{"id":20483,"date":"2026-02-06T00:00:00","date_gmt":"2026-02-06T00:00:00","guid":{"rendered":"https:\/\/is.miind.agency\/core-banking-data-not-decision-ready\/"},"modified":"2026-02-06T00:00:00","modified_gmt":"2026-02-06T00:00:00","slug":"core-banking-data-not-decision-ready","status":"publish","type":"post","link":"https:\/\/is.miind.agency\/en\/core-banking-data-not-decision-ready\/","title":{"rendered":"Why Core Banking data is not ready for decision-making and how to fix it"},"content":{"rendered":"\n<article>\n<p>In most Swiss private banks, Core Banking Systems such as Avaloq, OLYMPIC, Temenos, Wize or Efficience are powerful engines for daily operations. They ensure transaction execution, regulatory compliance, and client servicing. But when it comes to steering the business, these systems fall short. The \u201craw\u201d data they produce is not directly usable for decision-making.<\/p>\n\n<p>This is not a technical issue \u2014 it is a structural one.<\/p>\n\n<h2>Core Banking data: complete, yet not usable for analytics<\/h2>\n\n<p>Private banks today possess more data than ever before. But operational data is rarely suitable for analytical needs. It is often:<\/p>\n\n<ul>\n  <li><strong>Fragmented<\/strong> across modules and systems<\/li>\n  <li><strong>Context-dependent<\/strong> with different interpretations per team<\/li>\n  <li><strong>Difficult to reconcile<\/strong> across the organisation<\/li>\n  <li><strong>Not designed<\/strong> for analytical or strategic use<\/li>\n<\/ul>\n\n<p>As a result, instead of enabling decisions, data fuels internal debates and inconsistencies. Teams end up asking the same recurring question: <strong>\u201cWhich number is correct?\u201d<\/strong><\/p>\n\n<h2>Key performance indicators don\u2019t mean the same thing for everyone<\/h2>\n\n<p>Across many private banks, even the most essential KPIs lack a unified definition:<\/p>\n\n<ul>\n  <li>AuM<\/li>\n  <li>NnM<\/li>\n  <li>LiA<\/li>\n  <li>Revenues<\/li>\n  <li>RoA<\/li>\n  <li>Margins<\/li>\n  <li>Costs<\/li>\n  <li>Retrocessions<\/li>\n<\/ul>\n\n<p>Each department applies different rules, filters, exclusions and time horizons. Uncontrolled Excel extractions multiply discrepancies and create internal \u201cdata wars\u201d, with multiple versions of the truth circulating at once. Without a proper analytical data model, misalignment becomes the norm.<\/p>\n\n<h2>The solution: an analytical layer integrated into the Core Banking System<\/h2>\n\n<p>To be truly useful for decision-making, an analytical layer must perform <strong>three essential functions<\/strong>:<\/p>\n\n<h3>1. Standardise definitions<\/h3>\n<p>A shared data dictionary covering KPIs, dimensions, business rules, exceptions and periods. This is the foundation of governance and comparability.<\/p>\n\n<h3>2. Ensure full traceability<\/h3>\n<p>Decision-makers must understand where each number comes from, which transformations were applied, and how calculations were performed. Without transparency, trust in the data collapses.<\/p>\n\n<h3>3. Deliver data continuously, not as \u201cone-shot\u201d extractions<\/h3>\n<p>Analytics must be connected, refreshed and integrated into operational and managerial workflows \u2014 not produced manually through Excel.<\/p>\n\n<h2>What we observed in a Swiss private bank<\/h2>\n\n<p>The primary outcome was not \u201cone more dashboard\u201d. The real impact was <strong>the end of internal debates over which version of the number to trust<\/strong>.<\/p>\n\n<p>When data governance becomes solid, teams regain time, clarity and alignment \u2014 and the organisation finally focuses on performance instead of reconciliation.<\/p>\n\n<p>#swissmade #privatebanking #data #integraal #governance<\/p>\n<\/article>\n","protected":false},"excerpt":{"rendered":"<p>In most Swiss private banks, Core Banking Systems such as Avaloq, OLYMPIC, Temenos, Wize or Efficience are powerful engines for [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":19397,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"categories":[163],"tags":[],"industrie":[157],"solution":[153,156,152,151,154],"class_list":["post-20483","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-business-cases","industrie-private-banking","solution-credit-monitoring","solution-data-governance","solution-mis-profitability","solution-portfolio-investment-insights","solution-pricing-simulations"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.6 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Why Core Banking data is not ready for decision-making and how to fix it &#8211; 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