[et_pb_section fb_built=”1″ _builder_version=”4.24.2″ _module_preset=”default” custom_padding=”0px|||||” da_disable_devices=”off|off|off” global_colors_info=”{}” theme_builder_area=”post_content” da_is_popup=”off” da_exit_intent=”off” da_has_close=”on” da_alt_close=”off” da_dark_close=”off” da_not_modal=”on” da_is_singular=”off” da_with_loader=”off” da_has_shadow=”on”][et_pb_row _builder_version=”4.24.2″ _module_preset=”default” min_height=”321.7px” global_colors_info=”{}” theme_builder_area=”post_content”][et_pb_column type=”4_4″ _builder_version=”4.24.2″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”post_content”][et_pb_text _builder_version=”4.25.2″ _module_preset=”default” text_line_height=”1.8em” header_3_line_height=”1.3em” global_colors_info=”{}” theme_builder_area=”post_content”]<\/p>\n
Personal data is a\u00a0kind of new currency that can also be\u00a0used illegally, often leading to\u00a0identity and even property theft. It is predicted that data leaks will become an increasingly common phenomenon, carried out using complex methods and technologies. The increasing prevalence of this phenomenon highlights the need for even more effective protection of personal data.<\/p>\n
[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=”4.24.2″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”post_content”][et_pb_column type=”4_4″ _builder_version=”4.24.2″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”post_content”][et_pb_image src=”https:\/\/soflab.pl\/wp-content\/uploads\/2024\/06\/dane-sztuczne-po-anonimizacji-danych-rzeczywistych-scaled.jpg” alt=”dane wra\u017cliwe na \u015brodowiskach produkcyjnych” title_text=”dane sztuczne po anonimizacji danych rzeczywistych” _builder_version=”4.24.2″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”post_content”][\/et_pb_image][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=”4.25.2″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”post_content”][et_pb_column type=”4_4″ _builder_version=”4.24.2″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”post_content”][et_pb_text _builder_version=”4.25.2″ _module_preset=”default” text_line_height=”1.8em” header_line_height=”1.1em” global_colors_info=”{}” theme_builder_area=”post_content”]<\/p>\n
Reputation of a company affected by a data leak may become seriously compromised, which in turn, may lead to a potential image crisis, loss of customer trust, and sometimes even to business collapse. Therefore, protection of personal data is one of the priorities for all companies processing this type of data.<\/p>\n
Continuous technological development and advancement of market innovation force companies to take a number of actions supporting both competitiveness of their services and business growth at an equally fast pace. The companies need to constantly improve, develop, and update their products and services in order to adjust them to evolving consumer trends, needs, and expectations.<\/p>\n
Development of new product features involves certain risks, therefore, in order to effectively eliminate them, it is necessary to run a number of proper tests based on the test data. The test data may be production or synthetic data, but we must note that using the former type for testing bears the risk of personal data leakage.<\/p>\n
[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=”4.25.2″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”post_content”][et_pb_column type=”4_4″ _builder_version=”4.24.2″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”post_content”][et_pb_text _builder_version=”4.25.2″ _module_preset=”default” text_line_height=”1.8em” header_line_height=”1.1em” global_colors_info=”{}” theme_builder_area=”post_content”]<\/p>\n
Traditional testing based on artificially generated data may not provide sufficiently consistent and reliable information about the tested solution, which is why increasing number of companies are choosing to use a copy of the real-world production environment. In such cases the testers must remember that such data may include also personal data of customers, prone to certain risks. When anonymized, however, the data is no longer at risk and can be used as test data.<\/p>\n
Knowing this we face a dilemma: should we invest in extensive monitoring of the access to data or apply data anonymization?<\/strong><\/p>\n Generating the test data through anonymization of the production data can be an effective solution to enabling tests based on the real data without compromising the customer privacy. Other benefits of data anonymization include:<\/p>\n [\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=”4.24.2″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”post_content”][et_pb_column type=”4_4″ _builder_version=”4.24.2″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”post_content”][et_pb_image src=”https:\/\/soflab.pl\/wp-content\/uploads\/2024\/06\/ochrona-danych-osobowych-scaled.jpg” alt=”anonymisation to\u00a0protect personal data ” title_text=”protection of personal data” _builder_version=”4.24.2″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”post_content”][\/et_pb_image][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=”4.25.2″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”post_content”][et_pb_column type=”4_4″ _builder_version=”4.24.2″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”post_content”][et_pb_text _builder_version=”4.25.2″ _module_preset=”default” text_line_height=”1.8em” header_line_height=”1.1em” global_colors_info=”{}” theme_builder_area=”post_content”]<\/p>\n The development of data anonymization technologies, such as the Soflab G.A.L.L. tool, opens up new opportunities for organizations that, on the one hand, want to run consistent and reliable tests of their solutions, and on the other, ensure full security of sensitive data. Data anonymization enables effective testing without compromising customer privacy, which increases customer trust and loyalty, and helps avoid costly data leaks.<\/p>\n The spectrum of data anonymization tools aiming at protection of the user privacy is very broad and applicable in multiple diverse sectors, enabling effective analyses and testing.<\/p>\n Below we show some specific examples of how data anonymization can be used:<\/p>\n Banking:<\/strong> in the banking sector, data anonymization can be used to protect the customer data during credit risk analyses or to assess the effectiveness of marketing strategies. For example, anonymizing transaction data can allow banks to identify patterns in customer behaviors without revealing their identities.<\/p>\n Healthcare:<\/strong> anonymization of the patient data can be crucial for scientific research or epidemiological analyses. With anonymization of the medical data it is possible to maintain patient privacy, while enabling the use of this data for research purposes.<\/p>\n Retail:<\/strong> anonymization of the customer shopping data can help retailers identify shopping trends and customize offers without compromising customer privacy. By anonymizing data, the retailers can better understand their customers’ preferences and shopping behaviors.<\/p>\n Telecommunication:<\/strong> anonymization of subscriber data can be used to analyze the network load or plan infrastructure. This way telecom operators can manage their networks efficiently without compromising user privacy.<\/p>\n Education:<\/strong> in the education sector anonymization of students\u2019 data may be used to analyze exam results or assess effectiveness of the existing curricula. In addition, it enables scientific research and statistical analysis without revealing the participants\u2019 identities.<\/p>\n Transport:<\/strong> anonymization of the passenger travel data may help analyze traffic patterns or plan timetables. It allows transport companies to identify travel trends and optimize the operation of transport systems without the risk of violating passenger privacy.<\/p>\n [\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=”4.24.2″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”post_content”][et_pb_column type=”4_4″ _builder_version=”4.24.2″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”post_content”][et_pb_image src=”https:\/\/soflab.pl\/wp-content\/uploads\/2024\/06\/po-co-anonimizowac-dane-scaled.jpg” alt=”use of data anonymisation” title_text=”po co anonimizowa\u0107 dane” _builder_version=”4.24.2″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”post_content”][\/et_pb_image][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=”4.25.2″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”post_content”][et_pb_column type=”4_4″ _builder_version=”4.24.2″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”post_content”][et_pb_text _builder_version=”4.25.2″ _module_preset=”default” text_line_height=”1.8em” header_line_height=”1.1em” global_colors_info=”{}” theme_builder_area=”post_content”]<\/p>\n Each of the above sectors processes huge amounts of personal data (including sensitive data), so anonymization of this data plays a key role in protecting privacy of the users of systems and applications, enabling effective analyses and testing.<\/p>\n The key benefit of anonymization for the companies and organizations is the possibility to effectively use valuable data for research, analytics and marketing purposes, while ensuring its complete security.<\/p>\n If you’d like to know more about data anonymization and the ways we can help your business protect your customers’ private data, please contact us. Together we can build the most secure and trusted environment for your data. Find out how we can work together to create a secure and trusted environment for your data.<\/p>\n [\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section]<\/p>\n","protected":false},"excerpt":{"rendered":" Traditional testing based on artificially generated data may not provide sufficiently consistent and reliable information about the tested solution, which is why increasing number of companies are choosing to use a copy of the real-world production environment. In such cases the testers must remember that such data may include also personal data of customers, prone to certain risks. When anonymized, however, the data is no longer at risk and can be used as test data.<\/p>\n","protected":false},"author":23,"featured_media":243044,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_et_pb_use_builder":"on","_et_pb_old_content":"","_et_gb_content_width":"","footnotes":""},"categories":[182],"tags":[287,272],"class_list":["post-243139","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-data-anonymization-en-en","tag-data-anonymization","tag-software-testing"],"yoast_head":"\n\n
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Effective testing thanks to data anonymization<\/strong><\/h1>\n
Data anonymization in diverse sectors<\/strong><\/h3>\n
Anonymization of personal data is a priority for the organizations processing large amounts of test data<\/strong><\/h1>\n