{"id":13585,"date":"2023-01-05T13:14:53","date_gmt":"2023-01-05T12:14:53","guid":{"rendered":"https:\/\/parm.com\/?p=13585"},"modified":"2026-03-02T16:08:52","modified_gmt":"2026-03-02T15:08:52","slug":"data-quality-3","status":"publish","type":"post","link":"https:\/\/new-site.parm.com\/en\/data-quality-3\/","title":{"rendered":"Data Quality"},"content":{"rendered":"\n<div class=\"et_pb_section_0 et_pb_section et_section_regular et_block_section\"><div class=\"et_pb_row_0 et_pb_row et_block_row\"><div class=\"et_pb_column_0 et_pb_column et_pb_column_4_4 et-last-child et_block_column et_pb_css_mix_blend_mode_passthrough\"><div class=\"et_pb_text_0 et_pb_text et_pb_bg_layout_light et_pb_module et_block_module preset--module--divi-text--default\"><div class=\"et_pb_text_inner\"><h1>Data Quality Management<\/h1>\n<p><span style=\"font-size: large;\">Accurate analyses and correct decisions thanks to clean data<\/span><\/p>\n<\/div><\/div><\/div><\/div><div class=\"et_pb_row_1 et_pb_row et_block_row\"><div class=\"et_pb_column_1 et_pb_column et_pb_column_4_4 et-last-child et_block_column et_pb_css_mix_blend_mode_passthrough\"><div class=\"et_pb_image_0 et_pb_image et_pb_module et_block_module\"><span class=\"et_pb_image_wrap\"><img decoding=\"async\" src=\"https:\/\/new-site.parm.com\/wp-content\/uploads\/2022\/12\/Post_Data_quality.jpg\" width=\"2500\" height=\"1313\" srcset=\"https:\/\/new-site.parm.com\/wp-content\/uploads\/2022\/12\/Post_Data_quality.jpg 2500w, https:\/\/new-site.parm.com\/wp-content\/uploads\/2022\/12\/Post_Data_quality-1280x672.jpg 1280w, https:\/\/new-site.parm.com\/wp-content\/uploads\/2022\/12\/Post_Data_quality-980x515.jpg 980w, https:\/\/new-site.parm.com\/wp-content\/uploads\/2022\/12\/Post_Data_quality-480x252.jpg 480w\" sizes=\"(min-width: 0px) and (max-width: 480px) 480px, (min-width: 481px) and (max-width: 980px) 980px, (min-width: 981px) and (max-width: 1280px) 1280px, (min-width: 1281px) 2500px, 100vw\" class=\"wp-image-13592\" title=\"Post_Data_quality\" alt=\"Data Quality\" \/><\/span><\/div><\/div><\/div><div class=\"et_pb_row_2 et_pb_row et_pb_equal_columns et_block_row\"><div class=\"et_pb_column_2 et_pb_column et_pb_column_4_4 et-last-child et_block_column et_pb_css_mix_blend_mode_passthrough\"><div class=\"et_pb_text_1 et_pb_text et_pb_bg_layout_light et_pb_module et_block_module preset--module--divi-text--default\"><div class=\"et_pb_text_inner\"><p>The benefit of business intelligence stands and falls with the quality of the data used. Insights gained from unclean data are flawed. And decisions made on the basis of such data can lead to major problems. True to the motto \"garbage in, garbage out.\" High data quality is therefore a decisive success factor for companies. However, although the importance of data quality is well known in most companies, the data in many companies is still flawed.<\/p>\n<\/div><\/div><\/div><\/div><div class=\"et_pb_row_3 et_pb_row et_block_row\"><div class=\"et_pb_column_3 et_pb_column et_pb_column_4_4 et-last-child et_block_column et_pb_css_mix_blend_mode_passthrough\"><div class=\"et_pb_text_2 et_pb_text et_pb_bg_layout_light et_pb_module et_block_module preset--module--divi-text--default\"><div class=\"et_pb_text_inner\"><div id=\"ez-toc-container\" class=\"ez-toc-v2_0_82_2 counter-hierarchy ez-toc-counter ez-toc-custom ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<span class=\"ez-toc-title-toggle\"><\/span><\/div>\n<nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/new-site.parm.com\/en\/data-quality-3\/#What_is_data_quality_and_data_quality_management\" >What is data quality and data quality management?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/new-site.parm.com\/en\/data-quality-3\/#The_main_reasons_for_poor_data_quality\" >The main reasons for poor data quality<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/new-site.parm.com\/en\/data-quality-3\/#What_are_the_criteria_for_data_quality\" >What are the criteria for data quality?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/new-site.parm.com\/en\/data-quality-3\/#What_can_be_done_to_ensure_high_data_quality\" >What can be done to ensure high data quality?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/new-site.parm.com\/en\/data-quality-3\/#Tips_for_data_quality_management\" >Tips for data quality management:<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/new-site.parm.com\/en\/data-quality-3\/#Conclusion\" >Conclusion<\/a><\/li><\/ul><\/nav><\/div>\n<h2><span class=\"ez-toc-section\" id=\"What_is_data_quality_and_data_quality_management\"><\/span>What is data quality and data quality management?<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Data quality is a subjective term that must be defined individually for each company. It is the overall characteristics of a data set that meet the requirements of the users.<br \/>Data quality management refers to all processes and procedures that deal with ensuring high data quality. This includes the identification, cleansing and provision of data.<\/p>\n<\/div><\/div><div class=\"et_pb_text_3 et_pb_text et_pb_bg_layout_light et_pb_module et_block_module preset--module--divi-text--default\"><div class=\"et_pb_text_inner\"><h2><span class=\"ez-toc-section\" id=\"The_main_reasons_for_poor_data_quality\"><\/span>The main reasons for poor data quality<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Data is never 100 per cent clean and perfect. This may be due to the fact that data enters a company in different ways. As a result, it can be outdated, duplicated or inconsistent. To ensure the highest possible data quality, it helps if you know the main reasons for faulty data. This way you can avoid poor quality data in advance. These are the most important reasons:<\/p>\n<ul>\n<li><strong>Manual data entry:<\/strong> In many companies, manual data entry takes place. However, this is very error-prone. Data can be entered in the wrong place or in the wrong format and a typo or number error can easily occur.<\/li>\n<li><strong>Data conversion:<\/strong> When transferring data from one location to another, data can be accidentally lost or changed. This could be because the data is stored in different formats or the data structure is different.<\/li>\n<li><strong>Real-time updates:<\/strong> In order to make good decisions, it is important to work with up-to-date data at all times. But even here, errors can occur if individual data sets may not have been updated at the time of an analysis or because there has not been enough time to review the data.<\/li>\n<li><strong>Merging data:<\/strong> If data has to be merged, for example in consolidations, company mergers or system changes, errors such as invalid formats, duplicates and conflicts can also occur.<\/li>\n<li><strong>System upgrades:<\/strong> Frequent updates or upgrades of your software can also lead to errors, as it is possible that data is deleted or corrupted in the process.<\/li>\n<li><strong>Indiscriminate data collection: Companies often collect all the data that is generated.<\/strong> This holds some potential, as the data might be needed in future. However, it also makes quality assurance and data analysis more difficult. Therefore, if possible, only the data that is really needed should be stored.<\/li>\n<\/ul>\n<\/div><\/div><div class=\"et_pb_text_4 et_pb_text et_pb_bg_layout_light et_pb_module et_block_module preset--module--divi-text--default\"><div class=\"et_pb_text_inner\"><h2><span class=\"ez-toc-section\" id=\"What_are_the_criteria_for_data_quality\"><\/span>What are the criteria for data quality?<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Various criteria show you how high the quality of your data is and whether the data is suitable for a particular task.<\/p>\n<ul>\n<li><strong>Completeness: <\/strong><em>Are all data sets that are needed complete?<\/em><br \/>Incomplete data may not be usable or only partially usable. Therefore, it is important to ensure that a data set contains all necessary attributes and that the attributes in turn contain all necessary data.<\/li>\n<li><strong>Relevance: <\/strong><em>Is all the data needed for the intended purposes available?<\/em><br \/>Not all data that is generated is relevant for your purposes. Therefore, it should be collected deliberately so that only as much data as necessary is collected. This is especially true for customer data, which is subject to data privacy.<\/li>\n<li><strong>Accuracy: <\/strong><em>Is the data collected accurate and stated as needed?<\/em><br \/>When collecting data, it is important to ensure that the data is accurate. At the same time, it should also be in the level of detail required. This means, for example, that all necessary decimal places should be stored.<\/li>\n<li><strong>Timeliness: <\/strong><em>Are the data sets up to date?<\/em><br \/>New data is constantly being created in a company. Therefore, it makes sense to always carry out analyses with up-to-date data in order to detect changes or problems at an early stage. In practice, we often recommend that our clients refer to data with a reliable status when making decisions. Depending on the situation, it may make sense to use data from the previous day, for example, as live data can change within a very short time.<\/li>\n<li><strong>Validity: <\/strong><em>Is the origin of the data reliable, or does the data come from reliable sources?<\/em><br \/>The origin of the data sets should be traceable in order to assess whether the data is reliable.<\/li>\n<li><strong>Availability and accessibility: <\/strong><em>Can users easily access the data they need? Is it available in the required format?<\/em><br \/>For example, if relevant data is spread across different tools or is not in the right format, easy accessibility is not always given.<\/li>\n<li><strong>Consistency: <\/strong><em>Are there contradictions or duplicates in the data? Are there inconsistencies with other data?<\/em><br \/>Data must be unambiguous, without contradictions to itself or other data and without redundancies, as well as uniformly structured.<\/li>\n<\/ul>\n<\/div><\/div><div class=\"et_pb_text_5 et_pb_text et_pb_bg_layout_light et_pb_module et_block_module preset--module--divi-text--default\"><div class=\"et_pb_text_inner\"><h2><span class=\"ez-toc-section\" id=\"What_can_be_done_to_ensure_high_data_quality\"><\/span>What can be done to ensure high data quality?<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>In order for data to be of consistently high quality, it is first necessary to define how its quality can be measured. Then the data should be analysed, cleansed and monitored against the defined criteria. This process should be carried out regularly in order to maintain consistently high data quality and to be able to permanently eliminate sources of error.<\/p>\n<\/div><\/div><div class=\"et_pb_text_6 et_pb_text et_pb_bg_layout_light et_pb_module et_block_module preset--module--divi-text--default\"><div class=\"et_pb_text_inner\"><h3>1. Define criteria<\/h3>\n<p>The first step is to determine which criteria should be used to measure the quality of the data. For example, it is defined which data must be available for your purposes and in which format it should be available.<\/p>\n<h3>2. Data profiling \/ data analysis:<\/h3>\n<p>Data analysis is used to identify duplicate data, contradictions, errors and incomplete sets of information. In this way, the quality of the data can be measured and the data can be cleaned and updated in further steps. In addition, the data analysis can be used to identify sources of errors and thus take measures to ensure that the detected errors do not happen again in future.<\/p>\n<h3>3. Data Cleaning<\/h3>\n<p>In the data cleaning step, the problems identified in the data analysis are corrected. This means that duplicates are deleted, incomplete data is added or contradictions are corrected.<\/p>\n<h3>4. Data Monitoring:<\/h3>\n<p>The existing and new data should be continuously checked to ensure high data quality on a permanent basis.<\/p>\n<\/div><\/div><\/div><\/div><div class=\"et_pb_row_4 et_pb_row et_block_row\"><div class=\"et_pb_column_4 et_pb_column et_pb_column_4_4 et-last-child et_block_column et_pb_css_mix_blend_mode_passthrough\"><div class=\"et_pb_text_7 et_pb_text et_pb_bg_layout_light et_pb_module et_block_module preset--module--divi-text--default\"><div class=\"et_pb_text_inner\"><h2><span class=\"ez-toc-section\" id=\"Tips_for_data_quality_management\"><\/span>Tips for data quality management:<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<h3>1. Determine responsible persons<\/h3>\n<p>Without someone to take responsibility for data quality, no one may feel responsible for it. Therefore, it is important to define responsible persons. Depending on the data set, these can be different people, but also a single employee. The responsible persons are in charge of ensuring that the defined standards are adhered to when creating the data and that the data is regularly checked and maintained.<\/p>\n<h3>2. Dealing with quality deficiencies<\/h3>\n<p>There is no such thing as 100 per cent data quality, as errors can occur at any time. However, depending on the purpose of the application, it can be determined which data must be absolutely correct in order to be able to carry out correct analyses and thus make correct decisions. <br \/><em>Our tip:<\/em> It is important that as many data sets as possible are correct. However, the cost-benefit ratio of the corrections can be poor, for example if it takes a lot of time to clean the data, but you use the data little afterwards or it has no relevance. Therefore, give priority to addressing quality deficiencies in the essential data.<\/p>\n<h3>3. Improve data quality directly at the source<\/h3>\n<p>Business intelligence solutions such as myPARM BIact allow you to manually change, correct or add to stored data. However, you should bear in mind that when you make such corrections, on the one hand the data source is still flawed and on the other hand manual corrections also have a high potential for errors. In addition, it could be that existing errors are overlooked. Therefore, the quality of the data should be improved at the data source if possible. In this way, high quality data is made available to the BI software.<\/p>\n<h3>4. Continuous data monitoring<\/h3>\n<p>The more often you detect errors, correct them and take measures against them, the higher the quality of your data will be in the future. Nevertheless, it is important to view data quality as an iterative process, since new errors can arise at any time, the requirements for the data can change or the amount of data created and its diversity can increase. The process of data quality management should therefore be continuous.<\/p>\n<p>&nbsp;<\/p>\n<\/div><\/div><\/div><\/div><div class=\"et_pb_row_5 et_pb_row et_block_row\"><div class=\"et_pb_column_5 et_pb_column et_pb_column_4_4 et-last-child et_block_column et_pb_css_mix_blend_mode_passthrough\"><div class=\"et_pb_text_8 et_pb_text et_pb_bg_layout_light et_pb_module et_block_module preset--module--divi-text--default\"><div class=\"et_pb_text_inner\"><h2><span class=\"ez-toc-section\" id=\"Conclusion\"><\/span>Conclusion<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Making decisions based on data instead of gut feeling can contribute a lot to the success of your company. However, with this comes the risk that the data that led to a decision may be flawed. For this reason, it is important that good data quality management ensures that you can always rely on the accuracy of your data.<\/p>\n<\/div><\/div><\/div><\/div><div class=\"et_pb_row_6 et_pb_row et_block_row\"><div class=\"et_pb_column_6 et_pb_column et_pb_column_1_2 et_block_column et_pb_css_mix_blend_mode_passthrough\"><div class=\"et_pb_text_9 et_pb_text et_pb_bg_layout_light et_pb_module et_block_module preset--module--divi-text--default\"><div class=\"et_pb_text_inner\"><p>Weitere Informationen \u00fcber die Business Intelligence Software myPARM BI<span style=\"font-size: x-small;\"><sup>act<\/sup><\/span>:<\/p>\n<\/div><\/div><div class=\"et_pb_module et_pb_button_module_wrapper et_pb_button_0_wrapper preset--module--divi-button--default_wrapper\"><a class=\"et_pb_button_0 et_pb_button et_pb_bg_layout_light et_pb_module et_block_module preset--module--divi-button--default\" href=\"https:\/\/new-site.parm.com\/myparm-bi\/\" target=\"_blank\">Mehr Infos<\/a><\/div><\/div><div class=\"et_pb_column_7 et_pb_column et_pb_column_1_2 et-last-child et_block_column et_pb_css_mix_blend_mode_passthrough\"><div class=\"et_pb_text_10 et_pb_text et_pb_bg_layout_light et_pb_module et_block_module preset--module--divi-text--default\"><div class=\"et_pb_text_inner\"><p>M\u00f6chten Sie\u200b myPARM BI<span style=\"font-size: x-small;\"><sup>act<\/sup><\/span> in einer Demonstration kennenlernen? Dann vereinbaren Sie gleich einen Termin!<\/p>\n<\/div><\/div><div class=\"et_pb_module et_pb_button_module_wrapper et_pb_button_1_wrapper preset--module--divi-button--default_wrapper\"><a class=\"et_pb_button_1 et_pb_button et_pb_bg_layout_light et_pb_module et_block_module preset--module--divi-button--default\" href=\"https:\/\/calendly.com\/parmag\/myparm-biact-webdemo\" target=\"_blank\">Termin vereinbaren<\/a><\/div><\/div><\/div><div class=\"et_pb_row_7 et_pb_row et_block_row\"><div class=\"et_pb_column_8 et_pb_column et_pb_column_4_4 et-last-child et_block_column et_pb_css_mix_blend_mode_passthrough\"><div class=\"et_pb_code_0 et_pb_code et_pb_module\"><div class=\"et_pb_code_inner\"><div class=\"shariff shariff-align-flex-end shariff-widget-align-flex-start\" style=\"display:none\"><ul class=\"shariff-buttons theme-round orientation-horizontal buttonsize-medium\"><li class=\"shariff-button facebook shariff-nocustomcolor\" style=\"background-color:#4273c8\"><a href=\"https:\/\/www.facebook.com\/sharer\/sharer.php?u=https%3A%2F%2Fnew-site.parm.com%2Fen%2Fdata-quality-3%2F\" title=\"Bei Facebook teilen\" aria-label=\"Bei Facebook teilen\" role=\"button\" rel=\"nofollow\" class=\"shariff-link\" style=\"; 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                                \">\n  <div id=\"sib-form-container\" class=\"sib-form-container\">\n    <div id=\"error-message\" class=\"sib-form-message-panel\" style=\"font-size:16px; text-align:left; font-family:\" helvetica=\"\" sans-serif=\"\" color:=\"\" background-color:=\"\" border-radius:3px=\"\" border-color:=\"\">\n      <div class=\"sib-form-message-panel__text sib-form-message-panel__text--center\">\n        <svg viewbox=\"0 0 512 512\" class=\"sib-icon sib-notification__icon\">\n          <path d=\"M256 40c118.621 0 216 96.075 216 216 0 119.291-96.61 216-216 216-119.244 0-216-96.562-216-216 0-119.203 96.602-216 216-216m0-32C119.043 8 8 119.083 8 256c0 136.997 111.043 248 248 248s248-111.003 248-248C504 119.083 392.957 8 256 8zm-11.49 120h22.979c6.823 0 12.274 5.682 11.99 12.5l-7 168c-.268 6.428-5.556 11.5-11.99 11.5h-8.979c-6.433 0-11.722-5.073-11.99-11.5l-7-168c-.283-6.818 5.167-12.5 11.99-12.5zM256 340c-15.464 0-28 12.536-28 28s12.536 28 28 28 28-12.536 28-28-12.536-28-28-28z\"\/>\n        <\/svg>\n        <span class=\"sib-form-message-panel__inner-text\">\n Ihre Anmeldung konnte nicht gespeichert werden. Bitte versuchen Sie es erneut.\n                      <\/span>\n      <\/div>\n    <\/div>\n    <div><\/div>\n    <div id=\"success-message\" class=\"sib-form-message-panel\" style=\"font-size:16px; text-align:left; font-family:\" helvetica=\"\" sans-serif=\"\" color:=\"\" background-color:=\"\" border-radius:3px=\"\" border-color:=\"\">\n      <div class=\"sib-form-message-panel__text sib-form-message-panel__text--center\">\n        <svg viewbox=\"0 0 512 512\" class=\"sib-icon sib-notification__icon\">\n          <path d=\"M256 8C119.033 8 8 119.033 8 256s111.033 248 248 248 248-111.033 248-248S392.967 8 256 8zm0 464c-118.664 0-216-96.055-216-216 0-118.663 96.055-216 216-216 118.664 0 216 96.055 216 216 0 118.663-96.055 216-216 216zm141.63-274.961L217.15 376.071c-4.705 4.667-12.303 4.637-16.97-.068l-85.878-86.572c-4.667-4.705-4.637-12.303.068-16.97l8.52-8.451c4.705-4.667 12.303-4.637 16.97.068l68.976 69.533 163.441-162.13c4.705-4.667 12.303-4.637 16.97.068l8.451 8.52c4.668 4.705 4.637 12.303-.068 16.97z\"\/>\n        <\/svg>\n        <span class=\"sib-form-message-panel__inner-text\">\n Ihre Anmeldung war erfolgreich. Bitte sehen Sie in Ihr Postfach und best\u00e4tigen Sie Ihre Anmeldung. Sollte keine Nachricht ankommen, sehen Sie bitte in Ihren Spam-Ordner. Vielen Dank!\n                      <\/span>\n      <\/div>\n    <\/div>\n    <div><\/div>\n    <div id=\"sib-container\" class=\"sib-container--large sib-container--vertical\" style=\"text-align:center; background-color:rgba(27,62,144,1); max-width:1080px; border-radius:3px; border-width:0px; border-color:#C0CCD9; border-style:solid;\">\n      <form id=\"sib-form\" method=\"POST\" action=\"https:\/\/272a17fd.sibforms.com\/serve\/MUIEAOo1vFGWS7d0cvncbnGo6ZIm0MV83UA-ApqLz0GJgUd38j-GsNKlhCPZ1kVJVEGx5yChU7xjEUZEVAhHli4H01njDJsY0TOLof7HNzi7lZJvWMes1-Je1GVxivY9c8XrMzqbTrnQ9SuHnjhsRe5vXL8U5STdopGqk00vm6RWgZiOpVABZMg1kUY8-KjUWzt-Tpk5-zC9XQ2j\" data-type=\"subscription\">\n        <div style=\"padding: 8px 0;\">\n          <div class=\"sib-form-block\" style=\"font-size:32px; text-align:left; font-weight:700; font-family:\" helvetica=\"\" sans-serif=\"\" color:=\"\" background-color:transparent=\"\">\n            <p>Newsletter<\/p>\n          <\/div>\n        <\/div>\n        <div style=\"padding: 8px 0;\">\n          <div class=\"sib-form-block\" style=\"font-size:16px; text-align:left; font-family:\" helvetica=\"\" sans-serif=\"\" color:=\"\" background-color:transparent=\"\">\n            <div class=\"sib-text-form-block\">\n              <p>Melden Sie sich zu unserem monatlichen Newsletter an und werden Sie \u00fcber Produkte der Parm AG, Neuheiten, Trends im Projektmanagement sowie Angebote und Veranstaltungen informiert.<\/p>\n            <\/div>\n          <\/div>\n        <\/div>\n        <div style=\"padding: 8px 0;\">\n          <div class=\"sib-input sib-form-block\">\n            <div class=\"form__entry entry_block\">\n              <div class=\"form__label-row \">\n\n                <div class=\"entry__field\">\n                  <input class=\"input\" type=\"text\" id=\"EMAIL\" name=\"EMAIL\" autocomplete=\"off\" placeholder=\"EMAIL\" data-required=\"true\" required=\"\">\n                <\/div>\n              <\/div>\n\n              <label class=\"entry__error entry__error--primary\" style=\"font-size:16px; text-align:left; font-family:\" helvetica=\"\" sans-serif=\"\" color:=\"\" background-color:=\"\" border-radius:3px=\"\" border-color:=\"\">\n              <\/label>\n            <\/div>\n          <\/div>\n        <\/div>\n        <div style=\"padding: 8px 0;\">\n          <div class=\"sib-optin sib-form-block\" data-required=\"true\">\n            <div class=\"form__entry entry_mcq\">\n              <div class=\"form__label-row \">\n                <div class=\"entry__choice\">\n                  <label>\n                    <input type=\"checkbox\" class=\"input_replaced\" value=\"1\" id=\"OPT_IN\" name=\"OPT_IN\" required=\"\">\n                    <span class=\"checkbox checkbox_tick_positive\"><\/span><span style=\"font-size:14px; text-align:left; font-family:\" helvetica=\"\" sans-serif=\"\" color:=\"\" background-color:transparent=\"\"><p>Ich bin damit einverstanden, dass der Newsletter individuell auf meine Interessen abgestimmt wird. Zu diesem Zweck gestatte ich der Parm AG mein \u00d6ffnungs-, Klick- und Downloadverhalten im Newsetter zu analysieren und ein personenbezogenes Nutzerprofil von mir zu erstellen. Die <a href=\"https:\/\/new-site.parm.com\/datenschutzerklaerung\" target=\"_blank\" rel=\"noopener\">Datenschutzerkl\u00e4rung <\/a>habe ich gelesen und akzeptiere diese. Die Abmeldung vom Newsletter ist jederzeit m\u00f6glich.<\/p><span data-required=\"*\" style=\"display: inline;\" class=\"entry__label entry__label_optin\"><\/span><\/span> <\/label>\n                <\/div>\n              <\/div>\n              <label class=\"entry__error entry__error--primary\" style=\"font-size:16px; text-align:left; font-family:\" helvetica=\"\" sans-serif=\"\" color:=\"\" background-color:=\"\" border-radius:3px=\"\" border-color:=\"\">\n              <\/label>\n            <\/div>\n          <\/div>\n        <\/div>\n        <div style=\"padding: 8px 0;\">\n          <div class=\"g-recaptcha\" data-sitekey=\"6LdyzNAcAAAAANv3WwEWU18I26AH-2q2CcQ6eQUk\" data-callback=\"invisibleCaptchaCallback\" data-size=\"invisible\" onclick=\"executeCaptcha\"><\/div>\n        <\/div>\n        <div style=\"padding: 8px 0;\">\n          <div class=\"sib-form-block\" style=\"text-align: left\">\n            <button class=\"sib-form-block__button sib-form-block__button-with-loader\" style=\"font-size:16px; text-align:left; font-weight:700; font-family:\" helvetica=\"\" sans-serif=\"\" color:=\"\" background-color:=\"\" border-radius:3px=\"\" border-width:0px=\"\" form=\"sib-form\" type=\"submit\">\n              <svg class=\"icon clickable__icon progress-indicator__icon sib-hide-loader-icon\" viewbox=\"0 0 512 512\">\n                <path d=\"M460.116 373.846l-20.823-12.022c-5.541-3.199-7.54-10.159-4.663-15.874 30.137-59.886 28.343-131.652-5.386-189.946-33.641-58.394-94.896-95.833-161.827-99.676C261.028 55.961 256 50.751 256 44.352V20.309c0-6.904 5.808-12.337 12.703-11.982 83.556 4.306 160.163 50.864 202.11 123.677 42.063 72.696 44.079 162.316 6.031 236.832-3.14 6.148-10.75 8.461-16.728 5.01z\"\/>\n              <\/svg>\n ANMELDEN\n            <\/button>\n          <\/div>\n        <\/div>\n\n        <input type=\"text\" name=\"email_address_check\" value=\"\" class=\"input--hidden\">\n        <input type=\"hidden\" name=\"locale\" value=\"de\">\n      <\/form>\n    <\/div>\n  <\/div>\n<\/div>\n<!-- END - We recommend to place the below code where you want the form in your website html -->\n\n<!-- START - We recommend to place the below code in footer or bottom of your website html -->\n<script>\n  window.REQUIRED_CODE_ERROR_MESSAGE = 'W\u00e4hlen Sie bitte einen L\u00e4ndervorwahl aus.';\n  window.LOCALE = 'de';\n  window.EMAIL_INVALID_MESSAGE = window.SMS_INVALID_MESSAGE = \"Die eingegebenen Informationen sind nicht g\u00fcltig. Bitte \u00fcberpr\u00fcfen Sie die Felder und versuchen Sie es erneut.\";\n\n  window.REQUIRED_ERROR_MESSAGE = \"Dieses Feld darf nicht leer sein. \";\n\n  window.GENERIC_INVALID_MESSAGE = \"Die eingegebenen Informationen sind nicht g\u00fcltig. Bitte \u00fcberpr\u00fcfen Sie die Felder und versuchen Sie es erneut.\";\n\n\n\n\n  window.translation = {\n    common: {\n      selectedList: '{quantity} Liste ausgew\u00e4hlt',\n      selectedLists: '{quantity} Listen ausgew\u00e4hlt'\n    }\n  };\n\n  var AUTOHIDE = Boolean(0);\n<\/script>\n<script src=\"https:\/\/sibforms.com\/forms\/end-form\/build\/main.js\"><\/script>\n\n<script src=\"https:\/\/www.google.com\/recaptcha\/api.js?hl=de\"><\/script>\n\n<!-- END - We recommend to place the above code in footer or bottom of your website html -->\n<!-- End Sendinblue Form --><\/div><\/div><\/div><\/div><\/div>\n","protected":false},"excerpt":{"rendered":"<p>The benefit of business intelligence stands and falls with the quality of the data used. Insights gained from unclean data are also flawed. And decisions made on such a basis can lead to major problems. True to the motto &#8220;garbage in, garbage out.&#8221; High data quality is therefore a decisive success factor for companies. In the latest blog post, Parm AG explains what you can do to ensure the high quality of your data.<\/p>\n","protected":false},"author":1,"featured_media":13593,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[29],"tags":[62,63,522,523],"class_list":["post-13585","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-bi-en","tag-bi","tag-business-intelligence","tag-data-quality","tag-data-quality-management"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.3 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Data Quality | Parm AG<\/title>\n<meta name=\"description\" content=\"The benefit of business intelligence stands and falls with the quality of the data used. Therefore, effective data quality management is important.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/new-site.parm.com\/en\/data-quality-3\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Data Quality\" \/>\n<meta property=\"og:description\" content=\"The benefit of business intelligence stands and falls with the quality of the data used. Insights gained from unclean data are also flawed. And decisions made on such a basis can lead to major problems. True to the motto &quot;garbage in, garbage out.&quot; High data quality is therefore a decisive success factor for companies. In the latest blog post, Parm AG explains what you can do to ensure the high quality of your data.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/new-site.parm.com\/en\/data-quality-3\/\" \/>\n<meta property=\"og:site_name\" content=\"Parm AG\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/myparm\" \/>\n<meta property=\"article:published_time\" content=\"2023-01-05T12:14:53+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2026-03-02T15:08:52+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/new-site.parm.com\/wp-content\/uploads\/2022\/12\/Post_Data_quality.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"2500\" \/>\n\t<meta property=\"og:image:height\" content=\"1313\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"author\" content=\"Natascha Schleutker\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:title\" content=\"Data Quality\" \/>\n<meta name=\"twitter:description\" content=\"The benefit of business intelligence stands and falls with the quality of the data used. Insights gained from unclean data are also flawed. And decisions made on such a basis can lead to major problems. True to the motto &quot;garbage in, garbage out.&quot; High data quality is therefore a decisive success factor for companies. 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