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	<title>Adithya Buddhavarapu, Author at Datagaps | Gen AI-Powered Automated Cloud Data Testing</title>
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	<title>Adithya Buddhavarapu, Author at Datagaps | Gen AI-Powered Automated Cloud Data Testing</title>
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		<title>ERP Implementations Still Fail at Alarming Rates &#8211; Here&#8217;s Why Testing Automation With Robust Data Validation Is the Fix</title>
		<link>https://www.datagaps.com/blog/erp-implementation-failures-testing-automation-data-validation/</link>
					<comments>https://www.datagaps.com/blog/erp-implementation-failures-testing-automation-data-validation/#respond</comments>
		
		<dc:creator><![CDATA[Adithya Buddhavarapu]]></dc:creator>
		<pubDate>Thu, 04 Jun 2026 14:57:39 +0000</pubDate>
				<category><![CDATA[Cloud Data Migration]]></category>
		<category><![CDATA[Data Validation]]></category>
		<category><![CDATA[ETL Testing]]></category>
		<guid isPermaLink="false">https://www.datagaps.com/?p=49913</guid>

					<description><![CDATA[<p>Modern ERP transformations require a dual focus on testing automation and datavalidation to ensure quality, accuracy, and long-term system reliability. S/4HANA success is driven by a strong foundation built on both testing automation and data validation, ensuring processes run correctly and data drives the right decisions. I recently came across Godlan&#8217;s 2025 ERP Implementation Failure [&#8230;]</p>
<p>The post <a href="https://www.datagaps.com/blog/erp-implementation-failures-testing-automation-data-validation/">ERP Implementations Still Fail at Alarming Rates &#8211; Here&#8217;s Why Testing Automation With Robust Data Validation Is the Fix</a> appeared first on <a href="https://www.datagaps.com">Datagaps | Gen AI-Powered Automated Cloud Data Testing</a>.</p>
]]></description>
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									<p>Modern ERP transformations require a dual focus on testing automation and datavalidation to ensure quality, accuracy, and long-term system reliability.</p>								</div>
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									<p>S/4HANA success is driven by a strong foundation built on both testing automation and data validation, ensuring processes run correctly and data drives the right decisions.</p>								</div>
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									I recently came across Godlan&#8217;s <span style="text-decoration: underline;"><span style="color: #1967d2; text-decoration: underline;"><a style="color: #1967d2; text-decoration: underline;" href="https://godlan.com/erp-implementation-failure-statistics/" target="_blank" rel="noopener">2025 ERP Implementation Failure Statistics research</a></span></span>, and the numbers stopped me cold. Not because they were surprising — anyone who&#8217;s lived through a botched ERP rollout knows the pain — but because the industry keeps repeating the same mistakes, year after year, at an industrial scale.								</div>
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									<p>Let me walk you through what the data says, why it matters for anyone planning an SAP S/4HANA migration, and what I believe is the single most impactful lever to bend these failure curves: testing automation.</p>								</div>
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					<h2 class="elementor-heading-title elementor-size-default">The Numbers Are Brutal</h2>				</div>
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									Godlan&#8217;s research, drawing on Panorama Consulting Group&#8217;s 2025 ERP Report and 
Gartner analysis, paints a stark picture:								</div>
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					<h3 class="elementor-heading-title elementor-size-default">Industry-wide ERP implementation failure rates:</h3>				</div>
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									<p>• <strong>68%</strong> of ERP implementations fail to meet their objectives — and that&#8217;s theaverage <br />• <strong>73%</strong> failure rate for discrete manufacturing specifically <br />• <strong>189%</strong> average budget overrun across all industries <br />• <strong>215%</strong> budget overrun in discrete manufacturing <br />•<strong> 25–30%</strong> timeline extensions beyond original plans <br />• Only<strong> 27–32%</strong> of projects actually achieve their stated objectives</p>								</div>
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									That last number deserves a pause. Fewer than one in three ERP projects delivers what
was promised. And Gartner&#8217;s forward-looking analysis projects that 70% of ERP
implementations over the next three years will fail to meet objectives.								</div>
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									<p>These aren&#8217;t fringe projects failing. These are major enterprise investments often tensof millions of dollars that go sideways despite massive budgets, executive sponsorship, and vendor involvement.</p>								</div>
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					<h2 class="elementor-heading-title elementor-size-default">The Root Causes Are Predictable (and Preventable)</h2>				</div>
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									Godlan&#8217;s analysis of over 2,400 ERP implementations identified consistent failure patterns. The top root causes and their frequency:								</div>
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									<p>• <strong>Inadequate change management</strong> — 42% of failures <br />• <strong>Poor data migration</strong> — 38% <br />• <strong>Inexperienced implementation teams</strong> — 35% <br />• <strong>Lack of executive sponsorship</strong> — 31% <br />• <strong>Insufficient end-user training</strong> — 29% <br />•<strong> Scope creep</strong> — 26% <br />• <strong>Over-customization</strong> — 23% <br />• <strong>Vendor selection errors</strong> — 19%</p>								</div>
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									<p>The top three causes alone &#8211; change management, data migration, and team inexperience — account for over 75% of failures. And here&#8217;s what struck me: every single one of these failure modes is amplified by inadequate testing, and most of them are detectable through proper test automation before they become production crises.</p>								</div>
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					<h3 class="elementor-heading-title elementor-size-default">Think about it:</h3>				</div>
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									Poor data migration (38% of failures) is precisely the problem that automated data validation catches. When you&#8217;re moving hundreds of thousands of material master records, customer masters, vendor records, and BOMs from ECC to S/4HANA, manual spot-checking misses the long tail of data corruption, truncation, and transformation errors. Automated comparison scripts that verify source-to-target integrity field by field, table by table, catch what human eyes cannot. The Complexity Escalation Is Real								</div>
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									<p>One of the most useful frameworks in Godlan&#8217;s research is the business model risk analysis. Implementation risk doesn&#8217;t stay flat — it escalates dramatically based on operational complexity:</p>								</div>
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									<p>• <strong>Make-to-Stock</strong> — Medium risk (65/100) <br />• <strong>Make-to-Order</strong> — High risk (78/100) <br />• <strong>Configure-to-Order</strong> — Very High risk (85/100) <br />•<strong> Engineer-to-Order</strong> — Critical risk (92/100)</p>								</div>
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									<p>This matters enormously for SAP S/4HANA migrations. The more complex your manufacturing model, the more business logic is encoded in custom code, BOM structures, routing configurations, and pricing rules and the more surface area there is for migration defects.</p>								</div>
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									Manual testing simply cannot cover this surface area. A configure-to-order 
manufacturer might have thousands of configuration variants, each producing different 
BOMs and routing sequences. Testing even 5% of those combinations manually would 
take months. Automated parameterized tests can cover them in hours.								</div>
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					<h2 class="elementor-heading-title elementor-size-default">Testing Automation as the Common Denominator </h2>				</div>
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									<p>Testing automation has emerged as the common denominator across successful ERP implementations especially in complex S/4HANA transformations where speed, scale, and accuracy are critical. In modern implementations, it is most effective when consistently used along with data validation as a standard practice, not an option</p>								</div>
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									Here&#8217;s my thesis: testing automation doesn&#8217;t just address one root cause of ERP failure — it systematically mitigates the majority of them.								</div>
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									<p><strong>Accelerates project timelines</strong>, enabling rapid testing cycles alongside continuous data validation during iterative migrations</p><p><strong>Enables early detection of both system defects and data inconsistencies</strong>, preventing issues from reaching production</p><p><strong>Change management failures?</strong> Automated test suites demonstrate to end users and stakeholders that the new system works. They build confidence through evidence, not promises.</p><p><strong>Data migration failures?</strong> Automated source-to-target validation catches discrepancies at scale before go-live, not after. </p><p><strong>Inexperienced teams?</strong> A well-designed test automation framework provides guardrails. it encodes the business process knowledge that experienced consultants carry in their heads, making it available to the entire project team.<br /><br /><strong>Scope creep?</strong> Automated regression testing gives project leaders the confidence to say &#8220;the current scope works&#8221; and the data to evaluate whether proposed additions are worth the risk.<br /><strong><br />Over-customization?</strong> Automated tests that validate standard vs. custom behavior help teams identify where customization adds value vs. where it introduces risk. <br /><br />The organizations that beat the 68–73% failure rate aren&#8217;t doing anything exotic. They&#8217;re investing in structured, automated quality assurance from day one of the project not bolting it on at the end when everything is already on fire.</p>								</div>
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					<h2 class="elementor-heading-title elementor-size-default">The Cost of Inaction vs. the Cost of Automation</h2>				</div>
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									<p>Let&#8217;s put the Godlan numbers in financial context. If the average ERP implementation runs 189–215% over budget, and a mid-market SAP S/4HANA migration typically budgets $5–15 million, the overrun exposure is $9.5–32 million.</p>								</div>
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									<p>Meanwhile, a well-structured test automation initiative including tool licensing, framework development, and test creation typically runs 5–10% of total project budget and delivers ROI within 4–7 months.</p><p>The Forrester Total Economic Impact study on Tricentis SAP QA solutions documented 403% ROI over three years.</p>								</div>
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									<p>The asymmetry is stark: spend 5–10% upfront on automation to avoid 100–115% in cost overruns. That&#8217;s not a technology decision. That&#8217;s a fiduciary one.</p>								</div>
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					<h2 class="elementor-heading-title elementor-size-default">What Should You Do About It?</h2>				</div>
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									<p>If you&#8217;re planning, mid-flight, or recovering from an SAP S/4HANA migration, here&#8217;s what the data suggests:</p>								</div>
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							1. Treat testing as a first-class workstream, not a phase.						</span>
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						Testing should start in discovery and run continuously through hypercare. The organizations that succeed embed quality engineering from day one.					</p>
				
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							2. Automate data migration validation early. 						</span>
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						Don't wait until your third mock 
migration to discover that 20% of your material masters are corrupted. Build 
automated comparison scripts after your first test load.					</p>
				
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							3. Invest in end-to-end process automation, not just unit tests.						</span>
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						The defects that kill ERP go-lives aren't syntax errors — they're cross-module process failures.  Order-to-cash, procure-to-pay, plan-to-produce: these need automated end-to
end coverage.					</p>
				
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							4. Build the regression suite as a permanent asset. 						</span>
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						S/4HANA updates come faster than ECC. The regression suite you build during migration becomes your insurance policy for every future release.					</p>
				
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							5. Choose implementation partners with testing DNA.						</span>
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						The Godlan research is clear: inexperienced teams are a top-three failure driver. Your implementation partner should have a proven test automation methodology, not a slide deck about one.					</p>
				
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					<h2 class="elementor-heading-title elementor-size-default">Final Thought</h2>				</div>
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									<p>The ERP implementation failure statistics haven&#8217;t improved meaningfully in a decade. The industry keeps building billion-dollar systems and testing them with spreadsheets and hope. The organizations that break the pattern are the ones that treat quality as <br />infrastructure &#8211; automated, repeatable, and non-negotiable.</p>								</div>
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									Testing automation with data validation is not optionalit is critical in S/4HANA because:								</div>
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									<p>• <strong>Systems are real-time and highly integrated,</strong> requiring both automated testing and validated data to ensure accuracy across processes</p><p>• <strong>Errors directly affect business operations,</strong> making it essential to validate both system behavior and the data driving it<br /><br />• <strong>Fixing issues later is costly,</strong> especially when both defects and data inconsistencies are embedded in production<br /><br />• <strong>Clean, validated data combined with automated testing</strong> ensures a successful and stable transformation</p>								</div>
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									<p>Testing automation with data validation creates a controlled and reliable environment where both system functionality and data accuracy are continuously verified across every stage of the S/4HANA migration. </p>								</div>
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									<p>&#8220;In S/4HANA, testing automation with data validation is not just a technical requirement &#8211; it is a business-critical discipline that directly determines the success or failure of the entire implementation&#8221;.</p><p>The data is clear. The question is whether you&#8217;ll act on it.</p>								</div>
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									<p><em>Statistics referenced from Godlan&#8217;s <span style="text-decoration: underline;"><span style="color: #1967d2; text-decoration: underline;"><a style="color: #1967d2; text-decoration: underline;" href="https://godlan.com/erp-implementation-failure-statistics/" target="_blank" rel="noopener">2025 ERP Implementation Failure Statistics research</a></span></span>: citing Panorama Consulting Group&#8217;s 2025 ERP Report and Gartner analysis.</em></p>								</div>
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		<p>The post <a href="https://www.datagaps.com/blog/erp-implementation-failures-testing-automation-data-validation/">ERP Implementations Still Fail at Alarming Rates &#8211; Here&#8217;s Why Testing Automation With Robust Data Validation Is the Fix</a> appeared first on <a href="https://www.datagaps.com">Datagaps | Gen AI-Powered Automated Cloud Data Testing</a>.</p>
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		<title>Testing Automation of Material Master in SAP During Migration to S/4HANA</title>
		<link>https://www.datagaps.com/blog/sap-material-master-migration-testing-automation-s4hana/</link>
					<comments>https://www.datagaps.com/blog/sap-material-master-migration-testing-automation-s4hana/#respond</comments>
		
		<dc:creator><![CDATA[Adithya Buddhavarapu]]></dc:creator>
		<pubDate>Thu, 04 Jun 2026 14:54:51 +0000</pubDate>
				<category><![CDATA[Cloud Data Migration]]></category>
		<category><![CDATA[Data Validation]]></category>
		<category><![CDATA[ETL Testing]]></category>
		<guid isPermaLink="false">https://www.datagaps.com/?p=49939</guid>

					<description><![CDATA[<p>The clock is ticking. SAP&#8217;s 2027 mainstream maintenance deadline for ECC is driving a massive wave of S/4HANA migrations, with 59% of companies now fully or partially live on S/4HANA as of late 2025 — up 13 points from 2024. Yet one of the most underestimated risks in every migration sits quietly in the background: [&#8230;]</p>
<p>The post <a href="https://www.datagaps.com/blog/sap-material-master-migration-testing-automation-s4hana/">Testing Automation of Material Master in SAP During Migration to S/4HANA</a> appeared first on <a href="https://www.datagaps.com">Datagaps | Gen AI-Powered Automated Cloud Data Testing</a>.</p>
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									<p>The clock is ticking. SAP&#8217;s 2027 mainstream maintenance deadline for ECC is driving a massive wave of S/4HANA migrations, with 59% of companies now fully or partially live on S/4HANA as of late 2025 — up 13 points from 2024. Yet one of the most underestimated risks in every migration sits quietly in the background: the Material Master.</p>								</div>
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									<p>Material Master isn&#8217;t glamorous. It doesn&#8217;t get keynote stage time. But it touches everything — procurement, inventory, sales, production planning, quality management, finance. A single data inconsistency in your MARA or MARC tables can cascade through your entire supply chain on day one of go-live. And when you&#8217;re migrating hundreds of thousands (or millions) of material records from ECC to S/4HANA, manual testing simply doesn&#8217;t scale.</p>								</div>
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									<p>This blog lays out why Material Master testing automation is non-negotiable during S/4HANA migration, what changes in the data model demand it, and how to approach it practically.</p>								</div>
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					<h2 class="elementor-heading-title elementor-size-default">Why Material Master Is the Migration Minefield</h2>				</div>
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									<p>Material Master is often called a “<strong>migration minefield</strong>” because it is one of the <strong>most complex, interdependent, and business-critical data objects in SAP</strong>. Even small inconsistencies can cascade into major operational issues across procurement, production, sales, and finance.</p>								</div>
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									<p><span style="text-decoration: underline;"><span style="color: #1967d2; text-decoration: underline;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.sap.com/" target="_blank" rel="noopener">In SAP</a></span></span>, a material master is not a single entity but a collection of multiple views including Basic Data, Sales, Purchasing, MRP, Plant Data, Storage Location, Accounting, Costing, and Quality Management. Each view aligns with specific organizational levels and is supported by different underlying tables, creating a highly distributed data structure. This multi-dimensional complexity makes material master data one of the most sensitive and error-prone areas during migration.</p>								</div>
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					<h3 class="elementor-heading-title elementor-size-default">During an S/4HANA migration, several things change simultaneously: </h3>				</div>
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									<p>The data model <strong>has fundamentally shifted in S/4HANA.</strong> While the core Material Master tables (MARA, MARC, MARD, MBEW) still exist, <strong>they are no longer always the primary source of truth for transactional data.</strong> Inventory quantities in tables like <strong>MARD are now derived rather than persistently stored for reporting purposes</strong> when a material document is posted.</p>								</div>
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									<p>Instead, stock values are <strong>calculated in real time using the MATDOC table and accessed via CDS views.</strong> The old aggregate and index tables <strong>have been removed as part of the S/4HANA data simplification initiative </strong>and replaced by CDS view proxies.</p>								</div>
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									<p>This means any custom code or reports that read stock fields from MARD or MARC<strong> may now retrieve data through compatibility views or CDS layers rather than direct physical storage,</strong> and <strong>the performance behavior, data accuracy, and read patterns have fundamentally changed.</strong></p>								</div>
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							The Business Partner migration complicates vendor relationships.						</span>
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						In ECC, vendor masters lived separately. In S/4HANA, they're merged into the Business Partner framework. Material Master records with vendor-specific info (source lists, purchasing info records, quota arrangements) need their vendor references reconciled against the new BP structure. This is a cross-domain dependency that's easy to miss in isolated Material Master testing.					</p>
				
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						Most ECC systems are heavily customized. Custom fields appended to MARA, MARC, or MBEW need to be carried forward through the S/4HANA Migration Cockpit (LTMC/LTMOM) using BAPI extension structures like BAPI_TE_E1MARA and BAPI_TE_E1MARC. If the field selection group assignments (T-code OMSR) aren't configured correctly, data simply won't make it to the target database. This is the kind of silent failure that only shows up if you're testing at scale.					</p>
				
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							Data quality issues that were tolerable in ECC become blockers in S/4HANA.						</span>
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						 Duplicate materials, incomplete mandatory fields, mismatched units of measure, inconsistent material type assignments — all of these can cause the SUM/DMO conversion process to fail or produce corrupt records. One global food manufacturer found a 20% duplication rate in their Material Master during pre-migration audit.					</p>
				
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									Testing Material Master during an S/4HANA migration isn&#8217;t a single activity. It spans multiple test types, each of which benefits enormously from automation:								</div>
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							1. Data Migration Validation						</span>
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						This is the most obvious layer: verifying that every material record migrated correctly from ECC to S/4HANA. 					</p>
				
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									<strong>For automated testing, this means</strong>								</div>
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									<p>• Record count reconciliation across source (ECC) and target (S/4HANA) for every Material Master table — MARA, MARC, MARD, MBEW, MAKT, MVKE, and custom extensions.</p><p>• Field-by-field comparison for a statistically significant sample (or ideally all records), checking that values in every view transferred accurately.</p><p>• Checksum validation helps detect subtle data issues such as truncated descriptions, character encoding problems in the 40-character MAKTX field, and unit of measure mismatches.<br /><br />• Cross-referencing material-to-vendor relationships against the migrated Business Partner records.<br /><br />• <strong>Material type and valuation class validation</strong>, ensuring correct account determination and financial postings in S/4HANA.<br /><br />• Validation of custom (Z) fields through BAPI extension structures, confirming that enhancements in MARA/MARC are correctly populated in the target system.<br /><br />• Integration validation with dependent objects, such as pricing conditions, BOMs, and purchasing info records, to ensure materials function correctly in end-to end processes.<br /><br />• Data completeness checks, ensuring mandatory fields required in S/4HANA (e.g., Business Partner linkage, valuation data) are not missing.</p>								</div>
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									Automating this with tools like Tricentis Tosca, SAP CBTA, or even purpose-built SQL/ABAP comparison scripts can reduce what would be weeks of manual spot checking into hours of comprehensive, repeatable validation.								</div>
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									Once the data lands in S/4HANA, does it actually work? Can you create a purchase order for a migrated material? Does MRP run correctly against the migrated plant data? Does the material show up in Fiori apps the way users expect?								</div>
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									<p>Functional regression for Material Master means automating end-to-end business process scenarios that exercise the migrated data:</p>								</div>
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									<p>•<strong> Procure-to-Pay (P2P): </strong>Create a purchase requisition → convert to PO → goods receipt → invoice verification, all using migrated materials</p><p><strong>• Order-to-Cash (O2C):</strong> Create a sales order → delivery → billing using migrated materials with sales org data</p><p><strong>• Plan-to-Produce:</strong> Run MRP for migrated materials, verify planned orders, confirm production orders</p><p><strong>• Inventory Management:</strong> Post goods movements (MIGO) for migrated materials, verify stock levels in the new MATDOC-based data model.</p><p><strong>• Account determination validation,</strong> confirming that goods movements and invoices post correctly to the right GL accounts based on valuation class and material type.</p><p><strong>• Cross-module integration validation,</strong> ensuring material data works consistently across MM, SD, PP, and FI without breaks in data flow.</p><p>• <strong>Fiori app validation and user behavior checks,</strong> confirming that migrated materials appear correctly in apps like Manage Product Master Data, Stock Overview, and Create Purchase Order</p><p><strong>• Warehouse and storage integration validation,</strong> ensuring materials function properly with WM/EWM processes, including bin determination and stock placement</p><p><strong>• Tax and compliance validation,</strong> confirming that materials trigger correct tax codes and localization logic across regions</p><p><strong>• Batch management and serial number validation,</strong> ensuring batch-controlled or serialized materials behave correctly in procurement, production, and delivery processes</p><p><strong>• Availability check (ATP) validation,</strong> verifying that stock availability and confirmation logic work correctly with migrated inventory data</p>								</div>
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									<p>These scenarios should be scripted and parameterized so they can run against hundreds of representative materials, not just the three or four that someone happened to pick for manual testing.</p>								</div>
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						S/4HANA's Simplification List identifies thousands of changes that affect custom ABAP code. For Material Master specifically, any custom code that directly reads from deprecated tables, uses obsolete function modules, or references fields that have been removed or repurposed needs to be identified and tested.					</p>
				
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									Automated custom code scanning (using SAP&#8217;s Custom Code Migration app or the ATC checks in Eclipse/ADT) should be followed by automated functional tests of every Z program, Z-report, and user exit that touches Material Master data. The goal is to catch the programs that pass the static code check but still produce wrong results because of the changed data model semantics.								</div>
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							4. Performance Testing						</span>
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						This is the layer most teams skip — and pay for dearly after go-live. The shift from statically maintained stock fields to dynamically calculated CDS views means that transactions and reports reading MARD or MARC stock data will behave differently under load. A report that ran in 3 seconds in ECC against pre-aggregated stock tables might take 30 seconds in S/4HANA if the MATDOC table has millions of entries and the CDS view stack isn't optimized. 					</p>
				
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									<p>Automated performance testing should simulate realistic transaction volumes for key Material Master operations: mass material creation (MM01/API), MRP runs across plant level data, stock overview queries (MMBE), and batch material document postings. Identify the performance cliffs before your users find them.</p>								</div>
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					<h2 class="elementor-heading-title elementor-size-default">Building the Automation Framework</h2>				</div>
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									Here&#8217;s a practical approach to structuring Material Master test automation for an S/4HANA migration:								</div>
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									<strong>Phase 1: Pre-Migration (ECC Side)</strong> Extract baseline data from ECC Material Master
tables. Build automated comparison datasets. Identify the full inventory of custom
fields, custom code, and cross-module dependencies. This is your &#8220;source of truth&#8221;
snapshot.								</div>
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									<p><strong>Phase 2: Mock Migration Cycles</strong> Run the migration (via Migration Cockpit or SUM/DMO) in a sandbox environment. Execute the full automated test suite &#8211; <span style="text-decoration: underline;"><span style="color: #1967d2; text-decoration: underline;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/dataops-suite/" target="_blank" rel="noopener">data validation</a></span></span>, functional regression, custom code validation. Log every discrepancy. Fix, re-migrate, re-test. This cycle typically runs 3–5 times before the data and configuration are clean enough for dress rehearsal.</p>								</div>
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									<p><strong>Phase 3: Dress Rehearsal / Mock Cutover</strong> Full-scale migration in a production-mirror environment. Complete automated test suite plus performance testing under simulated production load. This is where you validate not just data correctness but also cutover timing and rollback procedures.</p>								</div>
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									<p><strong>Phase 4: Go-Live Validation</strong> Smoke test suite runs immediately post-cutover. Automated checks confirm record counts, critical material availability, and key transaction execution. Any failures trigger the rollback decision.</p>								</div>
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									<p><strong>Phase 5: Hypercare Regression</strong> Continuous automated regression during the first 2–4 weeks post-go-live, catching issues that emerge as users interact with migrated data in real business scenarios. SAP delivers S/4HANA updates at a faster cadence than ECC, so the regression suite you build here becomes a permanent asset.</p>								</div>
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									<p>For <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/data-migration-testing-automation/" target="_blank" rel="noopener">data migration validation</a></span> specifically, purpose-built SQL comparison scripts (running against both ECC and S/4HANA databases) or tools like Precisely&#8217;s Automate Evolve can validate millions of records with checksum and business-rule logic that goes beyond simple row counting.</p>								</div>
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					<h2 class="elementor-heading-title elementor-size-default">The Cost of Not Automating</h2>				</div>
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									<p>The math is straightforward. A typical mid-size manufacturer has 200,000+ material records across dozens of plants. Each record has 15–20 views. Manual testing of even 1% of records across all views would take months. And a single missed defect &#8211; a wrong unit of measure in a purchasing view, a missing MRP profile at one plant &#8211; can halt production lines or create procurement chaos on day one.</p>								</div>
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									<p>The 2026 ASUG/Precisely survey found that 49% of organizations cite business process change as their top migration barrier, and data quality emerged as a critical but often overlooked challenge. Automation doesn&#8217;t just accelerate testing &#8211; it&#8217;s the only way to achieve the coverage required to de-risk a Material Master migration at enterprise scale.</p>								</div>
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		<p>The post <a href="https://www.datagaps.com/blog/sap-material-master-migration-testing-automation-s4hana/">Testing Automation of Material Master in SAP During Migration to S/4HANA</a> appeared first on <a href="https://www.datagaps.com">Datagaps | Gen AI-Powered Automated Cloud Data Testing</a>.</p>
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		<title>Leveraging DataOps Suite to comply with BCBS, SOC2 and other regulations</title>
		<link>https://www.datagaps.com/blog/leveraging-dataops-suite-to-comply-with-bcbs-soc2-and-other-regulations/</link>
		
		<dc:creator><![CDATA[Adithya Buddhavarapu]]></dc:creator>
		<pubDate>Wed, 20 Apr 2022 11:59:53 +0000</pubDate>
				<category><![CDATA[DataOps]]></category>
		<guid isPermaLink="false">https://staging9.datagaps.com/?p=7382</guid>

					<description><![CDATA[<p>Many industries are regulated and have to adhere to a set of guidelines and industry standards. While we are a software company, we have been learning more about these standards and how our software is helping the financial industry adhere to the regulations.</p>
<p>The post <a href="https://www.datagaps.com/blog/leveraging-dataops-suite-to-comply-with-bcbs-soc2-and-other-regulations/">Leveraging DataOps Suite to comply with BCBS, SOC2 and other regulations</a> appeared first on <a href="https://www.datagaps.com">Datagaps | Gen AI-Powered Automated Cloud Data Testing</a>.</p>
]]></description>
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									<p>Many industries are regulated and have to adhere to a set of guidelines and industry standards. While we are a software company, we have been learning more about these standards and how our software is helping the financial industry adhere to the regulations.</p>								</div>
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									<p>Two of the standards we have been hearing about in the recent past are mentioned below:</p><ul><li><strong>The Systems and Organizational Controls (SOC) 2</strong></li><li><strong>Basel Committee on Banking Supervision’s standard number 239</strong></li></ul>								</div>
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									<p><span style="text-decoration: underline;"><strong>The Systems and Organizational Controls (SOC) 2,</strong></span></p><p>A comprehensive reporting framework was set forth by the American Institute of Chartered Public Accountants (ACPA). As part of this framework, organizations should engage independent auditors to execute a series of assessments to test the controls related to Trust Services Criteria (TSC) such as Security, Availability, <strong>Processing Integrity</strong>, Confidentiality, or Privacy.</p><p>From our perspective at Datagaps, achieving Processing Integrity is where we can add value and thus explaining our understanding below.</p><p><strong>The Processing Integrity component has 6 criteria.</strong></p><ul><li>Procedures exist to prevent, or detect and correct, processing errors to meet the entity’s processing integrity commitments and system requirements.</li></ul>								</div>
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									<ul><li>System inputs are measured and recorded completely, accurately, and time to meet the entity’s processing integrity commitments and system requirements.</li><li>Data is processed completely, accurately, and timely as authorized to meet the entity’s processing integrity commitments and system requirements.</li><li>Data is stored and maintained completely, accurately, and in a timely manner for its specified life span to meet the entity’s processing integrity commitments and system requirements.</li><li>System output is complete, accurate, and distributed to meet the entity’s processing integrity commitments and system requirements.</li><li>Modification of data, other than routine transaction processing, is authorized and processed to meet the entity’s processing integrity commitments and system requirements.</li></ul>								</div>
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									<p><strong>After the Global Financial Crisis in 2007-2008</strong>, one of the key lessons learned was that the Data Management frameworks and IT architectures in place at many banks at that time were inadequate to support and report risks associated with global banking. In lieu of that, the <strong>Basel Committee on Banking Supervision’s standard number 239</strong> was issued as principles for effective risk data aggregation and Risk Reporting.</p>								</div>
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									<p>These are <span style="text-decoration: underline;"><span style="color: #1967d2; text-decoration: underline;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.bis.org/publ/bcbs239.pdf" target="_blank" rel="noopener">14 principles</a></span></span> that were laid out to strengthen banks’ risk data aggregation capabilities and internal risk reporting practices and they fall under the following categories.</p>								</div>
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									<ul><li><strong>Overarching Governance and infrastructure</strong> – Principle 1 &amp; 2</li><li><strong>Risk Data Aggregation Capabilities – </strong>Principles 3 through 6</li></ul>								</div>
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									<p>In this blog, we will focus on few aspects of Principles 3 and 7 since they are the areas which are relevant from our perspective at Datagaps. If you want a complete description of the principles, please refer to this <span style="text-decoration: underline;"><span style="color: #1967d2; text-decoration: underline;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.bis.org/publ/bcbs239.pdf" target="_blank" rel="noopener">link</a></span></span>.</p>								</div>
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									<div id="elementor-tab-title-1421" class="elementor-tab-title elementor-tab-desktop-title" aria-selected="true" data-tab="1" role="tab" tabindex="0" aria-controls="elementor-tab-content-1421" aria-expanded="false">Principle 3</div>
									<div id="elementor-tab-title-1422" class="elementor-tab-title elementor-tab-desktop-title" aria-selected="false" data-tab="2" role="tab" tabindex="-1" aria-controls="elementor-tab-content-1422" aria-expanded="false">Principle 7:</div>
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									<div class="elementor-tab-title elementor-tab-mobile-title" aria-selected="true" data-tab="1" role="tab" tabindex="0" aria-controls="elementor-tab-content-1421" aria-expanded="false">Principle 3</div>
					<div id="elementor-tab-content-1421" class="elementor-tab-content elementor-clearfix" data-tab="1" role="tabpanel" aria-labelledby="elementor-tab-title-1421" tabindex="0" hidden="false"><p><strong>Accuracy and Integrity</strong> A bank should be able to generate accurate and reliable risk data to meet normal and stress/crisis reporting accuracy requirements. Data should be aggregated on a largely automated basis so as to minimize the probability of errors.</p><ul><li>Where a bank relies on manual processes and desktop applications (eg<br />spreadsheets, databases) and has specific risk units that use these applications for software development, it should have effective mitigants in place (eg end-user computing policies and procedures) and other effective controls that are consistently applied across the bank’s processes.</li><li>Risk data should be reconciled with the bank’s sources, including accounting data<br />where appropriate, to ensure that the risk data is accurate</li><li>There should be an appropriate balance between automated and manual systems. Where professional judgments are required, human intervention may be appropriate. A higher degree of automation is desirable to reduce the risk of errors.</li></ul></div>
									<div class="elementor-tab-title elementor-tab-mobile-title" aria-selected="false" data-tab="2" role="tab" tabindex="-1" aria-controls="elementor-tab-content-1422" aria-expanded="false">Principle 7:</div>
					<div id="elementor-tab-content-1422" class="elementor-tab-content elementor-clearfix" data-tab="2" role="tabpanel" aria-labelledby="elementor-tab-title-1422" tabindex="0" hidden="hidden"><p><strong>Accuracy</strong>: Risk management reports should accurately and precisely convey<br />aggregated risk data and reflect risk in an exact manner. Reports should be reconciled<br />and validated.</p><ol><li>Risk management reports should be accurate and precise to ensure a bank’s board and senior management can rely with confidence on the aggregated information to make critical decisions about risk.</li><li>To ensure the accuracy of the reports, a bank should maintain, at a minimum, the following</li></ol><ul><li>Defined requirements and processes to reconcile reports to risk data;</li><li>Automated and manual edit and reasonableness checks, including an inventory of the validation rules that are applied to quantitative information. The inventory should include explanations of the conventions used to describe any mathematical or logical relationships that should be verified through these validations or checks;</li><li>Integrated procedures for identifying, reporting, and explaining data errors or weaknesses in data integrity via exceptions reports</li></ul></div>
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									<ul><li><strong>Risk Reporting Practices</strong> – Principles 7 through 11</li><li><strong>Supervisory Review tools and Cooperation – </strong>Principles 12 through 14</li></ul>								</div>
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									<p>As we are collaborating more with the financial industry, we are learning that while the guidelines are fairly clear, the actual implementation is complex and there are no clear tools and implementation strategies to help banks achieve these principles. In this blog,  we want to delve a bit deeper with a concrete example of how our software can help achieve the Process Integrity required for the audits and enable trust in enterprise data and systems.</p>								</div>
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									<p>Consider General Ledger (GL), one of the most important elements in the Accounting world. Data is entered manually and automatically into the GL and oftentimes data is extracted from the GL into Enterprise Reporting systems or other downstream systems because the GL is a treasure trove of valuable information from Accounts Receivable, Accounts Payable, Purchasing, Payroll, and the sub-ledgers within the enterprise as shown in the image.</p>								</div>
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															<img decoding="async" width="640" height="453" src="https://www.datagaps.com/wp-content/uploads/Automated-Data-Entry-from-Other-Systems-1024x724.webp" class="attachment-large size-large wp-image-6336" alt="Automated-Data-Entry-from-Other-Systems" srcset="https://www.datagaps.com/wp-content/uploads/Automated-Data-Entry-from-Other-Systems-1024x724.webp 1024w, https://www.datagaps.com/wp-content/uploads/Automated-Data-Entry-from-Other-Systems-300x212.webp 300w, https://www.datagaps.com/wp-content/uploads/Automated-Data-Entry-from-Other-Systems-768x543.webp 768w, https://www.datagaps.com/wp-content/uploads/Automated-Data-Entry-from-Other-Systems-1536x1086.webp 1536w, https://www.datagaps.com/wp-content/uploads/Automated-Data-Entry-from-Other-Systems-2048x1448.webp 2048w" sizes="(max-width: 640px) 100vw, 640px" />															</div>
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									<p>If anything goes wrong with the GL, it can have huge implications within the enterprise because if GL is not trustworthy, then, the reporting will not be trustworthy as well and wrong numbers might get accidentally reported to Wall Street because which organizations have to pay huge fines to governing entities.</p>								</div>
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									<p>Now, the question is how and where can something go wrong with the process integrity when it comes to General Ledger? To answer this question, let’s take a look at a potential file output from the GL system in Oracle. As mentioned in the <span style="text-decoration: underline;"><span style="color: #1967d2; text-decoration: underline;"><a style="color: #1967d2; text-decoration: underline;" href="https://docs.oracle.com/cd/E69185_01/cwdirect/pdf/180/cwdirect_user_reference/SO02_09.htm#Ravr95793" target="_blank" rel="noopener">Oracle Documentation</a></span></span>,  a GL file can be imported or extracted in multiple formats through automated scheduler jobs such as AutoSys and one such format (IXGLDN) has the below columns.</p>								</div>
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									<table style="border-style: solid; border-color: Blue; color: #000000;" border="1" cellpadding="10"><tbody><tr><td style="width: 129px;"><p style="text-align: justify;"><strong>Field Name</strong></p></td><td style="text-align: justify; width: 163px;"><strong>Attributes</strong></td><td style="text-align: justify; width: 932px;"><strong>Field Description/Contents</strong></td></tr><tr style="text-align: justify;"><td style="width: 129px;"><strong>Run group</strong></td><td style="text-align: left; width: 163px;"><em>Alphanumeric, 12 positions</em></td><td style="width: 932px;">Composed of <strong>CW</strong> + the date the file was populated, in CCYYMMDD format. For example, the run group for a record created on February 19, 2003 is <strong>CW20030219</strong>.</td></tr><tr style="text-align: justify;"><td style="width: 129px;"><strong>Sequence number</strong></td><td style="text-align: left; width: 163px;"><em>Numeric, 6 positions</em></td><td style="width: 932px;">A unique sequence number assigned by the system to each record each time you generate the download file.</td></tr><tr style="text-align: justify;"><td style="width: 129px;"><strong>Company</strong></td><td style="text-align: left; width: 163px;"><em>Numeric, 4 positions</em></td><td style="width: 932px;">The first 4 positions of the <em>Cross reference number</em> field in<span style="color: #1967d2;"><span style="text-decoration: underline;"> <a style="color: #1967d2; text-decoration: underline;" href="https://docs.oracle.com/cd/E69185_01/cwdirect/pdf/180/cwdirect_user_reference/AP01_04.htm#Rass28130" target="_blank" rel="noopener">Working with Chart of Accounts (WCA)</a></span>.</span> If the first 4 positions include a non-numeric character, such as a hyphen (-) the system skips this character and takes the first 4 numeric values. For example, if the <em>Cross reference number</em> is <strong>100-10-12345</strong>, the <em>Company</em> will be <strong>1000</strong>. If the <em>Cross reference number</em> field does not contain numeric characters, the <em>Company</em> field will not be populated consistently.</td></tr><tr style="text-align: justify;"><td style="width: 129px;"><strong>Company old</strong></td><td style="text-align: left; width: 163px;"><em>Alphanumeric, 31 positions</em></td><td style="width: 932px;">The first 11 positions of the <em>Cross reference number</em> field in Work with Chart of Accounts (fast path = <strong>WCA</strong>).</td></tr><tr style="text-align: justify;"><td style="width: 129px;"><strong>Account number old</strong></td><td style="width: 163px; text-align: left;"><em>Alphanumeric, 31 positions</em></td><td style="width: 932px;">Positions 12-19 of the <em>Cross reference number</em> field in Work with Chart of Accounts (fast path = <strong>WCA</strong>).</td></tr><tr style="text-align: justify;"><td style="width: 129px;"><strong>Source code</strong></td><td style="width: 163px; text-align: left;"><em>Alphanumeric, 1 position</em></td><td style="width: 932px;">The <em>Journal source</em> field from the General Ledger Interface file, indicating the type of transaction that generated the posting. See Accounts Payable <span style="text-decoration: underline;"><a href="https://docs.oracle.com/cd/E69185_01/cwdirect/pdf/180/cwdirect_user_reference/AP_APPB.htm#Ratxdgli" target="_blank" rel="noopener">Chapter 41: Displaying the General Ledger Interface (DGLI)</a></span> for a list of journal source codes.</td></tr><tr style="text-align: justify;"><td style="width: 129px;"><strong>Transaction date</strong></td><td style="width: 163px; text-align: left;"><em>Numeric, 8 positions (CCYYMMDD format)</em></td><td style="width: 932px;">The date this record was downloaded to the General Ledger Download file. For example, the transaction date for a record created on February 19, 1999 is <strong>19990219</strong>.</td></tr><tr style="text-align: justify;"><td style="width: 129px;"><strong>Reference</strong></td><td style="width: 163px; text-align: left;"><em>Alphanumeric, 10 positions</em></td><td style="width: 932px;">Composed of <strong>CW</strong> + the date the download file was created. For example, the <em>Reference</em> for a record created on February 19, 1999 is <strong>CW19990219</strong>.</td></tr><tr style="text-align: justify;"><td style="width: 129px;"><strong>Description</strong></td><td style="width: 163px; text-align: left;"><em>Alphanumeric, 30 positions</em></td><td style="width: 932px;">Same as the <em>Reference</em> field.</td></tr><tr style="text-align: justify;"><td style="width: 129px;"><strong>Currency code</strong></td><td style="width: 163px; text-align: left;"><em>Alphanumeric, 5 positions</em></td><td style="width: 932px;">Blank</td></tr><tr style="text-align: justify;"><td style="width: 129px;"><strong>Units amount</strong></td><td style="width: 163px; text-align: left;"><em>Numeric, 15 positions with a 2-place decimal</em></td><td style="width: 932px;">0.00</td></tr><tr style="text-align: justify;"><td style="width: 129px;"><strong>Units amount sign</strong></td><td style="width: 163px; text-align: left;"><em>Alphanumeric, 1 position</em></td><td style="width: 932px;">Blank</td></tr><tr style="text-align: justify;"><td style="width: 129px;"><strong>Transaction amount</strong></td><td style="width: 163px; text-align: left;"><em>Numeric, 15 positions with a 2-place decimal</em></td><td style="width: 932px;">The <em>Transaction amount</em> field from the General Ledger Interface file.</td></tr><tr style="text-align: justify;"><td style="width: 129px;"><strong>Transaction amount sign</strong></td><td style="width: 163px; text-align: left;"><em>Alphanumeric, 1 position</em></td><td style="width: 932px;"><p>+ if the transaction amount is positive.</p><p>– if the transaction amount is negative.</p></td></tr><tr style="text-align: justify;"><td style="width: 129px;"><strong>Base amount</strong></td><td style="width: 163px; text-align: left;"><em>Numeric, 15 positions with a 2-place decimal</em></td><td style="width: 932px;">The <em>transaction amount</em> field from the General Ledger Interface file.</td></tr><tr style="text-align: justify;"><td style="width: 129px;"><strong>Base amount sign</strong></td><td style="width: 163px; text-align: left;"><em>Alphanumeric, 1 position</em></td><td style="width: 932px;"><p>+ if the transaction amount is positive.</p><p>– if the transaction amount is negative.</p></td></tr><tr style="text-align: justify;"><td style="width: 129px;"><strong>Base rate</strong></td><td style="width: 163px; text-align: left;"><em>Numeric, 12 positions with a 6-place decimal</em></td><td style="width: 932px;">.000000</td></tr><tr style="text-align: justify;"><td style="width: 129px;"><strong>System</strong></td><td style="width: 163px; text-align: left;"><em>Alphanumeric, 2 positions</em></td><td style="width: 932px;">CW</td></tr><tr style="text-align: justify;"><td style="width: 129px;"><strong>Program code</strong></td><td style="width: 163px; text-align: left;"><em>Alphanumeric, 5 positions</em></td><td style="width: 932px;">Blank</td></tr><tr style="text-align: justify;"><td style="width: 129px;"><strong>Auto rev</strong></td><td style="width: 163px; text-align: left;"><em>Alphanumeric, 1 position</em></td><td style="width: 932px;">N</td></tr><tr style="text-align: justify;"><td style="width: 129px;"><strong>Posting date</strong></td><td style="width: 163px; text-align: left;"><em>Numeric, 8 positions (CCYYMMDD format)</em></td><td style="width: 932px;">The date of the transaction from the General Ledger Interface file, converted into 8-position format. For example, the posting date for a record from February 19, 1999 is <strong>19990219</strong>.</td></tr><tr style="text-align: justify;"><td style="width: 129px;"><strong>Activity</strong></td><td style="width: 163px; text-align: left;"><em>Alphanumeric, 15 positions</em></td><td style="width: 932px;">Blank</td></tr><tr style="text-align: justify;"><td style="width: 129px;"><strong>Account category</strong></td><td style="width: 163px; text-align: left;"><em>Alphanumeric, 5 positions</em></td><td style="width: 932px;">Blank</td></tr><tr style="text-align: justify;"><td style="width: 129px;"><strong>Document number</strong></td><td style="width: 163px; text-align: left;"><em>Alphanumeric, 15 positions</em></td><td style="width: 932px;"><strong>CW</strong> + the date the download file was created + the sequence number for this record. For example, the document number for the first record created on February 19, 1999 is <strong>CW19990219000001</strong>.</td></tr><tr style="text-align: justify;"><td style="width: 129px;"><strong>To base amount</strong></td><td style="width: 163px; text-align: left;"><em>Numeric, 15 positions with a 2-place decimal</em></td><td style="width: 932px;"><strong>1.00</strong></td></tr><tr style="text-align: justify;"><td style="width: 129px;"><strong>To base amount Sign</strong></td><td style="width: 163px; text-align: left;"><em>Alphanumeric, 1 position</em></td><td style="width: 932px;">+</td></tr><tr style="text-align: justify;"><td style="width: 129px;"><strong>Effect date</strong></td><td style="width: 163px; text-align: left;"><em>Numeric, 8 positions (CCYYMMDD format)</em></td><td style="width: 932px;"><strong>0</strong></td></tr><tr style="text-align: justify;"><td style="width: 129px;"><strong>Journal book number</strong></td><td style="width: 163px; text-align: left;"><em>Alphanumeric, 12 positions</em></td><td style="width: 932px;">Blank</td></tr><tr style="text-align: justify;"><td style="width: 129px;"><strong>MX value 1</strong></td><td style="width: 163px; text-align: left;"><em>Alphanumeric, 20 positions</em></td><td style="width: 932px;">Blank</td></tr><tr style="text-align: justify;"><td style="width: 129px;"><strong>MX value 2</strong></td><td style="width: 163px; text-align: left;"><em>Alphanumeric, 20 positions</em></td><td style="width: 932px;">Blank</td></tr><tr style="text-align: justify;"><td style="width: 129px;"><strong>MX value 3</strong></td><td style="width: 163px; text-align: left;"><em>Alphanumeric, 20 positions</em></td><td style="width: 932px;">Blank</td></tr></tbody></table>								</div>
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									<p>Whenever the <strong>version of the software related to scheduling or ETL or GL is updated</strong>, there is a possibility for the files to not be in sync with the expected formats and thus result in wrong data sets. If testing teams are able to achieve the desired quality, there are various challenges such as </p>								</div>
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									<ul><li>While the principles recommend banks have one source of data, it is impossible in large banks because the data is distributed across multiple systems.</li><li>Manual intervention related to all the files involved in testing is unavoidable with the current set of tools and frameworks.</li><li>Lack of end-to-end automation that instills trust in Quality Assurance and Reporting. </li><li>Lost productivity in setting up test files and data.</li></ul>								</div>
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									<p>Relying on Excel and manual eye-balling to check values and validate rules. As an example, consider the Excel file shared by the BIS as a reference to help banks adhere to the principles. It has multiple worksheets and one specifically called “Checks” that supervisors and other governance body members can use to validate the risk-related metrics. All this has to be done manually.</p>								</div>
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															<img decoding="async" width="640" height="281" src="https://www.datagaps.com/wp-content/uploads/BIS_Excel-1024x450.webp" class="attachment-large size-large wp-image-6358" alt="BIS_Excel" srcset="https://www.datagaps.com/wp-content/uploads/BIS_Excel-1024x450.webp 1024w, https://www.datagaps.com/wp-content/uploads/BIS_Excel-300x132.webp 300w, https://www.datagaps.com/wp-content/uploads/BIS_Excel-768x338.webp 768w, https://www.datagaps.com/wp-content/uploads/BIS_Excel.webp 1456w" sizes="(max-width: 640px) 100vw, 640px" />															</div>
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									<p>In our future blogs, we will explain how DataOps Suite can help banks achieve the following benefits and adhere to the principles laid out by organizations such as AICPA, BIS, etc. </p>								</div>
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									<ul><li>Elimination of Manual Intervention</li><li>End to end automation of test scripts</li><li>Extremely high performance to test billions of records in little time.</li><li>Integrated scheduling</li></ul>								</div>
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									<p><strong>References:</strong></p><p><span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" title="Referral Link 1" href="https://linfordco.com/blog/processing-integrity/#:~:text=Processing%20Integrity%20is%20one%20of%20the%20five%20trust,transactions%20or%20completing%20processing%20on%20behalf%20of%20clients.">Referral Link 1</a> </span></p><p><span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" title="Referral Link 2" href="https://docs.oracle.com/cd/E69185_01/cwdirect/pdf/180/cwdirect_user_reference/SO02_09.htm#Ravr95793">Referral Link 2</a></span></p><p><span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" title="Referral Link 3" href="https://linfordco.com/blog/processing-integrity/#:~:text=Processing%20Integrity%20is%20one%20of%20the%20five%20trust,transactions%20or%20completing%20processing%20on%20behalf%20of%20clients.">Referral Link 3</a></span></p>								</div>
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		<p>The post <a href="https://www.datagaps.com/blog/leveraging-dataops-suite-to-comply-with-bcbs-soc2-and-other-regulations/">Leveraging DataOps Suite to comply with BCBS, SOC2 and other regulations</a> appeared first on <a href="https://www.datagaps.com">Datagaps | Gen AI-Powered Automated Cloud Data Testing</a>.</p>
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