{"id":1952,"date":"2026-03-20T10:13:28","date_gmt":"2026-03-20T10:13:28","guid":{"rendered":"https:\/\/dorg.pro\/competency\/data-quality-module-service\/"},"modified":"2026-04-02T10:34:03","modified_gmt":"2026-04-02T10:34:03","slug":"data-quality-module-service","status":"publish","type":"competency","link":"https:\/\/dorg.pro\/en\/competency\/data-quality-module-service\/","title":{"rendered":"Data Quality Module Service"},"content":{"rendered":"\n<p>Before integrating predictive capabilities into a DORG, an organization must know if its data is ready, which ML model is best suited for its use case, and if the investment produces a measurable return. DORG University provides this service directly through the application of its proprietary module \u2014 not available on the market nor transferable to Faculty or third parties. <\/p>\n\n<p>The service is divided into two distinct phases. In the first, DORG University analyzes the client&#8217;s dataset, calculates characterization metrics, identifies relevant independent and dependent variables, optimizes hyperparameter selection, and trains the model by identifying predictive patterns. In the second phase, the validation module estimates the algorithm&#8217;s performance as the dataset grows \u2014 at 2x, 5x, and 10x the available data \u2014 and translates technical metrics into concrete economic indicators: minimum performance for production release, payback period, ROI over a selectable horizon, and reallocable man-hours.  <\/p>\n\n<p>The result is a structured business case \u2014 with best-case and worst-case scenarios \u2014 that supports the investment decision with verifiable quantitative data. The service includes support for dataset cleaning and preparation in the preliminary phase. <\/p>\n\n<p>Main areas of application: predictive maintenance in manufacturing, demand forecasting in supply chain, identification of anomalies and fraud in the financial sector, and predictive process quality.<\/p>\n\n<p><\/p>\n","protected":false},"featured_media":1954,"template":"","meta":{"_acf_changed":false},"global-service":[84,84],"class_list":["post-1952","competency","type-competency","status-publish","has-post-thumbnail","hentry","global-service-dorg-operational-competency"],"acf":[],"_links":{"self":[{"href":"https:\/\/dorg.pro\/en\/wp-json\/wp\/v2\/competency\/1952","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/dorg.pro\/en\/wp-json\/wp\/v2\/competency"}],"about":[{"href":"https:\/\/dorg.pro\/en\/wp-json\/wp\/v2\/types\/competency"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/dorg.pro\/en\/wp-json\/wp\/v2\/media\/1954"}],"wp:attachment":[{"href":"https:\/\/dorg.pro\/en\/wp-json\/wp\/v2\/media?parent=1952"}],"wp:term":[{"taxonomy":"global-service","embeddable":true,"href":"https:\/\/dorg.pro\/en\/wp-json\/wp\/v2\/global-service?post=1952"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}