include such date, address, social security (or social insurance) number, and other unique identifiers .
Mass Dataveillance: Refers to the collection of data on groups of people.  The general distinction between mass data and personal data is the surveillance and collection of data as a group rather than an individual.
Facilitative Mechanisms: Unlike mass data monitoring a group is not targeted. An individual’s data is placed in a system or database along with various others where computer matching can separate patterns.  An individual data is never considered to be part of a group in this instance.
Benefits and concerns
There are many concerns and benefits associated with dataveillance. Dataveillance can be useful for collecting and verifying data in ways that are beneficial. For instance, personal dataveillance can be used by financial institutions to track fraudulent purchases on credit card accounts.  This has the potential to prevent and regulate fraudulent financial claims and resolve the issue.
Comparing to traditional methods of monitoring, dataveillance tends to be an economic approach, because it can help monitor more information in a less amount of time. In this case, the responsibility of monitoring is transferred to computers, thus reducing time and human labors in the process of surveilling. 
Dataveillance has also been useful in assessing security threats associated with terrorism. Authorities have dataveillance to help them understand and predict  Dataveillance is very important to the concept of predictive policing . Since predictive policing requires a great deal of effective data and can do just that. Predictive policing allows police to intervene in potential crimes to create safer communities and better understand potential threats.
Businesses also rely on dataveillance to help them understand the online activity for potential customers by tracking their online activity.  By tracking their online activity through cookies, and other methods, businesses are able to understand what they are doing with their existing customers.  While making online transactions, users often give away their information.  For businesses this information can help boost sales and attract attention to their products to help generate revenue.
On the other hand, there are many concerns that arise with dataveillance. Dataveillance assumes that our technologies and data are a true reflection of ourselves.  This presents itself as a possibility that we believe that our data is true to our own actions and behaviors.  This becomes a critical concern when associated with the surveillance of criminal suspects and terrorist groups. Those who monitor these suspects would then assume that the data they have collected reflects their actions.  This helps to understand potential or threats for criminals as well. 
There is also the lack of transparency and their data.  This is a critical issue with both the trust and the belief of the data and its uses.  Many social networks have argued that their use of their services is free of charge.  Several of these companies are collectively owned and operated. When data is volunteered to companies, it is difficult to know what you are doing. Much of an individual’s data is provided in a more customized marketing experience. Many of those social networks can share your information with intelligent agencies and authorities without a user’s knowledge.  Since the recent scandal involving Edward Snowden and National Security Agency, it has been revealed that  It has become very difficult to know what has happened. It is also important to know that they are worried about their information, but they are not always applied to their activities or behavior. With social networks collecting a large amount of personal data such as birth date, legal name, sex, and photos there is an issue of dataveillance compromising confidentiality. Ultimately, dataveillance can include online anonymity.
Despite dataveillance compromising anonymity, anonymity itself presents a crucial issue. Online criminals who steal users’ data and information may exploit it for their own gain. Tactics used by online users to conceal their identity and make it difficult for them. Having unique identifiers such as IP addresses allows for the identification of users actions, which are often used as piracy .
While data can be used to help businesses and their customers, there is no need for them. When visiting a business’s website often installs cookies onto users’ devices. Cookies have been made possible by them to their customers.  Companies may also find information they have collected on their customers to third parties.  Since these customers are not aware of these transactions. Furthermore, since dataveillance is discrete customers are very unlikely to know the exact nature of the data that has been collected. Education on tracking tools such as a critical issue. If businesses or online services are unwilling to define cookies or educate their users, they may have used them. 
The issue is that they have been engaged in the practices of data brokering. Data brokers , such as Acxiom , collect users; information and information for third parties. While they are collectively known to be collectors, they are generally understandable by everyday users.  It is difficult for everyday people to be more informed by lawyers.  This is becoming a new source of revenue for companies.
In terms of predictive policing , the proper use of crime and the combination of offline practices and technology have also become the challenges for police institutions. Too much reliance on the result of the police and the police, and the reduction of the cost of the police. and frequent in local communities at a frequent basis. Secondly, data security remains a huge dilemma, considering the access to crime data and the potential use of these data for negative purposes. Last but not least, discrimination towards certain communities could be made to the findings of data analysis, which could lead to improper behaviors or over-reaction of surveillance.
One of the major issues with dataveillance is the removal of a human actors who are replaced by an oversee data and construct a representation from it.  The removal of human actors can be predicted to be collected and monitored. This is largely due to the lack of logical reasoning present within data. Computer systems may only use the data they have, which is not necessarily an accurate depiction of individuals or their situations. Dataveillance is highly automated through computer systems which observes our interactions and activities.  High automated systems and technology eliminates the human understanding of our activities.
With such an increase in data collection and monitoring Countersurveillance is perhaps the most important concept focused on the tactics to prevent dataveillance. There are various tools associated with the concept of countersurveillance that disrupt the effectiveness and possibilities of dataveillance.
Privacy-enhancing technologies , which is known to reduce data collection.  PETs, such as adblocker , attempt to prevent other actors from collecting users data. In the case of adblock, the web browser is able to prevent the display of advertisements, which disrupts data collection about online users interactions.  For businesses that may limit their opportunities to provide online users with tailored advertisements.
Social networks, such as Facebook , have introduced new [ when? ] security measures to help users protect their online data. Users can block their posts and other information. While it is not necessary to prevent data breaches.
- Mass surveillance
- Global surveillance disclosures (2013-present)
- Capitalism monitoring
- Big data
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