{"id":2715,"date":"2021-05-11T14:22:11","date_gmt":"2021-05-11T12:22:11","guid":{"rendered":"https:\/\/devstage.bix-consulting.com\/?p=2715"},"modified":"2023-06-16T11:50:51","modified_gmt":"2023-06-16T09:50:51","slug":"erweiterung-bestehendes-bw","status":"publish","type":"post","link":"https:\/\/teststage.bix-consulting.com\/en\/erweiterung-bestehendes-bw\/","title":{"rendered":"Extension of an existing business warehouse"},"content":{"rendered":"<p>[et_pb_section fb_built=&#8220;1&#8243; disabled_on=&#8220;on|on|on&#8220; _builder_version=&#8220;4.21.0&#8243; _module_preset=&#8220;default&#8220; custom_padding=&#8220;||0px||false|false&#8220; disabled=&#8220;on&#8220; global_colors_info=&#8220;{}&#8220; theme_builder_area=&#8220;et_body_layout&#8220;][et_pb_row use_custom_gutter=&#8220;on&#8220; gutter_width=&#8220;2&#8243; _builder_version=&#8220;4.21.0&#8243; _module_preset=&#8220;default&#8220; background_color=&#8220;#F4F4F4&#8243; border_radii=&#8220;off|20px|20px||&#8220; global_colors_info=&#8220;{}&#8220; theme_builder_area=&#8220;et_body_layout&#8220;][et_pb_column type=&#8220;4_4&#8243; _builder_version=&#8220;4.21.0&#8243; _module_preset=&#8220;default&#8220; global_colors_info=&#8220;{}&#8220; theme_builder_area=&#8220;et_body_layout&#8220;][et_pb_text _builder_version=&#8220;4.21.0&#8243; _module_preset=&#8220;default&#8220; custom_padding=&#8220;|20px||20px|false|false&#8220; global_colors_info=&#8220;{}&#8220; theme_builder_area=&#8220;et_body_layout&#8220;]<\/p>\n<h4>Challenge<\/h4>\n<p style=\"text-align: justify;\">Ein in europaweit agierendes Unternehmen bietet Tankkarten f\u00fcr seine ca. 250.000 Gesch\u00e4ftskunden an. Das Gesch\u00e4ftsmodell beinhaltet f\u00fcr das Unternehmen ein hohes finanzielles Risiko, da es prozessbedingt in Vorleistung (Kostenabgang vor Zahlung des Endkunden) geht. Die H\u00f6he der Vorleistung betr\u00e4gt pro Monat einen 10-stelligen Betrag! Ziel muss es demnach sein, die Wahrscheinlichkeit eines Zahlungsausfalls zu minimieren.<\/p>\n<p>[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=&#8220;4.21.0&#8243; _module_preset=&#8220;default&#8220; custom_padding=&#8220;0px||0px||false|false&#8220; global_colors_info=&#8220;{}&#8220; theme_builder_area=&#8220;et_body_layout&#8220;][et_pb_column type=&#8220;4_4&#8243; _builder_version=&#8220;4.21.0&#8243; _module_preset=&#8220;default&#8220; global_colors_info=&#8220;{}&#8220; theme_builder_area=&#8220;et_body_layout&#8220;][et_pb_post_title title=&#8220;off&#8220; meta=&#8220;off&#8220; force_fullwidth=&#8220;off&#8220; image_width=&#8220;60%&#8220; _builder_version=&#8220;4.21.0&#8243; _module_preset=&#8220;default&#8220; background_color=&#8220;#F4F4F4&#8243; custom_margin=&#8220;||||false|false&#8220; custom_padding=&#8220;||||false|false&#8220; global_colors_info=&#8220;{}&#8220; theme_builder_area=&#8220;et_body_layout&#8220;][\/et_pb_post_title][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=&#8220;4.21.0&#8243; _module_preset=&#8220;default&#8220; custom_padding=&#8220;0px||||false|false&#8220; global_colors_info=&#8220;{}&#8220; theme_builder_area=&#8220;et_body_layout&#8220;][et_pb_column type=&#8220;4_4&#8243; _builder_version=&#8220;4.21.0&#8243; _module_preset=&#8220;default&#8220; global_colors_info=&#8220;{}&#8220; theme_builder_area=&#8220;et_body_layout&#8220;][et_pb_text _builder_version=&#8220;4.21.0&#8243; _module_preset=&#8220;default&#8220; background_color=&#8220;#F4F4F4&#8243; custom_padding=&#8220;20px|20px|20px|20px|false|false&#8220; border_radii=&#8220;off|||20px|20px&#8220; global_colors_info=&#8220;{}&#8220; theme_builder_area=&#8220;et_body_layout&#8220;]<\/p>\n<h4>The approach<\/h4>\n<p style=\"text-align: justify;\">biX Consulting hat auf Basis von KI ein Modell entwickelt, welches die Problematik der Kundenbewertung deutlich optimiert. Im Rahmen von Big Data werden sekundenschnell interne sowie externe Daten zusammengef\u00fchrt, verarbeitet und daraus eine verl\u00e4ssliche Aussage zur Bonit\u00e4t des (Neu)-Kunden getroffen. Mit dieser L\u00f6sung wird die Wahrscheinlichkeit des Ausfallsrisikos elementar reduziert.<\/p>\n<p>[\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=&#8220;1&#8243; _builder_version=&#8220;4.21.0&#8243; _module_preset=&#8220;default&#8220; custom_margin=&#8220;||||false|false&#8220; custom_padding=&#8220;0px||||false|false&#8220; global_colors_info=&#8220;{}&#8220; theme_builder_area=&#8220;et_body_layout&#8220;][et_pb_row use_custom_gutter=&#8220;on&#8220; gutter_width=&#8220;2&#8243; make_equal=&#8220;on&#8220; _builder_version=&#8220;4.21.0&#8243; _module_preset=&#8220;default&#8220; background_color=&#8220;#F4F4F4&#8243; border_radii=&#8220;on|20px|20px|20px|20px&#8220; global_colors_info=&#8220;{}&#8220; theme_builder_area=&#8220;et_body_layout&#8220;][et_pb_column type=&#8220;4_4&#8243; _builder_version=&#8220;4.21.0&#8243; _module_preset=&#8220;default&#8220; global_colors_info=&#8220;{}&#8220; theme_builder_area=&#8220;et_body_layout&#8220;][et_pb_text _builder_version=&#8220;4.21.0&#8243; _module_preset=&#8220;default&#8220; custom_padding=&#8220;|20px|0px|20px|false|false&#8220; hover_enabled=&#8220;0&#8243; global_colors_info=&#8220;{}&#8220; theme_builder_area=&#8220;et_body_layout&#8220; sticky_enabled=&#8220;0&#8243;]<\/p>\n<h4>Challenge<\/h4>\n<p style=\"text-align: justify;\">The monitoring of loading processes in data warehousing often poses a particular challenge. Even if all processes seem to run through without mistakes, this does not mean that the data is available in the required quality. In particular, non-obvious errors necessitate regular quantitative and qualitative checks of the data, which are time-consuming and must be carried out manually.<\/p>\n<p>[\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=&#8220;1&#8243; _builder_version=&#8220;4.21.0&#8243; _module_preset=&#8220;default&#8220; custom_margin=&#8220;||||false|false&#8220; custom_padding=&#8220;0px||0px||false|false&#8220; global_colors_info=&#8220;{}&#8220; theme_builder_area=&#8220;et_body_layout&#8220;][et_pb_row _builder_version=&#8220;4.21.0&#8243; _module_preset=&#8220;default&#8220; custom_padding=&#8220;||0px||false|false&#8220; global_colors_info=&#8220;{}&#8220; theme_builder_area=&#8220;et_body_layout&#8220;][et_pb_column type=&#8220;4_4&#8243; _builder_version=&#8220;4.21.0&#8243; _module_preset=&#8220;default&#8220; global_colors_info=&#8220;{}&#8220; theme_builder_area=&#8220;et_body_layout&#8220;][et_pb_post_title title=&#8220;off&#8220; meta=&#8220;off&#8220; _builder_version=&#8220;4.21.0&#8243; _module_preset=&#8220;default&#8220; border_radii=&#8220;on|20px|20px|20px|20px&#8220; global_colors_info=&#8220;{}&#8220; theme_builder_area=&#8220;et_body_layout&#8220;][\/et_pb_post_title][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=&#8220;1&#8243; _builder_version=&#8220;4.21.0&#8243; _module_preset=&#8220;default&#8220; custom_margin=&#8220;||||false|false&#8220; custom_padding=&#8220;||0px||false|false&#8220; global_colors_info=&#8220;{}&#8220; theme_builder_area=&#8220;et_body_layout&#8220;][et_pb_row _builder_version=&#8220;4.21.0&#8243; _module_preset=&#8220;default&#8220; custom_padding=&#8220;0px||0px||false|false&#8220; global_colors_info=&#8220;{}&#8220; theme_builder_area=&#8220;et_body_layout&#8220;][et_pb_column type=&#8220;4_4&#8243; _builder_version=&#8220;4.21.0&#8243; _module_preset=&#8220;default&#8220; global_colors_info=&#8220;{}&#8220; theme_builder_area=&#8220;et_body_layout&#8220;][et_pb_text _builder_version=&#8220;4.21.0&#8243; _module_preset=&#8220;default&#8220; background_color=&#8220;RGBA(255,255,255,0)&#8220; custom_padding=&#8220;20px|20px|20px|20px|false|false&#8220; hover_enabled=&#8220;0&#8243; border_radii=&#8220;off|||20px|20px&#8220; global_colors_info=&#8220;{}&#8220; theme_builder_area=&#8220;et_body_layout&#8220; sticky_enabled=&#8220;0&#8243;]<\/p>\n<h4>The approach<\/h4>\n<p style=\"text-align: justify;\">Based on the history, a model is created from which the data warehouse system learns how change results can be considered normal in the data - due to daily loading processes.\n\nOn this basis, corridors can then be determined regarding the number of changes, but also regarding content elements, such as outliers in key figures or amounts of certain aggregation levels. If these corridors are then exceeded or undercut, the system reports this and a targeted error analysis is possible.<\/p>\n<p style=\"text-align: justify;\">By using the biX AI Tools, the solution approach can be fully integrated into your SAP system.<\/p>\n<p>[\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=&#8220;1&#8243; _builder_version=&#8220;4.21.0&#8243; _module_preset=&#8220;default&#8220; custom_padding=&#8220;||0px||false|false&#8220; global_colors_info=&#8220;{}&#8220; theme_builder_area=&#8220;et_body_layout&#8220;][et_pb_row _builder_version=&#8220;4.21.0&#8243; _module_preset=&#8220;default&#8220; background_color=&#8220;#F4F4F4&#8243; border_radii=&#8220;on|20px|20px|20px|20px&#8220; global_colors_info=&#8220;{}&#8220; theme_builder_area=&#8220;et_body_layout&#8220;][et_pb_column type=&#8220;4_4&#8243; _builder_version=&#8220;4.16&#8243; _module_preset=&#8220;default&#8220; global_colors_info=&#8220;{}&#8220; theme_builder_area=&#8220;et_body_layout&#8220;][et_pb_text _builder_version=&#8220;4.21.0&#8243; _module_preset=&#8220;default&#8220; custom_padding=&#8220;|20px||20px|false|false&#8220; hover_enabled=&#8220;0&#8243; global_colors_info=&#8220;{}&#8220; theme_builder_area=&#8220;et_body_layout&#8220; sticky_enabled=&#8220;0&#8243;]<\/p>\n<h4>Your benefit<\/h4>\n<p style=\"text-align: justify;\"><span>Monitoring expenses can be significantly reduced, as regular monitoring is carried out by the system itself. In addition, deviations in both quantitative and qualitative aspects can be detected and analysed earlier. In the long run, this leads to a higher overall quality of the system.<\/span><\/p>\n<p>[\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section]<\/p>","protected":false},"excerpt":{"rendered":"<p>Reduction of the monitoring effort through customer-specific extensions of the existing business warehouse<\/p>","protected":false},"author":6,"featured_media":2829,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_et_pb_use_builder":"on","_et_pb_old_content":"","_et_gb_content_width":"","_lmt_disableupdate":"","_lmt_disable":"","footnotes":""},"categories":[32],"tags":[43,39,26,45,29,31,61,89,92,48,40,93,90,91,38,27,44,41,50,42,49,46,47],"modified_by":"admin","_links":{"self":[{"href":"https:\/\/teststage.bix-consulting.com\/en\/wp-json\/wp\/v2\/posts\/2715"}],"collection":[{"href":"https:\/\/teststage.bix-consulting.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/teststage.bix-consulting.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/teststage.bix-consulting.com\/en\/wp-json\/wp\/v2\/users\/6"}],"replies":[{"embeddable":true,"href":"https:\/\/teststage.bix-consulting.com\/en\/wp-json\/wp\/v2\/comments?post=2715"}],"version-history":[{"count":0,"href":"https:\/\/teststage.bix-consulting.com\/en\/wp-json\/wp\/v2\/posts\/2715\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/teststage.bix-consulting.com\/en\/wp-json\/wp\/v2\/media\/2829"}],"wp:attachment":[{"href":"https:\/\/teststage.bix-consulting.com\/en\/wp-json\/wp\/v2\/media?parent=2715"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/teststage.bix-consulting.com\/en\/wp-json\/wp\/v2\/categories?post=2715"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/teststage.bix-consulting.com\/en\/wp-json\/wp\/v2\/tags?post=2715"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}