{"id":99836,"date":"2026-03-11T11:15:41","date_gmt":"2026-03-11T10:15:41","guid":{"rendered":"https:\/\/www.efcom.de\/data-quality-crucial-to-success-or-failure\/"},"modified":"2026-03-11T11:29:07","modified_gmt":"2026-03-11T10:29:07","slug":"data-quality-crucial-to-success-or-failure","status":"publish","type":"post","link":"https:\/\/www.efcom.de\/en\/data-quality-crucial-to-success-or-failure\/","title":{"rendered":"Data quality \u2013 crucial to success or failure"},"content":{"rendered":"<div class=\"wpb-content-wrapper\"><p>[vc_row css=&#8221;.vc_custom_1708334309580{margin-top: 45px !important;}&#8221;][vc_column width=&#8221;1\/5&#8243;][\/vc_column][vc_column width=&#8221;3\/5&#8243;]<div id=\"ultimate-heading-89946a095543603b6\" class=\"uvc-heading ult-adjust-bottom-margin ultimate-heading-89946a095543603b6 uvc-6085 \" data-hspacer=\"no_spacer\"  data-halign=\"center\" style=\"text-align:center\"><div class=\"uvc-heading-spacer no_spacer\" style=\"top\"><\/div><div class=\"uvc-main-heading ult-responsive\"  data-ultimate-target='.uvc-heading.ultimate-heading-89946a095543603b6 h1'  data-responsive-json-new='{\"font-size\":\"desktop:44px;\",\"line-height\":\"desktop:65px;\"}' ><h1 style=\"font-family:&#039;Roboto Condensed&#039;;font-weight:300;color:#eb9000;margin-bottom:30px;\">Article<\/h1><\/div><\/div><div id=\"ultimate-heading-27206a0955436047f\" class=\"uvc-heading ult-adjust-bottom-margin ultimate-heading-27206a0955436047f uvc-5453 \" data-hspacer=\"no_spacer\"  data-halign=\"center\" style=\"text-align:center\"><div class=\"uvc-heading-spacer no_spacer\" style=\"top\"><\/div><div class=\"uvc-main-heading ult-responsive\"  data-ultimate-target='.uvc-heading.ultimate-heading-27206a0955436047f h1'  data-responsive-json-new='{\"font-size\":\"desktop:50px;\",\"line-height\":\"desktop:65px;\"}' ><h1 style=\"font-family:&#039;Roboto Condensed&#039;;font-weight:700;\">Data quality \u2013 crucial to success or failure<\/h1><\/div><\/div>[vc_single_image image=&#8221;99835&#8243; img_size=&#8221;large&#8221; alignment=&#8221;center&#8221; css=&#8221;.vc_custom_1773224661604{padding-top: 30px !important;}&#8221;][\/vc_column][vc_column width=&#8221;1\/5&#8243;][\/vc_column][\/vc_row][vc_row][vc_column width=&#8221;1\/5&#8243;][\/vc_column][vc_column width=&#8221;2\/5&#8243;][\/vc_column][vc_column width=&#8221;1\/5&#8243;]<div class=\"ult-content-box-container \" >\t\t<div class=\"ult-content-box\" style=\"box-shadow: px px px px none;-webkit-transition: all 700ms ease;-moz-transition: all 700ms ease;-ms-transition: all 700ms ease;-o-transition: all 700ms ease;transition: all 700ms ease;\"  data-hover_box_shadow=\"none\"    ><style type=\"text\/css\" data-type=\"the7_shortcodes-inline-css\">.dt-shortcode-soc-icons.soc-icons-deb4b65d0fb787b768125036b35fa523 a {\n  margin-right: 10px;\n}\n.dt-shortcode-soc-icons a.soc-icons-deb4b65d0fb787b768125036b35fa523 {\n  margin-right: 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a.single-soc-icon-15b988045b57f6709eca704584b326f8:hover .soc-icon,\n#phantom .dt-shortcode-soc-icons a.single-soc-icon-15b988045b57f6709eca704584b326f8:hover .soc-icon {\n  color: #ffffff;\n  background: none;\n}\n#page .dt-shortcode-soc-icons a.single-soc-icon-15b988045b57f6709eca704584b326f8.dt-icon-hover-bg-on:after,\n#phantom .dt-shortcode-soc-icons a.single-soc-icon-15b988045b57f6709eca704584b326f8.dt-icon-hover-bg-on:after {\n  background: rgba(0,0,0,0.5);\n}\n#page .dt-shortcode-soc-icons a.single-soc-icon-15b988045b57f6709eca704584b326f8:not(:hover) .soc-font-icon,\n#phantom .dt-shortcode-soc-icons a.single-soc-icon-15b988045b57f6709eca704584b326f8:not(:hover) .soc-font-icon,\n#page .dt-shortcode-soc-icons a.single-soc-icon-15b988045b57f6709eca704584b326f8:not(:hover) .soc-icon,\n#phantom .dt-shortcode-soc-icons a.single-soc-icon-15b988045b57f6709eca704584b326f8:not(:hover) .soc-icon {\n  color: #ffffff;\n  background: none;\n}\n.dt-shortcode-soc-icons a.single-soc-icon-15b988045b57f6709eca704584b326f8 .soc-font-icon,\n.dt-shortcode-soc-icons a.single-soc-icon-15b988045b57f6709eca704584b326f8 .soc-icon {\n  font-size: 18px;\n}<\/style><a title=\"Mail\" href=\"mailto: sales@efcom.de\" class=\"mail single-soc-icon-15b988045b57f6709eca704584b326f8 dt-icon-bg-on dt-icon-hover-bg-on\"><i class=\"soc-icon soc-font-icon dt-icon-mail\"><\/i><span class=\"screen-reader-text\">Mail<\/span><\/a><\/div>\t\t<\/div><\/div>[\/vc_column][vc_column width=&#8221;1\/5&#8243;][\/vc_column][\/vc_row][vc_row css=&#8221;.vc_custom_1771255716804{padding-top: 30px !important;}&#8221;][vc_column width=&#8221;1\/5&#8243;][\/vc_column][vc_column width=&#8221;3\/5&#8243;][vc_column_text css=&#8221;.vc_custom_1773224816983{padding-top: 20px !important;padding-bottom: 10px !important;}&#8221;]<span style=\"font-size: 15pt; line-height: 2.0;\">Data quality is not only crucial in factoring. Regardless of the systems or processing procedures involved \u2013 whether traditional <strong>rule-based<\/strong> or <strong>AI-driven<\/strong> \u2013 those who tolerate poor data quality should not complain about modest results. If we reverse the \u2018garbage in, garbage out\u2019 rule to \u2018<strong>quality in, quality out\u2019<\/strong>, factoring and supply chain finance providers in particular will benefit significantly \u2013 in several ways. In this short article, we would like to discuss what is generally meant by \u2018data quality\u2019, the advantages of good data quality, what to look out for in your own data management, and why a good database will be of crucial importance, especially in AI-driven analysis. <\/span><\/p>\n<p>&nbsp;<\/p>\n<p><span style=\"font-size: 15pt; line-height: 2.0;\">Let&#8217;s take a few steps back: What exactly is meant by <strong>data quality<\/strong>? Essentially, it refers to the following factors: the completeness of the data, its accuracy, timeliness, consistency and traceability.<sup>1,2<\/sup> The following overview lists some details.<\/span><\/p>\n<p>&nbsp;<\/p>\n<ul>\n<li><span style=\"font-size: 15pt; line-height: 2.0;\"><strong>Completeness: <\/strong>All relevant information about the claim must be available, such as debtor data (legal form, address, credit rating), payment terms, proof of delivery, invoice references, etc.<\/span><\/li>\n<li><span style=\"font-size: 15pt; line-height: 2.0;\"><strong>Accuracy: <\/strong>This means that there are no invoicing errors, the amounts\/VAT\/payment terms are correct and there are no duplicates.<\/span><\/li>\n<li><span style=\"font-size: 15pt; line-height: 2.0;\"><strong>Up-to-date: <\/strong>All creditworthiness information and limits should be up to date..<\/span><\/li>\n<li><span style=\"font-size: 15pt; line-height: 2.0;\"><strong>Consistency: <\/strong>Is the data consistent across different systems (core banking, risk systems, reporting)?<\/span><\/li>\n<li><span style=\"font-size: 15pt; line-height: 2.0;\"><strong>Traceability: <\/strong>Data origin, change history and processing steps are transparently documented and can be reconstructed at any time.<sup>1,2<\/sup><\/span><\/li>\n<\/ul>\n<p>[\/vc_column_text][\/vc_column][vc_column width=&#8221;1\/5&#8243;][\/vc_column][\/vc_row][vc_row css=&#8221;.vc_custom_1771254929366{padding-top: 10px !important;}&#8221;][vc_column width=&#8221;1\/5&#8243;][\/vc_column][vc_column width=&#8221;3\/5&#8243;]<div id=\"ultimate-heading-90416a09554360d59\" class=\"uvc-heading ult-adjust-bottom-margin ultimate-heading-90416a09554360d59 uvc-3505  uvc-heading-default-font-sizes\" data-hspacer=\"no_spacer\"  data-halign=\"left\" style=\"text-align:left\"><div class=\"uvc-heading-spacer no_spacer\" style=\"top\"><\/div><div class=\"uvc-main-heading ult-responsive\"  data-ultimate-target='.uvc-heading.ultimate-heading-90416a09554360d59 h3'  data-responsive-json-new='{\"font-size\":\"\",\"line-height\":\"\"}' ><h3 style=\"--font-weight:theme;\">The benefits of good data quality<\/h3><\/div><\/div>[\/vc_column][vc_column width=&#8221;1\/5&#8243;][\/vc_column][\/vc_row][vc_row css=&#8221;.vc_custom_1771254946826{padding-top: 10px !important;}&#8221;][vc_column width=&#8221;1\/5&#8243;][\/vc_column][vc_column width=&#8221;3\/5&#8243;][vc_column_text css=&#8221;.vc_custom_1773159111158{padding-top: 20px !important;padding-bottom: 10px !important;}&#8221;]<span style=\"font-size: 15pt; line-height: 2.0;\">So far, so good. What <strong>advantages<\/strong> does good data quality offer from the perspective of factoring or supply chain finance providers? Specifically, these include improved risk management (lower default risks, better liquidity forecasts), greater operational efficiency (less manual follow-up, better automation) and, last but not least, fewer conflicts with regulatory requirements (compliance, DORA, etc.). In short, data quality has a direct impact on a company&#8217;s own <strong>risk<\/strong>, <strong>profitability<\/strong>, <strong>regulatory security<\/strong> and <strong>competitiveness<\/strong>. <\/span><\/p>\n<p><span style=\"font-size: 15pt; line-height: 2.0;\">If we look at risk analysis by a factoring provider, for example, poor data quality can have serious consequences \u2013 ranging from financial ruin to criminal prosecution. Furthermore, in the financial sector, high-quality data is the basis for <strong>security strategies<\/strong> and effective <strong>compliance<\/strong>.<sup>3<\/sup> If that&#8217;s not enough of an argument, according to Gartner, companies lose up to several million US dollars annually due to poor data quality.<sup>4<\/sup> So what can be done to ensure the best possible data quality within an entire company? What would be the path from a purely reactive approach (firefighting) to proactive data management?<\/span>[\/vc_column_text][\/vc_column][vc_column width=&#8221;1\/5&#8243;][\/vc_column][\/vc_row][vc_row css=&#8221;.vc_custom_1771254968554{padding-top: 10px !important;}&#8221;][vc_column width=&#8221;1\/5&#8243;][\/vc_column][vc_column width=&#8221;3\/5&#8243;]<div id=\"ultimate-heading-95016a09554360ddc\" class=\"uvc-heading ult-adjust-bottom-margin ultimate-heading-95016a09554360ddc uvc-7278  uvc-heading-default-font-sizes\" data-hspacer=\"no_spacer\"  data-halign=\"left\" style=\"text-align:left\"><div class=\"uvc-heading-spacer no_spacer\" style=\"top\"><\/div><div class=\"uvc-main-heading ult-responsive\"  data-ultimate-target='.uvc-heading.ultimate-heading-95016a09554360ddc h3'  data-responsive-json-new='{\"font-size\":\"\",\"line-height\":\"\"}' ><h3 style=\"--font-weight:theme;\">Consider data as an asset<\/h3><\/div><\/div>[\/vc_column][vc_column width=&#8221;1\/5&#8243;][\/vc_column][\/vc_row][vc_row css=&#8221;.vc_custom_1771254975067{padding-top: 10px !important;}&#8221;][vc_column width=&#8221;1\/5&#8243;][\/vc_column][vc_column width=&#8221;3\/5&#8243;][vc_column_text css=&#8221;.vc_custom_1773159454550{padding-top: 20px !important;padding-bottom: 10px !important;}&#8221;]<span style=\"font-size: 15pt; line-height: 2.0;\">One possible solution is to establish a <strong>\u2018Data Centre of Excellence\u2019<\/strong>. This is a centralised function that defines company-wide standards, governance and best practices for data management. Such a \u2018Data Centre of Excellence\u2019 acts as a kind of regulatory authority for all data-related initiatives and ensures that data is treated as a <strong>strategic asset<\/strong>. It is therefore a process that helps companies move from fragmented data management to a <strong>structured, scalable approach<\/strong>. This is what enables advanced analytics, AI and digital transformation.<sup>5<\/sup> In this context, data is also referred to as the <strong>\u2018single source of truth\u2019<\/strong> (SSOT).<\/span>[\/vc_column_text][\/vc_column][vc_column width=&#8221;1\/5&#8243;][\/vc_column][\/vc_row][vc_row css=&#8221;.vc_custom_1771254996817{padding-top: 10px !important;}&#8221;][vc_column width=&#8221;1\/5&#8243;][\/vc_column][vc_column width=&#8221;3\/5&#8243;]<div id=\"ultimate-heading-99886a09554360e34\" class=\"uvc-heading ult-adjust-bottom-margin ultimate-heading-99886a09554360e34 uvc-2679  uvc-heading-default-font-sizes\" data-hspacer=\"no_spacer\"  data-halign=\"left\" style=\"text-align:left\"><div class=\"uvc-heading-spacer no_spacer\" style=\"top\"><\/div><div class=\"uvc-main-heading ult-responsive\"  data-ultimate-target='.uvc-heading.ultimate-heading-99886a09554360e34 h3'  data-responsive-json-new='{\"font-size\":\"\",\"line-height\":\"\"}' ><h3 style=\"--font-weight:theme;\">SMEs: Using scarce resources effectively<\/h3><\/div><\/div>[\/vc_column][vc_column width=&#8221;1\/5&#8243;][\/vc_column][\/vc_row][vc_row css=&#8221;.vc_custom_1771255004785{padding-top: 10px !important;}&#8221;][vc_column width=&#8221;1\/5&#8243;][\/vc_column][vc_column width=&#8221;3\/5&#8243;][vc_column_text css=&#8221;.vc_custom_1773159422191{padding-top: 20px !important;padding-bottom: 10px !important;}&#8221;]<span style=\"font-size: 15pt; line-height: 2.0;\">For SMEs, which have to manage with more <strong>limited resources<\/strong>, careful planning and implementation of a \u2018Data Centre of Excellence\u2019 is all the more important. First, clear objectives must be formulated: What are the <strong>short- and long-term goals<\/strong> in terms of data quality, how will their achievement be measured in concrete terms, and are certain milestones planned? Next, the roles must be assigned internally: Who will lead the project, who will be responsible for data analysis? The next step is to define the <strong>data standards<\/strong> (What do we want? What do we need to comply with?) and data-related internal processes must be defined. Ultimately, it is a matter of finding <strong>suitable tools<\/strong> for collaboration between individual team members and for the actual data management, including analysis and visualisation.<sup>6<\/sup> There are a number of tool operators who also offer their services to smaller organisations with correspondingly lower data volumes.<br \/>\n<\/span>[\/vc_column_text][\/vc_column][vc_column width=&#8221;1\/5&#8243;][\/vc_column][\/vc_row][vc_row css=&#8221;.vc_custom_1771255011634{padding-top: 10px !important;}&#8221;][vc_column width=&#8221;1\/5&#8243;][\/vc_column][vc_column width=&#8221;3\/5&#8243;]<div id=\"ultimate-heading-43266a09554360e94\" class=\"uvc-heading ult-adjust-bottom-margin ultimate-heading-43266a09554360e94 uvc-2622  uvc-heading-default-font-sizes\" data-hspacer=\"no_spacer\"  data-halign=\"left\" style=\"text-align:left\"><div class=\"uvc-heading-spacer no_spacer\" style=\"top\"><\/div><div class=\"uvc-main-heading ult-responsive\"  data-ultimate-target='.uvc-heading.ultimate-heading-43266a09554360e94 h3'  data-responsive-json-new='{\"font-size\":\"\",\"line-height\":\"\"}' ><h3 style=\"--font-weight:theme;\">Data quality and AI<\/h3><\/div><\/div>[\/vc_column][vc_column width=&#8221;1\/5&#8243;][\/vc_column][\/vc_row][vc_row css=&#8221;.vc_custom_1771255017418{padding-top: 10px !important;}&#8221;][vc_column width=&#8221;1\/5&#8243;][\/vc_column][vc_column width=&#8221;3\/5&#8243;][vc_column_text css=&#8221;.vc_custom_1773224800557{padding-top: 20px !important;padding-bottom: 10px !important;}&#8221;]<span style=\"font-size: 15pt; line-height: 2.0;\">In this context, let us now consider the ever-increasing use of AI. Whether data is available and its quality play an increasingly important role in AI-controlled systems. This is because when <strong>AI agents<\/strong> are used, they act largely autonomously on the basis of the data and instructions they receive. In such a scenario, there is deliberately no longer <strong>any human control authority<\/strong> to detect errors, inconsistencies or deviations after a decision has been made. Consequently, poor data quality in an agent-based model can even lead to systemic risk, as errors are amplified by machines.<sup>7<\/sup> <\/span><\/p>\n<p>&nbsp;<\/p>\n<p><span style=\"font-size: 15pt; line-height: 2.0;\">One thing is certain: the <strong>future of data management<\/strong> lies in \u2018autonomous data quality management,\u2019 where <strong>AI-driven tools<\/strong> proactively manage cleansing, enrichment, and monitoring with minimal manual oversight. These systems use <strong>machine learning (ML)<\/strong> to identify complex patterns that could indicate fraud or data corruption before they impact business results.<sup>8<\/sup> Such automated \u2018decision engines\u2019 require high-quality, consistent and rich data \u2013 otherwise these technologies will not work reliably. In other words, if you rely on low-quality data, a model&#8217;s attention will inevitably be drawn to the wrong characteristics. That is why the so-called <strong>data-centric approach<\/strong> prioritises quality over quantity.<sup>9<\/sup> This brings us back to the concept of data as SSOT, which is essentially about decoupling data and logic. <\/span>[\/vc_column_text][\/vc_column][vc_column width=&#8221;1\/5&#8243;][\/vc_column][\/vc_row][vc_row css=&#8221;.vc_custom_1771255011634{padding-top: 10px !important;}&#8221;][vc_column width=&#8221;1\/5&#8243;][\/vc_column][vc_column width=&#8221;3\/5&#8243;]<div id=\"ultimate-heading-53006a09554360eea\" class=\"uvc-heading ult-adjust-bottom-margin ultimate-heading-53006a09554360eea uvc-7489  uvc-heading-default-font-sizes\" data-hspacer=\"no_spacer\"  data-halign=\"left\" style=\"text-align:left\"><div class=\"uvc-heading-spacer no_spacer\" style=\"top\"><\/div><div class=\"uvc-main-heading ult-responsive\"  data-ultimate-target='.uvc-heading.ultimate-heading-53006a09554360eea h3'  data-responsive-json-new='{\"font-size\":\"\",\"line-height\":\"\"}' ><h3 style=\"--font-weight:theme;\">Important: Establish a data-first culture<\/h3><\/div><\/div>[\/vc_column][vc_column width=&#8221;1\/5&#8243;][\/vc_column][\/vc_row][vc_row css=&#8221;.vc_custom_1771255017418{padding-top: 10px !important;}&#8221;][vc_column width=&#8221;1\/5&#8243;][\/vc_column][vc_column width=&#8221;3\/5&#8243;][vc_column_text css=&#8221;.vc_custom_1773224790198{padding-top: 20px !important;padding-bottom: 10px !important;}&#8221;]<span style=\"font-size: 15pt; line-height: 2.0;\">Ultimately, technical tools and frameworks are only as effective as the culture that supports them. Data quality is a corporate discipline and not just an IT task. It is therefore crucial to establish a data-first culture within the company. However, the transition to such a culture can meet with internal resistance. It is therefore necessary to clearly <strong>communicate the benefits<\/strong> and <strong>offer training<\/strong> to involve all employees in the change process.<sup>10<\/sup> It is important to implement data processes that are <strong>accessible<\/strong> and <strong>understandable<\/strong> to everyone involved. Otherwise, this will lead to frustration among employees and the entire system will quickly falter.<sup>11<\/sup> <\/span><\/p>\n<p>&nbsp;<\/p>\n<p><span style=\"font-size: 15pt; line-height: 2.0;\">Furthermore, the success of data-driven initiatives must also be <strong>measurable<\/strong> and adaptable as needed. It should be possible to identify obstacles and implement improvements when using data across different teams.<sup>12<\/sup> Otherwise, there is a risk that, after an initial phase of euphoria, the employees responsible will quickly lose track of the big picture and subsequently lose interest in driving the project forward. Last but not least, <strong>collaboration<\/strong> between individual departments should be encouraged. When teams work together on shared data, it promotes a sense of collective responsibility and reinforces the cultural importance of data orientation. Introducing and maintaining a data-oriented culture should therefore be viewed as a marathon, not a sprint.<sup>12<\/sup> <\/span>[\/vc_column_text][\/vc_column][vc_column width=&#8221;1\/5&#8243;][\/vc_column][\/vc_row][vc_row css=&#8221;.vc_custom_1771255025603{padding-top: 10px !important;}&#8221;][vc_column width=&#8221;1\/5&#8243;][\/vc_column][vc_column width=&#8221;3\/5&#8243;]<div id=\"ultimate-heading-35966a09554360f56\" class=\"uvc-heading ult-adjust-bottom-margin ultimate-heading-35966a09554360f56 uvc-242  uvc-heading-default-font-sizes\" data-hspacer=\"no_spacer\"  data-halign=\"left\" style=\"text-align:left\"><div class=\"uvc-heading-spacer no_spacer\" style=\"top\"><\/div><div class=\"uvc-main-heading ult-responsive\"  data-ultimate-target='.uvc-heading.ultimate-heading-35966a09554360f56 h3'  data-responsive-json-new='{\"font-size\":\"\",\"line-height\":\"\"}' ><h3 style=\"--font-weight:theme;\">Conclusion<\/h3><\/div><\/div>[\/vc_column][vc_column width=&#8221;1\/5&#8243;][\/vc_column][\/vc_row][vc_row css=&#8221;.vc_custom_1771255032306{padding-top: 10px !important;}&#8221;][vc_column width=&#8221;1\/5&#8243;][\/vc_column][vc_column width=&#8221;3\/5&#8243;][vc_column_text css=&#8221;.vc_custom_1773224334696{padding-top: 20px !important;padding-bottom: 10px !important;}&#8221;]<span style=\"font-size: 15pt; line-height: 2.0;\">Data quality is playing an increasingly important role and is even one of the decisive factors in the success or failure of AI-driven systems. It is therefore advisable for forward-looking companies to take a systematic approach to their own data quality management and invest in clear standards and tools. A sustainable data-oriented culture provides the basis for coping with change and promoting innovation. Companies that follow this approach will not only survive in a data-rich future, but also thrive.<sup>12<\/sup><\/span>[\/vc_column_text][\/vc_column][vc_column width=&#8221;1\/5&#8243;][\/vc_column][\/vc_row][vc_row css=&#8221;.vc_custom_1771255032306{padding-top: 10px !important;}&#8221;][vc_column width=&#8221;1\/5&#8243;][\/vc_column][vc_column width=&#8221;3\/5&#8243;][vc_column_text css=&#8221;.vc_custom_1773160018369{padding-top: 20px !important;padding-bottom: 10px !important;}&#8221;]<span style=\"font-size: 15pt; line-height: 2.0;\">Sources:<\/span><\/p>\n<p><span style=\"font-size: 15pt; line-height: 2.0;\"><sup>1<\/sup> Monte Carlo Data, 2025: The 6 Data Quality Dimensions (Plus 1 You Can\u2019t Ignore) With Examples<\/span><\/p>\n<p><span style=\"font-size: 15pt; line-height: 2.0;\"><sup>2<\/sup> IBM: Was ist Datenqualit\u00e4t?<\/span><\/p>\n<p><span style=\"font-size: 15pt; line-height: 2.0;\"><sup>3<\/sup> Data Ladder, 2026: Why Data Quality Is the Foundation of Effective Compliance in Security and Investigations?<\/span><\/p>\n<p><span style=\"font-size: 15pt; line-height: 2.0;\"><sup>4<\/sup> Gartner: Data Quality: Best Practices for Accurate Insights<\/span><\/p>\n<p><span style=\"font-size: 15pt; line-height: 2.0;\"><sup>5 <\/sup>lake FS, 2026: Building a Data Center of Excellence for Modern Data Teams<\/span><\/p>\n<p><span style=\"font-size: 15pt; line-height: 2.0;\"><sup>6<\/sup> Revolent: 5 steps to building your Data Center of Excellence<\/span><\/p>\n<p><span style=\"font-size: 15pt; line-height: 2.0;\"><sup>7<\/sup> The Forrester Wave\u2122: Data Quality Solutions, 2026: How to read it and why it matters now<\/span><\/p>\n<p><span style=\"font-size: 15pt; line-height: 2.0;\"><sup>8<\/sup> delpha, 2025: The Ultimate Guide to Data Quality and AI in CRM: Unlocking Maximum Performance<\/span><\/p>\n<p><span style=\"font-size: 15pt; line-height: 2.0;\"><sup>9<\/sup> Human First, 2021: The Importance of High-Quality Training Data<\/span><\/p>\n<p><span style=\"font-size: 15pt; line-height: 2.0;\"><sup>10<\/sup> statworx, 2024: Wie F\u00fchrungskr\u00e4fte die Datenkultur im Unternehmen st\u00e4rken k\u00f6nnen<\/span><\/p>\n<p><span style=\"font-size: 15pt; line-height: 2.0;\"><sup>11 <\/sup>datacoves: 3 Core Pillars to Achieve a Data-Driven Culture<\/span><\/p>\n<p><span style=\"font-size: 15pt; line-height: 2.0;\"><sup>12<\/sup> James Fee, 2024: Sustaining a Data-First Culture: Turning Principles into Long-Term Success<\/span>[\/vc_column_text][\/vc_column][vc_column width=&#8221;1\/5&#8243;][\/vc_column][\/vc_row]<\/p>\n<\/div>","protected":false},"excerpt":{"rendered":"<p>[vc_row css=&#8221;.vc_custom_1708334309580{margin-top: 45px !important;}&#8221;][vc_column width=&#8221;1\/5&#8243;][\/vc_column][vc_column width=&#8221;3\/5&#8243;][vc_single_image image=&#8221;99835&#8243; img_size=&#8221;large&#8221; alignment=&#8221;center&#8221; css=&#8221;.vc_custom_1773224661604{padding-top: 30px !important;}&#8221;][\/vc_column][vc_column width=&#8221;1\/5&#8243;][\/vc_column][\/vc_row][vc_row][vc_column width=&#8221;1\/5&#8243;][\/vc_column][vc_column width=&#8221;2\/5&#8243;][\/vc_column][vc_column width=&#8221;1\/5&#8243;][\/vc_column][vc_column width=&#8221;1\/5&#8243;][\/vc_column][\/vc_row][vc_row css=&#8221;.vc_custom_1771255716804{padding-top: 30px !important;}&#8221;][vc_column width=&#8221;1\/5&#8243;][\/vc_column][vc_column width=&#8221;3\/5&#8243;][vc_column_text css=&#8221;.vc_custom_1773224816983{padding-top: 20px !important;padding-bottom: 10px !important;}&#8221;]Data quality is not only crucial in factoring. Regardless of the systems or processing procedures involved \u2013 whether traditional rule-based or AI-driven \u2013 those who tolerate poor data quality&hellip;<\/p>\n","protected":false},"author":5,"featured_media":99835,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"yasr_overall_rating":0,"yasr_post_is_review":"","yasr_auto_insert_disabled":"","yasr_review_type":"","footnotes":""},"categories":[145],"tags":[37,36,47,304],"class_list":["post-99836","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-artikel-en","tag-efcom","tag-factoring","tag-innovation","tag-symposium","category-145","description-off"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.2 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Data quality \u2013 crucial to success or failure - efcom gmbh | Proven and efficient standard software for factoring institutes<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.efcom.de\/en\/data-quality-crucial-to-success-or-failure\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Data quality \u2013 crucial to success or failure - efcom gmbh | Proven and efficient standard software for factoring institutes\" \/>\n<meta property=\"og:description\" content=\"[vc_row css=&#8221;.vc_custom_1708334309580{margin-top: 45px !important;}&#8221;][vc_column width=&#8221;1\/5&#8243;][\/vc_column][vc_column width=&#8221;3\/5&#8243;][vc_single_image image=&#8221;99835&#8243; img_size=&#8221;large&#8221; alignment=&#8221;center&#8221; css=&#8221;.vc_custom_1773224661604{padding-top: 30px !important;}&#8221;][\/vc_column][vc_column width=&#8221;1\/5&#8243;][\/vc_column][\/vc_row][vc_row][vc_column width=&#8221;1\/5&#8243;][\/vc_column][vc_column width=&#8221;2\/5&#8243;][\/vc_column][vc_column width=&#8221;1\/5&#8243;][\/vc_column][vc_column width=&#8221;1\/5&#8243;][\/vc_column][\/vc_row][vc_row css=&#8221;.vc_custom_1771255716804{padding-top: 30px !important;}&#8221;][vc_column width=&#8221;1\/5&#8243;][\/vc_column][vc_column width=&#8221;3\/5&#8243;][vc_column_text css=&#8221;.vc_custom_1773224816983{padding-top: 20px !important;padding-bottom: 10px !important;}&#8221;]Data quality is not only crucial in factoring. 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