{"id":5027,"date":"2020-06-18T10:00:37","date_gmt":"2020-06-18T08:00:37","guid":{"rendered":"https:\/\/blog.startups.ch\/en\/?p=5027"},"modified":"2020-06-16T17:57:47","modified_gmt":"2020-06-16T15:57:47","slug":"is-my-boss-allowed-to-collect-my-data-employee-data-mining","status":"publish","type":"post","link":"https:\/\/blog.startups.ch\/en\/is-my-boss-allowed-to-collect-my-data-employee-data-mining\/","title":{"rendered":"Is my boss allowed to collect my data? \u2013 Employee Data Mining"},"content":{"rendered":"\n

In times of\nBig Data, Artificial Intelligence and Machine Learning, more and more employers\nare using algorithms to collect data about their employees. This process is\nknown as data mining and is used to make forecasts about HR developments and to\noptimize HR decisions.<\/h2>\n\n\n\n
\"\"<\/figure>\n\n\n\n

Big-data\nanalytics and algorithms are omnipresent today, even in working life. Modern\ntechnologies are used to collect data on employees, for example by analyzing\ne-mails, in order to monitor employee performance and make targeted personnel\ndecisions. This process is known as data mining. But is  it permissible from the point of view of\nlabour and data protection law?<\/p>\n\n\n\n

Data\nprocessing by the employer<\/strong><\/p>\n\n\n\n

Data protection law applies to personal data, i.e. to information relating to an identified or identifiable person (Art. 3 FADP<\/a>). Processing is defined as any handling of personal data regardless of the means and procedures used, in particular the acquisition, storage, use, revision, disclosure, archiving or destruction of data. A person is identifiable if his or her identity can be deduced from the information collected or can be determined with reasonable effort. Since the data collected in companies is largely personal data, it is usually covered by the Federal Act on Data Protection<\/a>.<\/p>\n\n\n\n

Protective\nprovisions in labour law<\/strong><\/p>\n\n\n\n

According to Art. 328b CO<\/a>, the employer may only process data about the employee if it concerns his suitability for the employment relationship or is necessary for the execution of the employment contract. This condition is fulfilled, for example, when it comes to ensuring legitimate operational procedures, such as quality assurance, the preservation of evidence in business transactions or the control of the employee’s compliance with instructions. Article 26(1) of the Labour Code Decree also prohibits the use of monitoring and control systems designed to monitor the behaviour of workers at the workplace. This provision excludes performance and security checks. If surveillance systems are used for this purpose, however, they must be designed in such a way that they do not impair the employees’ freedom of movement or health.<\/p>\n\n\n\n

Protection provisions in data protection law<\/strong><\/p>\n\n\n\n

According to Art. 4 DSG<\/a>, personal data may only be processed lawfully. To ensure that no personal rights are unlawfully violated, surveillance requires a justification. It should be noted that the consent of the employee alone is not sufficient, because in the employment relationship the employee has little choice but to consent. The monitoring and evaluation of data must also be proportionate and carried out in good faith. This means that the employer must choose the mildest form of surveillance. Big-data analyses seldom meet this criterion, as usually vast amounts of unneeded information are collected. Furthermore, the collection of personal data must be appropriate, and the transparency requirement must be respected. The appropriateness can be ensured by the adoption of a monitoring regulation in which the reasons that allow data collection and the rules for data processing are recorded. In order to maintain transparency, the employer must inform the employees about the scope and purpose of the monitoring.<\/p>\n\n\n\n

Anonymous data<\/strong><\/p>\n\n\n\n

Because data protection law only covers personal data, i.e. data that\nallows identification, the protection provisions can theoretically be\ncircumvented by making the data anonymous. Processing is permitted in this case\nif the data cannot be deanonymised, i.e. the data does not allow any\nconclusions to be drawn about the identity of persons. But careful: Although\nthis makes circumvention of data protection law possible in theory, it is\ndifficult to implement in practice. Modern technologies such as big-data\nalgorithms and machine-learning tools are often able to draw conclusions about\nthe persons underlying the analysis even on the basis of apparently anonymous\ndata.<\/p>\n\n\n\n

Do you have any innovative business idea you would like to make come true? Use STARTUPS.CH experts to become self-employed.<\/p>\n\n\n\n\u00bb Blog<\/a>\u00bb Found online<\/a>\n","protected":false},"excerpt":{"rendered":"

In times of Big Data, Artificial Intelligence and Machine Learning, more and more employers are using algorithms to collect data about their employees. This process is known as data mining and is used to make forecasts about HR developments and to optimize HR decisions. Big-data analytics and algorithms are omnipresent today, even in working life. […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","enabled":false}}},"categories":[10],"tags":[],"jetpack_publicize_connections":[],"aioseo_notices":[],"views":1428,"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"jetpack_shortlink":"https:\/\/wp.me\/p54Pb1-1j5","jetpack-related-posts":[],"_links":{"self":[{"href":"https:\/\/blog.startups.ch\/en\/wp-json\/wp\/v2\/posts\/5027"}],"collection":[{"href":"https:\/\/blog.startups.ch\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blog.startups.ch\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blog.startups.ch\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/blog.startups.ch\/en\/wp-json\/wp\/v2\/comments?post=5027"}],"version-history":[{"count":1,"href":"https:\/\/blog.startups.ch\/en\/wp-json\/wp\/v2\/posts\/5027\/revisions"}],"predecessor-version":[{"id":5029,"href":"https:\/\/blog.startups.ch\/en\/wp-json\/wp\/v2\/posts\/5027\/revisions\/5029"}],"wp:attachment":[{"href":"https:\/\/blog.startups.ch\/en\/wp-json\/wp\/v2\/media?parent=5027"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blog.startups.ch\/en\/wp-json\/wp\/v2\/categories?post=5027"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blog.startups.ch\/en\/wp-json\/wp\/v2\/tags?post=5027"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}