{"id":13585,"date":"2023-01-05T13:03:39","date_gmt":"2023-01-05T12:03:39","guid":{"rendered":"https:\/\/parm.com\/?p=13585"},"modified":"2026-09-15T11:30:28","modified_gmt":"2026-09-15T09:30:28","slug":"data-quality-3","status":"publish","type":"post","link":"https:\/\/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 sound 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 loading=\"lazy\" decoding=\"async\" src=\"https:\/\/parm.com\/wp-content\/uploads\/2022\/12\/Post_Data_quality.jpg\" width=\"2500\" height=\"1313\" srcset=\"https:\/\/parm.com\/wp-content\/uploads\/2022\/12\/Post_Data_quality.jpg 2500w, https:\/\/parm.com\/wp-content\/uploads\/2022\/12\/Post_Data_quality-1280x672.jpg 1280w, https:\/\/parm.com\/wp-content\/uploads\/2022\/12\/Post_Data_quality-980x515.jpg 980w, https:\/\/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\" alt=\"Data Quality\" title=\"Post_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 value of business intelligence stands or falls with the quality of the data used. Insights derived from poor-quality data are flawed. And decisions made on such a basis can lead to major problems. As the saying goes, \u201cgarbage in, garbage out.\u201d High data quality is therefore a critical success factor for companies. Yet although most companies recognize the importance of data quality, the data in many companies is still subpar.<\/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_88 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:\/\/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:\/\/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:\/\/parm.com\/en\/data-quality-3\/#How_can_data_quality_be_determined_The_criteria\" >How can data quality be determined? The criteria<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/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:\/\/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:\/\/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 refers to the overall characteristics of a dataset that enable it to meet user requirements.<br \/>Data quality management refers to all processes and procedures involved in ensuring high data quality. This includes identifying, cleansing, and making data available.<\/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 percent clean and perfect. This may be because data enters a company through various channels. As a result, it may be outdated, duplicated, or inconsistent. To ensure the highest possible data quality, it helps to understand the main causes of data errors. This way, you can prevent poor data quality before it happens. Here are the main causes:<\/p>\n<ul>\n<li><strong>Manual Data Entry:<\/strong> Many companies rely on manual data entry. However, this process is highly prone to errors. Data can be entered in the wrong place or in the wrong format, and typos or transposed digits can easily occur.<\/li>\n<li><strong>Data conversion:<\/strong> When transferring data from one storage location to another, data can be accidentally lost or altered. This can happen, for example, because the data is stored in different formats or the data structure is different.<\/li>\n<li><strong>Real-time updates:<\/strong> To make sound decisions, it is important to work with up-to-date data at all times. However, errors can still occur if individual data records have not yet been updated at the time of analysis or if there has not been enough time to verify the data.<\/li>\n<li><strong>Data Merging:<\/strong> When data needs to be merged\u2014for example, during consolidations, mergers, or system changes\u2014errors such as invalid formats, duplicates, and conflicts can also occur.<\/li>\n<li><strong>System Upgrades:<\/strong> Frequent updates or upgrades to your software can also lead to errors, as data may be deleted or corrupted during the process.<\/li>\n<li><strong>Indiscriminate Data Collection:<\/strong> Companies often collect all the data they generate. This offers some potential, as the data might be needed in the future. However, it also makes quality assurance and data analysis more difficult. Therefore, only the data that is truly needed should be stored whenever possible.<\/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=\"How_can_data_quality_be_determined_The_criteria\"><\/span>How can data quality be determined? The criteria<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 the required data records complete?<\/em><br \/>Incomplete data may be unusable or only partially usable. Therefore, it is essential to ensure that a data record contains all necessary attributes and that those attributes, in turn, contain all necessary data.<\/li>\n<li><strong>Relevance:<\/strong> <em>Is all the data required for the planned purposes available?<\/em><br \/> Not all data collected is relevant to your purposes. Therefore, it should be collected deliberately, so that only as much data as necessary is gathered. This applies particularly to customer data, which is subject to data protection regulations.<\/li>\n<li><strong>Accuracy: <\/strong><em>Is the collected data correct and reported as required?<\/em><br \/>When collecting data, it is important to ensure that the data is accurate. At the same time, it should also be available at the required level of detail. This means, for example, that all necessary decimal places should be recorded.<\/li>\n<li><strong>Timeliness: <\/strong><em>Are the data sets up to date?<\/em><br \/>New data is constantly being generated within a company. Therefore, it makes sense to always perform analyses using current data in order to identify changes or problems early on. In practice, we often recommend that our clients base their decision-making on data from a reliable point in time. Depending on the situation, it may make sense, for example, to use data from the previous day, since live data can change very quickly.<\/li>\n<li><strong>Validity: <\/strong><em>Is the origin of the data reliable, or do the data come from reliable sources?<\/em><br \/>The origin of the data sets should be traceable in order to assess whether the data are 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 scattered across various tools or is not available in the correct format, easy accessibility is not always guaranteed.<\/li>\n<li><strong>Consistency: <\/strong><em>Are there any contradictions or duplicates in the data? Are there any discrepancies with other data?<\/em><br \/>Data must be unique, free of contradictions with itself or other data, free of redundancies, and structured consistently.<\/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>To ensure that data consistently maintains a high level of quality, the first step is to define how its quality can be measured. The data should then be analyzed, cleaned, and monitored based on the defined criteria. This process should be carried out regularly to maintain consistently high data quality and 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 data quality. For example, you\u2019ll define what data must be available for your purposes and in what format it should be provided.<\/p>\n<h3>2. Data profiling \/ data analysis:<\/h3>\n<p>Data analysis is used to identify duplicate data, inconsistencies, errors, and incomplete information. This allows the quality of the data to be measured and, in subsequent steps, the data to be cleaned and updated. In addition, data analysis can be used to identify sources of error and thus take measures to ensure that the identified errors do not occur again in the future.<\/p>\n<h3>3. Data Cleaning<\/h3>\n<p>The Data Cleaning step resolves the issues identified during data analysis. This means that duplicates are deleted, incomplete data is filled in, and inconsistencies 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 taking responsibility for data quality, no one may feel accountable for it. That is why it is important to designate responsible parties. Depending on the dataset, these may be different people, or they may be a single employee. Those responsible are tasked with ensuring that the defined standards are followed when creating the data and that the data is regularly reviewed and maintained.<\/p>\n<h3>2. Dealing with quality deficiencies<\/h3>\n<p>There is no such thing as 100 percent data quality, since errors can occur at any time. Depending on the intended use, however, it is possible to determine which data must be accurate in order to perform correct analyses and thus make the right decisions. <br \/><em>Our tip:<\/em> While it is important that as many data records as possible are accurate, However, the cost-benefit ratio of making corrections may be unfavorable\u2014for example, if cleaning the data takes a lot of time but you end up using the data very little afterward or it has no relevance. Therefore, prioritize addressing quality issues 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 modify, correct, or supplement stored data. However, you should keep in mind that, on the one hand, the data source remains incorrect even after such corrections, and on the other hand, manual corrections also carry a high risk of error. Furthermore, existing errors might be overlooked. Therefore, the quality of the data should ideally be improved at the data source itself. This ensures that high-quality data is made available to the BI software.<\/p>\n<h3>4. Continuous data monitoring<\/h3>\n<p>The more often you identify errors, correct them, and take action to address 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, data requirements can change, or the volume and diversity of data can increase. The data quality management process should therefore be carried out on an ongoing basis.<\/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 rather than gut feelings can greatly contribute to your company\u2019s success. However, this comes with the risk that the data leading to a decision may be inaccurate. For this reason, it is important to have a robust data quality management system in place to ensure 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>Learn more about the Business Intelligence Software 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--group--divi-button--divi-button--hi917qs--default_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--group--divi-button--divi-button--hi917qs--default preset--module--divi-button--default\" href=\"https:\/\/parm.com\/myparm-bi\/\" target=\"_blank\">More info<\/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>Would you like to get to know myPARM BI<span style=\"font-size: x-small;\"><sup>act<\/sup><\/span> in a demo presentation? Then make an appointment right away!<\/p>\n<\/div><\/div><div class=\"et_pb_module et_pb_button_module_wrapper et_pb_button_1_wrapper preset--group--divi-button--divi-button--hi917qs--default_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--group--divi-button--divi-button--hi917qs--default preset--module--divi-button--default\" href=\"https:\/\/calendly.com\/parmag\/myparm-biact-webdemo\" target=\"_blank\">Book meeting<\/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-left shariff-widget-align-left\" 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%2Fparm.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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background-color: transparent;  \">\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\"><\/path>\n        <\/svg>\n        <span class=\"sib-form-message-panel__inner-text\">\n  Your registration could not be saved. 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Please check your mailbox and confirm your registration. If you do not receive a message, please check your spam folder. Thank you very much!\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>Sign up for our monthly newsletter to stay informed about Parm AG products, new releases, trends in project management, as well as special offers and events.<\/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>I agree to the newsletter being tailored to my individual interests. For this purpose, I allow Parm AG to analyze my opening, clicking and downloading behavior in the newsetter and to create a personal user profile of me. I have read the <a href=\"https:\/\/parm.com\/en\/privacy-policy\/\" target=\"_blank\" rel=\"noopener\">privacy policy <\/a>and accept it. I can unsubscribe from the newsletter at any time.<\/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\"><\/path>\n              <\/svg>\n  REGISTER\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 value of business intelligence stands or falls with the quality of the data used. Insights derived from inaccurate data are also flawed. And decisions made on such a basis can lead to major problems. As the saying goes, \u201cgarbage in, garbage out.\u201d High data quality is therefore a crucial success factor for companies. In its latest blog post, Parm AG explains what you can do to ensure high data quality.<\/p>\n","protected":false},"author":1,"featured_media":13593,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[29],"tags":[64,65,533,534],"class_list":["post-13585","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-bi-en","tag-bi-en","tag-business-intelligence-en","tag-data-quality-en","tag-data-quality-management-en"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.5 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Data Quality | Parm AG<\/title>\n<meta name=\"description\" content=\"The value of business intelligence stands or falls with the quality of the data used. That is why 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:\/\/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 value of business intelligence stands or falls with the quality of the data used. Insights derived from inaccurate data are also flawed. And decisions made on such a basis can lead to major problems. As the saying goes, \u201cgarbage in, garbage out.\u201d High data quality is therefore a crucial success factor for companies. In its latest blog post, Parm AG explains what you can do to ensure your data is of high quality.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/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=\"2023-01-05T12:03:39+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2026-09-15T09:30:28+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/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 value of business intelligence stands or falls with the quality of the data used. Insights derived from inaccurate data are also flawed. And decisions made on such a basis can lead to major problems. As the saying goes, \u201cgarbage in, garbage out.\u201d High data quality is therefore a crucial success factor for companies. 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