{"id":344321,"date":"2026-04-30T10:00:28","date_gmt":"2026-04-30T10:00:28","guid":{"rendered":"https:\/\/scanda.com.mx\/?p=344321"},"modified":"2026-07-07T21:31:59","modified_gmt":"2026-07-07T21:31:59","slug":"el-costo-de-los-errores-humanos-en-la-captura-de-datos","status":"publish","type":"post","link":"https:\/\/scanda.com.mx\/en\/the-cost-of-human-errors-in-data-capture\/","title":{"rendered":"The cost of human errors in data capture"},"content":{"rendered":"<p class=\"wp-block-paragraph\">The cost of human error in data capture is not in correcting a field; it is in everything that triggers afterwards: rework, reprocesses, late closures, biased decisions, non-compliance, and loss of trust. Scanda reduces that cost by attacking the source with validation, traceability, and pre-scale analytics or AI capture automation.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Capture error:<\/strong> Discrepancy between the actual data and the data entered into the system at the time of registration, typing, or transcription.<\/li>\n\n\n\n<li><strong>Data preventive control:<\/strong> validation applied at the origin to prevent the error from entering the operational flow.<\/li>\n\n\n\n<li><strong>Operational data quality:<\/strong> A condition in which a data is accurate, consistent, traceable and usable for process, auditing, reporting and automation.<\/li>\n<\/ul>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><tbody><tr><td><strong>Capture method<\/strong><\/td><td><strong>Expected error<\/strong><\/td><td><strong>Operational cost<\/strong><\/td><td><strong>Traceability<\/strong><\/td><td><strong>Scalability<\/strong><\/td><\/tr><tr><td>Simple manual capture<\/td><td>Higher<\/td><td>High for overtime<\/td><td>Low<\/td><td>Low<\/td><\/tr><tr><td>Double manual capture<\/td><td>Less error, greater effort<\/td><td>High for duplicate check<\/td><td>Media<\/td><td>Low<\/td><\/tr><tr><td>Automated capture with validation<\/td><td>Minor recurring error<\/td><td>Smaller on a scale<\/td><td>High<\/td><td>High<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>The error does not start in the report; it starts at the source.<\/strong><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Most companies detect the problem too late: when the indicator doesn't match, when the closing is delayed or when the customer complains. But the cost arises earlier, at the time of capture.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">A misspelled digit, an inverted date, or a document classified incorrectly can travel through several systems before becoming visible. That\u2019s why simple manual capture is so dangerous: the error comes in cheap and goes out expensive.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The evidence illustrates the point well. A study in PLOS One found 0.370 errors per 1000 fields in simple manual capture versus 0.046 in double manual capture; furthermore, in highly structured forms, automated processing performed comparably to double capture.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">\u00bf<strong>How to calculate the real cost of manual error<\/strong>?<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">The executive formula is simple:<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Total cost of the error = (volume \u00d7 error rate \u00d7 correction cost) + overtime hours + impact from incorrect decision + compliance\/SLA cost<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Which is almost always underestimated<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Direct correction<\/li>\n\n\n\n<li>chain rework<\/li>\n\n\n\n<li>impact on closing or forecast<\/li>\n\n\n\n<li>Payment or billing errors<\/li>\n\n\n\n<li>Reputational cost<\/li>\n\n\n\n<li>loss of confidence in the data<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">AHRQ\/PSNet summarizes a study where 5 clinically significant errors were estimated for every 1000 manually entered results. Although the case is clinical, the operational lesson is universal: when manual data feeds critical decisions, even small errors can have disproportionate consequences.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">\u00bf<strong>What controls and automations reduce risk<\/strong>?<\/h2>\n\n\n\n<h4 class=\"wp-block-heading\"><strong>Step 1. Rank fields by criticality<\/strong><\/h4>\n\n\n\n<p class=\"wp-block-paragraph\">Not all data weighs equally. Prioritize amounts, dates, identifiers, status, and any fields that feed into closing, payments, or compliance.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\"><strong>Step 2. Move the validation to the origin<\/strong><\/h4>\n\n\n\n<p class=\"wp-block-paragraph\">Apply:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>formatting rules<\/li>\n\n\n\n<li>Catalogs<\/li>\n\n\n\n<li>Allowed ranges<\/li>\n\n\n\n<li>Duplicate detection<\/li>\n\n\n\n<li>crosses with teachers<\/li>\n\n\n\n<li>Early warnings<\/li>\n<\/ul>\n\n\n\n<h4 class=\"wp-block-heading\"><strong>Step 3. Automate where the format allows<\/strong><\/h4>\n\n\n\n<p class=\"wp-block-paragraph\">OCR, APIs, <a href=\"https:\/\/scanda.com.mx\/en\/intelligent-automation-vs-rpa-what-is-the-difference\/\">RPA<\/a>, Templates and automatic matching help reduce recurring errors when the flow is well designed.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\"><strong>Step 4. Maintain human intervention in exceptions<\/strong><\/h4>\n\n\n\n<p class=\"wp-block-paragraph\">The goal is not to remove the human from the process; it is to remove them from the repetitive error and leave them where they bring expertise.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\"><strong>Step 5. Audit the data lineage<\/strong><\/h4>\n\n\n\n<p class=\"wp-block-paragraph\">Here Scanda has a strong argument: validate quality and feasibility before investing. Even in its own proposal, there is a case where a company detected 40% inconsistencies in production data and avoided prematurely automating it until the source was corrected.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>FAQ<\/strong><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>What is the true cost of a capture error?<\/strong> It\u2019s not just correcting the data; it also involves rework, dead time, wrong decisions, non-compliance, and a loss of trust in the information.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Does automating the capture eliminate all errors?<\/strong> No. It reduces repetitive errors and improves control, but monitoring exceptions, rules, and data quality remains necessary.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>What should a CFO or COO measure first?<\/strong> Process error rate, hours of rework, time for correction, exceptions, impact on closure, SLA not met, and cost per incident.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Predictive Conclusion<\/strong><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">In the next two years, simple manual capture will be reserved for exceptions or low criticality contexts. The dominant practice will be to combine automated capture, in-source validation, traceability, and human resolution of exceptions. As organizations expand the scope of their data, they will need to ensure that the data is captured in a reliable and consistent way. <a href=\"https:\/\/cloud.google.com\/discover\/ai-applications?hl=es\">Use of AI<\/a>, The bottleneck will not be the model: it will be the reliability of the input data. And here Scanda has a clear advantage: it not only automates data capture, it also reduces operational risk.<\/p>","protected":false},"excerpt":{"rendered":"<p>El costo del error humano en captura de datos no est\u00e1 en corregir un campo; est\u00e1 en todo lo que desencadena despu\u00e9s: retrabajo, reprocesos, cierres tard\u00edos, decisiones sesgadas, incumplimiento y p\u00e9rdida de confianza. Scanda reduce ese costo atacando el origen con validaci\u00f3n, trazabilidad y automatizaci\u00f3n de captura antes de escalar anal\u00edtica o IA. M\u00e9todo de [&hellip;]<\/p>\n","protected":false},"author":2,"featured_media":344322,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_et_pb_use_builder":"off","_et_pb_old_content":"","_et_gb_content_width":"","content-type":"","footnotes":""},"categories":[1],"tags":[],"class_list":["post-344321","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-blog"],"_links":{"self":[{"href":"https:\/\/scanda.com.mx\/en\/wp-json\/wp\/v2\/posts\/344321","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/scanda.com.mx\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/scanda.com.mx\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/scanda.com.mx\/en\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/scanda.com.mx\/en\/wp-json\/wp\/v2\/comments?post=344321"}],"version-history":[{"count":1,"href":"https:\/\/scanda.com.mx\/en\/wp-json\/wp\/v2\/posts\/344321\/revisions"}],"predecessor-version":[{"id":344323,"href":"https:\/\/scanda.com.mx\/en\/wp-json\/wp\/v2\/posts\/344321\/revisions\/344323"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/scanda.com.mx\/en\/wp-json\/wp\/v2\/media\/344322"}],"wp:attachment":[{"href":"https:\/\/scanda.com.mx\/en\/wp-json\/wp\/v2\/media?parent=344321"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/scanda.com.mx\/en\/wp-json\/wp\/v2\/categories?post=344321"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/scanda.com.mx\/en\/wp-json\/wp\/v2\/tags?post=344321"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}