{"id":16547,"date":"2026-07-22T09:00:00","date_gmt":"2026-07-22T07:00:00","guid":{"rendered":"https:\/\/www.datamondial.com\/?p=16547"},"modified":"2026-07-06T10:25:27","modified_gmt":"2026-07-06T08:25:27","slug":"erp-implementation-stall-data-migration-cleanup","status":"publish","type":"post","link":"https:\/\/www.datamondial.com\/en\/erp-implementation-stall-data-migration-cleanup\/","title":{"rendered":"Why Your ERP Implementation is Stalling: Viewing Data Migration as an IT Task, Not a Data Cleanup Project"},"content":{"rendered":"

Executive boards approving the budget for a new ERP system almost inevitably face cost overruns down the line. The root cause of these financial setbacks rarely lies in faulty software. A persistent design flaw arises because decision-makers treat data migration as a simple copy-paste job from the legacy system to the new one. When making the strategic choice between in-house cleanup and nearshoring for such transitions, it becomes clear that the quality of the source files dictates success.<\/p>\n

Polluted legacy data frustrates the entire implementation process. Years of accumulating flawed master data, duplicate customer files, and unresolved waybills block the automated flow of information. This specific issue doesn’t occur when an organization merely runs a version upgrade where the underlying data model remains completely identical on the database side. However, as soon as you transition to a new ERP architecture with different table structures, historical data demands immediate attention to safeguard your go-live. That is why it is essential to proactively clean up or migrate your customer data using a structured approach.<\/p>\n

The Blind Spot in System Transitions<\/h2>\n

A migration project fails spectacularly when technical project teams focus solely on the software layer. There is a massive gap between standard IT capabilities and a truly operational data perspective. Software and migration scripts simply map patterns. A migration tool forces a field from system A into a pre-structured column in system B, but the algorithm cannot read business context.<\/p>\n

According to KPMG’s insight article ‘New systems, old data \u2013 data migration tips for the project manager’, this exact lack of context provides the direct fuel for future problems. Duplicate freight forwarder entries are transferred blindly and unfiltered into the new environment via automated scripts. IT consultants possess the technical skills to structure the database. However, they do not validate selected customs codes or specific loading references for factual accuracy. <\/p>\n

This creates a structural mismatch in expectations during the project. The IT team has one focal point: hitting the go-live within the planned sprints. Once the database is populated and the software is running, they check off that technical milestone. At that momentum, the entire burden of correcting transferred errors and inconsistent files is pushed onto the back office\u2014a team simultaneously struggling to master an unfamiliar software package.<\/p>\n

Mapping Versus Validation in Practice<\/h3>\n

Defining the routing maps for data transport is known as data mapping. A field like ‘Customer_ID’ in the legacy software gets a technical link to ‘Debtor_Number’ in the new ERP. This exercise merely guarantees that the letters and numbers end up in the correct database slot. <\/p>\n

Validation focuses purely on content. An entered customs code might be technically mapped with success as a seven-digit string, but it could be completely obsolete under the latest export documentation regulations. Validation ensures the record is commercially correct and flows smoothly through logistical scanning processes without requiring human intervention.<\/p>\n

The Hidden Workload Beyond the Deadline<\/h3>\n

If project managers steer exclusively by IT milestones, operational chaos will only reveal itself immediately after the formal handover. Imported freight contracts lack updated fuel surcharges or return conditions. To avoid delaying current shipments, back-office staff must manually correct these file errors ad-hoc within the newly launched system. The time savings a new ERP was supposed to deliver evaporate entirely within the first few weeks due to this invisible recovery phase. The Cegeka article ‘Data migration for a new ERP: preventing a debacle’ identifies this exact pattern as the primary source of implementation stress.<\/p>\n

Why Data Quality is an Operational Responsibility<\/h2>\n

Domain knowledge dictates the success of a data migration. Only industry specialists can spot the vital nuances needed to flawlessly classify and clean logistical and financial data before the transfer. Years of decentralized data entry across different departments yield unpredictable and inconsistent field values. Free-text fields often inadvertently contain hard agreements about loading and unloading times or customer-specific billing criteria. <\/p>\n

Only operational staff with a deep understanding of underlying supply chain logic can recognize these apparent anomalies in historical records management. When screening outdated tariff agreements and historical waybills, they filter the data using human judgment. They determine whether a specific logistical record should be overwritten, archived as inactive, or fundamentally enriched using external data sources before it burdens the new system.<\/p>\n

In the methodology used to visualize this separation, the ‘system logic versus data validity’ matrix acts as a powerful compass. This framework identifies which datasets are suitable for automated batch cleanup and which records demand individual visual inspection by a subject matter expert. To clearly define the workload, teams generally start with a checklist of master data mutations over the past 12 months. Any information left untouched during that period undergoes mandatory screening before inclusion in the final upload. Implementing such robust frameworks usually hits a roadblock of insufficient back-office capacity almost immediately. While this intensive preparation and testing phase demands heavy involvement, operational departments are still trying to maintain the regular flow of goods without interruption.<\/p>\n

Supply Chain Logic as a Necessary Filter<\/h3>\n

Decentralized data entry entails a human element. Typographical errors in container numbers, expired HS codes, or non-standard notations for hazardous materials (ADR) are commonplace in any mature ERP system. Across historical waybills and transport agreements, this translates into thousands of process exceptions. Algorithms simply freeze when confronted with these deviations. Human decision-making, driven by logistics business logic, forms the critical operational filter needed to resolve these interpretation issues upfront and ensure clean data streams.<\/p>\n

Table: IT vs. Business Responsibilities in Data Migration<\/h3>\n

A sharp division of responsibilities prevents project failure. The matrix below provides immediate insight into the distribution of tasks between the technical team and the core organization during the cleanup and migration trajectory.<\/p>\n\n\n\n\n\n\n\n\n\n
Component<\/th>\nIT Department \/ Software Provider<\/th>\nBusiness \/ Operational Back Office<\/th>\n<\/tr>\n<\/thead>\n
Structure & Mapping<\/strong><\/td>\nLink fields via scripts (legacy table to new table).<\/td>\nDefine which free-text fields are migrated or discarded.<\/td>\n<\/tr>\n
Validation<\/strong><\/td>\nTest whether records import into the staging environment without error codes.<\/td>\nVerify that data accurately aligns with current tariffs and contracts.<\/td>\n<\/tr>\n
Cleanup<\/strong><\/td>\nExecute mass delete actions and queries for duplicate records.<\/td>\nIdentify which of the duplicate records contains the single source of truth.<\/td>\n<\/tr>\n
Archiving<\/strong><\/td>\nSet up the technical separation of historical data into cold storage.<\/td>\nDetermine which historical files must be retained for legal compliance.<\/td>\n<\/tr>\n
Go-Live Testing Phase<\/strong><\/td>\nPerform system performance and load-time testing.<\/td>\nVerify data accuracy by running random samples of incoming customer orders.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Solving Back-Office Capacity Constraints<\/h3>\n

The intense time pressure surrounding data preparation, filtering, and correction puts operations under severe strain. Organizations face a tough choice: can structural cleanup based on logistics logic realistically be handled internally? The pure hours required for the implementation directly clash with the necessity to ensure continuous customer satisfaction and operational continuity.<\/p>\n

Engaging a specialized BPO (Business Process Outsourcing) provider for data entry and file compilation creates much-needed breathing room. European nearshoring facilities offer highly reliable stability in this area. This strategy combines immediate EU compliance for data processing with rapid communication across shared time zones. An external team of trained documentation personnel can process transport documents, waybills, and financial contract conditions at scale. This immense scalability drastically accelerates the preparatory phase, laying out your data in exactly the right format.<\/p>\n

The Cost of Postponing Error Correction<\/h2>\n

The management memo noting ‘we will resolve remaining data issues post go-live’ guarantees maximum financial damage. In practice, this functions as a massive delay mechanism that instantly erodes the ROI of the newly purchased software package. Companies that push errors down the line are confronted with operational paralysis in their core processes the very day after migration.<\/p>\n

Storing flawed master data blocks vital business functions like order-to-cash. The moment collections stagnate due to system bugs, panic ensues. Ad-hoc data clinics stall daily business operations, pulling expensive ERP consultants and internal team leaders off their regular duties just to manually steer locked tables. The remaining project budget reserved for the final optimization phase evaporates rapidly through these unplanned expenditures. Various independent overviews, including SAP’s own ‘ERP Migration Checklist’, emphasize the explicit dangers hidden in timing: repairing a live environment takes demonstrably longer than proper preparation.<\/p>\n

The Immediate Impact on Billing Streams<\/h3>\n

Unprocessed or poor data places an immediate brake on revenue recognition from the exact moment of production. Billing streams simply do not tolerate hiccups in debtor numbers or VAT IDs. Incomplete data running through a tightly configured new financial software package instantly generates error logs, prompting the system logic to park those invoices as drafts. Files with missing or incorrect waybills get stuck during recalculations. Payments are subsequently delayed or completely rejected by supply chain partners who demand that reference numbers on financial documents are meticulously accurate. Furthermore, management forecasting reports lose all credibility, making accurate billing based on forecasts practically unachievable.<\/p>\n

Calculation Example: Staging Environment Versus User Interface<\/h3>\n

Quantifying this time difference in working hours makes the flaws of a postponed strategy glaringly obvious. Consider a logistical dataset of 10,000 records requiring cleanup. <\/p>\n

    \n
  1. \n

    Front-end Cleanup (Staging Environment):<\/strong> Data streams are temporarily housed in an isolated, protected middle layer (staging). Bulk exports, pivot tables, and specialized data-cleansing tools flag anomalies in simple list views. Resolving these fields across the entire dataset generally averages about one minute per record. This results in roughly 166 man-hours of targeted data processing before the upload even starts. Organizations can effectively outsource this task to lower costs and have the database optimized by specialists.<\/p>\n<\/li>\n

  2. \n

    Back-end Cleanup (User Interface):<\/strong> Post go-live, the record is locked within the protective framework of the active ERP software. To adjust a single record, an employee must navigate visual menus, click through search terms, execute individual manual changes, force a log override entry to alter control data, and dismiss mandatory software pop-ups. This process demands three times as many actions. What initially took one minute now consumes three minutes per file.<\/p>\n<\/li>\n<\/ol>\n

    Consequently, the operational workload explodes linearly from 166 hours up to 500 hours of pure productivity loss. Project teams frequently ignore this user interface penalty, even though this multiplier directly eats into staff capacity that simply does not exist on a working production floor.<\/p>\n

    Prevent Implementation Stalls<\/h2>\n

    Delays in ERP implementations are directly rooted in the underestimation of logistical master data\u2014specifically when it is treated as a strict IT requirement rather than a serious operational cleanup project. Companies significantly reduce the risks of derailing budgets and disrupted billing streams when they validate their files thoroughly using strict business logic before going live. Cleaning up during a preparatory quiet phase protects daily business continuity, saves crucial hours through bulk processing, and guarantees that the new software launches without a costly backlog. Assess exactly what the current state of your master data means for your upcoming system decisions. When comparing in-house bandwidth against nearshoring options for data management, an external perspective often provides the required acceleration. Request a no-obligation process scan through DataMondial and get definitive answers regarding process optimization, required data entry bandwidth, and the complete cleansing and enrichment of your historical database via highly efficient, fully EU-compliant solutions.<\/p>\n","protected":false},"excerpt":{"rendered":"

    Prevent data migration delays in your ERP implementation. Learn why treating data transfer as an IT task rather than an operational cleanup stalls your go-live.<\/p>\n","protected":false},"author":10,"featured_media":16545,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[91],"tags":[],"class_list":["post-16547","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-blog-en"],"acf":[],"yoast_head":"\nERP Implementation: Prevent Data Migration Delays | DataMondial<\/title>\n<meta name=\"description\" content=\"Discover why ERP implementations stall. Learn how treating data migration as an operational cleanup rather than just an IT task prevents costly project delays.\" \/>\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.datamondial.com\/waarom-uw-erp-implementatie-vertraagt-datamigratie-zien-als-it-in-plaats-van-opschoonproject\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"ERP Implementation: Prevent Data Migration Delays | DataMondial\" \/>\n<meta property=\"og:description\" content=\"Discover why ERP implementations stall. Learn how treating data migration as an operational cleanup rather than just an IT task prevents costly project delays.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.datamondial.com\/waarom-uw-erp-implementatie-vertraagt-datamigratie-zien-als-it-in-plaats-van-opschoonproject\/\" \/>\n<meta property=\"og:site_name\" content=\"DataMondial\" \/>\n<meta property=\"article:published_time\" content=\"2026-07-22T07:00:00+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.datamondial.com\/wp-content\/uploads\/2026\/07\/erp-implementation-stall-data-migration-cleanup-en-featured.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"1376\" \/>\n\t<meta property=\"og:image:height\" content=\"768\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"author\" content=\"Ralph van Es\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Ralph van Es\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"9 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\\\/\\\/www.datamondial.com\\\/waarom-uw-erp-implementatie-vertraagt-datamigratie-zien-als-it-in-plaats-van-opschoonproject\\\/#article\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/www.datamondial.com\\\/waarom-uw-erp-implementatie-vertraagt-datamigratie-zien-als-it-in-plaats-van-opschoonproject\\\/\"},\"author\":{\"name\":\"Ralph van Es\",\"@id\":\"https:\\\/\\\/www.datamondial.com\\\/#\\\/schema\\\/person\\\/5438b776538ac7702fbaa3b85ebf463e\"},\"headline\":\"Why Your ERP Implementation is Stalling: Viewing Data Migration as an IT Task, Not a Data Cleanup Project\",\"datePublished\":\"2026-07-22T07:00:00+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\\\/\\\/www.datamondial.com\\\/waarom-uw-erp-implementatie-vertraagt-datamigratie-zien-als-it-in-plaats-van-opschoonproject\\\/\"},\"wordCount\":1846,\"publisher\":{\"@id\":\"https:\\\/\\\/www.datamondial.com\\\/#organization\"},\"image\":{\"@id\":\"https:\\\/\\\/www.datamondial.com\\\/waarom-uw-erp-implementatie-vertraagt-datamigratie-zien-als-it-in-plaats-van-opschoonproject\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/www.datamondial.com\\\/wp-content\\\/uploads\\\/2026\\\/07\\\/erp-implementation-stall-data-migration-cleanup-en-featured.jpg\",\"articleSection\":[\"Blog\"],\"inLanguage\":\"en-US\"},{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/www.datamondial.com\\\/waarom-uw-erp-implementatie-vertraagt-datamigratie-zien-als-it-in-plaats-van-opschoonproject\\\/\",\"url\":\"https:\\\/\\\/www.datamondial.com\\\/waarom-uw-erp-implementatie-vertraagt-datamigratie-zien-als-it-in-plaats-van-opschoonproject\\\/\",\"name\":\"ERP Implementation: Prevent Data Migration Delays | DataMondial\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/www.datamondial.com\\\/#website\"},\"primaryImageOfPage\":{\"@id\":\"https:\\\/\\\/www.datamondial.com\\\/waarom-uw-erp-implementatie-vertraagt-datamigratie-zien-als-it-in-plaats-van-opschoonproject\\\/#primaryimage\"},\"image\":{\"@id\":\"https:\\\/\\\/www.datamondial.com\\\/waarom-uw-erp-implementatie-vertraagt-datamigratie-zien-als-it-in-plaats-van-opschoonproject\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/www.datamondial.com\\\/wp-content\\\/uploads\\\/2026\\\/07\\\/erp-implementation-stall-data-migration-cleanup-en-featured.jpg\",\"datePublished\":\"2026-07-22T07:00:00+00:00\",\"description\":\"Discover why ERP implementations stall. Learn how treating data migration as an operational cleanup rather than just an IT task prevents costly project delays.\",\"breadcrumb\":{\"@id\":\"https:\\\/\\\/www.datamondial.com\\\/waarom-uw-erp-implementatie-vertraagt-datamigratie-zien-als-it-in-plaats-van-opschoonproject\\\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\\\/\\\/www.datamondial.com\\\/waarom-uw-erp-implementatie-vertraagt-datamigratie-zien-als-it-in-plaats-van-opschoonproject\\\/\"]}]},{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\\\/\\\/www.datamondial.com\\\/waarom-uw-erp-implementatie-vertraagt-datamigratie-zien-als-it-in-plaats-van-opschoonproject\\\/#primaryimage\",\"url\":\"https:\\\/\\\/www.datamondial.com\\\/wp-content\\\/uploads\\\/2026\\\/07\\\/erp-implementation-stall-data-migration-cleanup-en-featured.jpg\",\"contentUrl\":\"https:\\\/\\\/www.datamondial.com\\\/wp-content\\\/uploads\\\/2026\\\/07\\\/erp-implementation-stall-data-migration-cleanup-en-featured.jpg\",\"width\":1376,\"height\":768,\"caption\":\"Project manager reviews data mapping during ERP implementation to prevent data migration delays on a modern desk.\"},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\\\/\\\/www.datamondial.com\\\/waarom-uw-erp-implementatie-vertraagt-datamigratie-zien-als-it-in-plaats-van-opschoonproject\\\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\\\/\\\/www.datamondial.com\\\/en\\\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Why Your ERP Implementation is Stalling: Viewing Data Migration as an IT Task, Not a Data Cleanup Project\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\\\/\\\/www.datamondial.com\\\/#website\",\"url\":\"https:\\\/\\\/www.datamondial.com\\\/\",\"name\":\"DataMondial\",\"description\":\"\",\"publisher\":{\"@id\":\"https:\\\/\\\/www.datamondial.com\\\/#organization\"},\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\\\/\\\/www.datamondial.com\\\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"en-US\"},{\"@type\":\"Organization\",\"@id\":\"https:\\\/\\\/www.datamondial.com\\\/#organization\",\"name\":\"DataMondial\",\"url\":\"https:\\\/\\\/www.datamondial.com\\\/\",\"logo\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\\\/\\\/www.datamondial.com\\\/#\\\/schema\\\/logo\\\/image\\\/\",\"url\":\"https:\\\/\\\/www.datamondial.com\\\/wp-content\\\/uploads\\\/2022\\\/10\\\/datamondial_onderschrift.svg\",\"contentUrl\":\"https:\\\/\\\/www.datamondial.com\\\/wp-content\\\/uploads\\\/2022\\\/10\\\/datamondial_onderschrift.svg\",\"width\":431,\"height\":94,\"caption\":\"DataMondial\"},\"image\":{\"@id\":\"https:\\\/\\\/www.datamondial.com\\\/#\\\/schema\\\/logo\\\/image\\\/\"},\"sameAs\":[\"https:\\\/\\\/www.linkedin.com\\\/company\\\/datamondial\\\/\"]},{\"@type\":\"Person\",\"@id\":\"https:\\\/\\\/www.datamondial.com\\\/#\\\/schema\\\/person\\\/5438b776538ac7702fbaa3b85ebf463e\",\"name\":\"Ralph van Es\"}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"ERP Implementation: Prevent Data Migration Delays | DataMondial","description":"Discover why ERP implementations stall. Learn how treating data migration as an operational cleanup rather than just an IT task prevents costly project delays.","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/www.datamondial.com\/waarom-uw-erp-implementatie-vertraagt-datamigratie-zien-als-it-in-plaats-van-opschoonproject\/","og_locale":"en_US","og_type":"article","og_title":"ERP Implementation: Prevent Data Migration Delays | DataMondial","og_description":"Discover why ERP implementations stall. Learn how treating data migration as an operational cleanup rather than just an IT task prevents costly project delays.","og_url":"https:\/\/www.datamondial.com\/waarom-uw-erp-implementatie-vertraagt-datamigratie-zien-als-it-in-plaats-van-opschoonproject\/","og_site_name":"DataMondial","article_published_time":"2026-07-22T07:00:00+00:00","og_image":[{"width":1376,"height":768,"url":"https:\/\/www.datamondial.com\/wp-content\/uploads\/2026\/07\/erp-implementation-stall-data-migration-cleanup-en-featured.jpg","type":"image\/jpeg"}],"author":"Ralph van Es","twitter_card":"summary_large_image","twitter_misc":{"Written by":"Ralph van Es","Est. reading time":"9 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"Article","@id":"https:\/\/www.datamondial.com\/waarom-uw-erp-implementatie-vertraagt-datamigratie-zien-als-it-in-plaats-van-opschoonproject\/#article","isPartOf":{"@id":"https:\/\/www.datamondial.com\/waarom-uw-erp-implementatie-vertraagt-datamigratie-zien-als-it-in-plaats-van-opschoonproject\/"},"author":{"name":"Ralph van Es","@id":"https:\/\/www.datamondial.com\/#\/schema\/person\/5438b776538ac7702fbaa3b85ebf463e"},"headline":"Why Your ERP Implementation is Stalling: Viewing Data Migration as an IT Task, Not a Data Cleanup Project","datePublished":"2026-07-22T07:00:00+00:00","mainEntityOfPage":{"@id":"https:\/\/www.datamondial.com\/waarom-uw-erp-implementatie-vertraagt-datamigratie-zien-als-it-in-plaats-van-opschoonproject\/"},"wordCount":1846,"publisher":{"@id":"https:\/\/www.datamondial.com\/#organization"},"image":{"@id":"https:\/\/www.datamondial.com\/waarom-uw-erp-implementatie-vertraagt-datamigratie-zien-als-it-in-plaats-van-opschoonproject\/#primaryimage"},"thumbnailUrl":"https:\/\/www.datamondial.com\/wp-content\/uploads\/2026\/07\/erp-implementation-stall-data-migration-cleanup-en-featured.jpg","articleSection":["Blog"],"inLanguage":"en-US"},{"@type":"WebPage","@id":"https:\/\/www.datamondial.com\/waarom-uw-erp-implementatie-vertraagt-datamigratie-zien-als-it-in-plaats-van-opschoonproject\/","url":"https:\/\/www.datamondial.com\/waarom-uw-erp-implementatie-vertraagt-datamigratie-zien-als-it-in-plaats-van-opschoonproject\/","name":"ERP Implementation: Prevent Data Migration Delays | DataMondial","isPartOf":{"@id":"https:\/\/www.datamondial.com\/#website"},"primaryImageOfPage":{"@id":"https:\/\/www.datamondial.com\/waarom-uw-erp-implementatie-vertraagt-datamigratie-zien-als-it-in-plaats-van-opschoonproject\/#primaryimage"},"image":{"@id":"https:\/\/www.datamondial.com\/waarom-uw-erp-implementatie-vertraagt-datamigratie-zien-als-it-in-plaats-van-opschoonproject\/#primaryimage"},"thumbnailUrl":"https:\/\/www.datamondial.com\/wp-content\/uploads\/2026\/07\/erp-implementation-stall-data-migration-cleanup-en-featured.jpg","datePublished":"2026-07-22T07:00:00+00:00","description":"Discover why ERP implementations stall. Learn how treating data migration as an operational cleanup rather than just an IT task prevents costly project delays.","breadcrumb":{"@id":"https:\/\/www.datamondial.com\/waarom-uw-erp-implementatie-vertraagt-datamigratie-zien-als-it-in-plaats-van-opschoonproject\/#breadcrumb"},"inLanguage":"en-US","potentialAction":[{"@type":"ReadAction","target":["https:\/\/www.datamondial.com\/waarom-uw-erp-implementatie-vertraagt-datamigratie-zien-als-it-in-plaats-van-opschoonproject\/"]}]},{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/www.datamondial.com\/waarom-uw-erp-implementatie-vertraagt-datamigratie-zien-als-it-in-plaats-van-opschoonproject\/#primaryimage","url":"https:\/\/www.datamondial.com\/wp-content\/uploads\/2026\/07\/erp-implementation-stall-data-migration-cleanup-en-featured.jpg","contentUrl":"https:\/\/www.datamondial.com\/wp-content\/uploads\/2026\/07\/erp-implementation-stall-data-migration-cleanup-en-featured.jpg","width":1376,"height":768,"caption":"Project manager reviews data mapping during ERP implementation to prevent data migration delays on a modern desk."},{"@type":"BreadcrumbList","@id":"https:\/\/www.datamondial.com\/waarom-uw-erp-implementatie-vertraagt-datamigratie-zien-als-it-in-plaats-van-opschoonproject\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/www.datamondial.com\/en\/"},{"@type":"ListItem","position":2,"name":"Why Your ERP Implementation is Stalling: Viewing Data Migration as an IT Task, Not a Data Cleanup Project"}]},{"@type":"WebSite","@id":"https:\/\/www.datamondial.com\/#website","url":"https:\/\/www.datamondial.com\/","name":"DataMondial","description":"","publisher":{"@id":"https:\/\/www.datamondial.com\/#organization"},"potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/www.datamondial.com\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"en-US"},{"@type":"Organization","@id":"https:\/\/www.datamondial.com\/#organization","name":"DataMondial","url":"https:\/\/www.datamondial.com\/","logo":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/www.datamondial.com\/#\/schema\/logo\/image\/","url":"https:\/\/www.datamondial.com\/wp-content\/uploads\/2022\/10\/datamondial_onderschrift.svg","contentUrl":"https:\/\/www.datamondial.com\/wp-content\/uploads\/2022\/10\/datamondial_onderschrift.svg","width":431,"height":94,"caption":"DataMondial"},"image":{"@id":"https:\/\/www.datamondial.com\/#\/schema\/logo\/image\/"},"sameAs":["https:\/\/www.linkedin.com\/company\/datamondial\/"]},{"@type":"Person","@id":"https:\/\/www.datamondial.com\/#\/schema\/person\/5438b776538ac7702fbaa3b85ebf463e","name":"Ralph van Es"}]}},"_links":{"self":[{"href":"https:\/\/www.datamondial.com\/en\/wp-json\/wp\/v2\/posts\/16547","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.datamondial.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.datamondial.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.datamondial.com\/en\/wp-json\/wp\/v2\/users\/10"}],"replies":[{"embeddable":true,"href":"https:\/\/www.datamondial.com\/en\/wp-json\/wp\/v2\/comments?post=16547"}],"version-history":[{"count":1,"href":"https:\/\/www.datamondial.com\/en\/wp-json\/wp\/v2\/posts\/16547\/revisions"}],"predecessor-version":[{"id":16549,"href":"https:\/\/www.datamondial.com\/en\/wp-json\/wp\/v2\/posts\/16547\/revisions\/16549"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.datamondial.com\/en\/wp-json\/wp\/v2\/media\/16545"}],"wp:attachment":[{"href":"https:\/\/www.datamondial.com\/en\/wp-json\/wp\/v2\/media?parent=16547"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.datamondial.com\/en\/wp-json\/wp\/v2\/categories?post=16547"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.datamondial.com\/en\/wp-json\/wp\/v2\/tags?post=16547"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}