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Neoaug 2013 critical success factors for data quality management-chain-sys-corporation
Neoaug 2013 critical success factors for data quality management-chain-sys-corporation
Neoaug 2013 critical success factors for data quality management-chain-sys-corporation
Neoaug 2013 critical success factors for data quality management-chain-sys-corporation
Neoaug 2013 critical success factors for data quality management-chain-sys-corporation
Neoaug 2013 critical success factors for data quality management-chain-sys-corporation
Neoaug 2013 critical success factors for data quality management-chain-sys-corporation
Neoaug 2013 critical success factors for data quality management-chain-sys-corporation
Neoaug 2013 critical success factors for data quality management-chain-sys-corporation
Neoaug 2013 critical success factors for data quality management-chain-sys-corporation
Neoaug 2013 critical success factors for data quality management-chain-sys-corporation
Neoaug 2013 critical success factors for data quality management-chain-sys-corporation
Neoaug 2013 critical success factors for data quality management-chain-sys-corporation
Neoaug 2013 critical success factors for data quality management-chain-sys-corporation
Neoaug 2013 critical success factors for data quality management-chain-sys-corporation
Neoaug 2013 critical success factors for data quality management-chain-sys-corporation
Neoaug 2013 critical success factors for data quality management-chain-sys-corporation
Neoaug 2013 critical success factors for data quality management-chain-sys-corporation
Neoaug 2013 critical success factors for data quality management-chain-sys-corporation
Neoaug 2013 critical success factors for data quality management-chain-sys-corporation
Neoaug 2013 critical success factors for data quality management-chain-sys-corporation
Neoaug 2013 critical success factors for data quality management-chain-sys-corporation
Neoaug 2013 critical success factors for data quality management-chain-sys-corporation
Neoaug 2013 critical success factors for data quality management-chain-sys-corporation
Neoaug 2013 critical success factors for data quality management-chain-sys-corporation
Neoaug 2013 critical success factors for data quality management-chain-sys-corporation
Neoaug 2013 critical success factors for data quality management-chain-sys-corporation
Neoaug 2013 critical success factors for data quality management-chain-sys-corporation
Neoaug 2013 critical success factors for data quality management-chain-sys-corporation
Neoaug 2013 critical success factors for data quality management-chain-sys-corporation
Neoaug 2013 critical success factors for data quality management-chain-sys-corporation
Neoaug 2013 critical success factors for data quality management-chain-sys-corporation
Neoaug 2013 critical success factors for data quality management-chain-sys-corporation
Neoaug 2013 critical success factors for data quality management-chain-sys-corporation
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Neoaug 2013 critical success factors for data quality management-chain-sys-corporation

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For More Details Visit Us: http://www.chain-sys.com

For More Details Visit Us: http://www.chain-sys.com

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  • 1. Critical Success factors for Data Quality Management (DQM) Presented by Austin Davis & Stephen Rivet June 10, 2013 Creator of the appLOAD™ Suite of Data Management Tools appLOAD™ appEXTRACT™ appMIGRATE™ appINTERFACE™ appBI™
  • 2. o o o o o Oracle Platinum Partner Oracle ConsultingServices Partner Oracle EBS, Database and Middleware products Reseller Oracle EBS Mobility Solution Provider Oracle University AuthorizedEducation providerand Center Proud developers and owners of: o e-chain™ ERP/SCM suite for 11 Verticals o appLOAD™ family of productivitytools for Oracle eBS R12/11i- Oracle ValidatedIntegration o Accelerated IntegrationSolutions for Salesforce, Siebel, Optivision, Icon, etc. An Introduction CHAIN SYS is a fast growing technology, product and management consulting company. Established in 1998 and headquartered in Lansing, MI, with more than 350+ engineers and 100 customers worldwide. Strong Partnership with Oracle Corporation
  • 3. Information is currency. Information Quality contributes to that edge by delivering the right information, at the right time, in the right place, to the right people. The right Information can help Inventory Managers keep the supply chain lean, help CEO’s make long-term plans for growth based on accurate and dependable performance measures.
  • 4. Key Concepts: Data, Data, Data and even more Data Plan Obtain Store Maintain Apply DisposeData Life Cycle Master data •Customers •Employees •Vendors •Locations •Organization •Accounts •Products Reference Data •Customer type •Item type Transactional Data •Sales orders •Purchase orders •Trips •Deliveries •Invoices •Payments Meta Data •Table name •Field name •Constraints •Data types
  • 5. Assessment Awareness Action Data Quality Improvement Cycle: Does it ever end? Assessment Data Quality Cycle AwarenessAction 1.Business needs 2.Analyse 3.Assess data quality 4. Assess Business impact 1.Identify root cause 2.Improvement plans 1.Prevent future errors 2.Correct current data errors 3.Implement Controls
  • 6. Define Measure Analyze Improve Control Business Needs Analyze Data Assess Data quality Assess Business Impact Identify root cause Develop improvem ent plans Prevent Future data errors Correct current data errors Implement Controls Data Quality Improvement Cycle Six sigma DMAIC
  • 7. Cross Reference Suppliers Financials Get Clean, Stay Clean Source Systems TheTool Combine, Match, Grouping, Cleansing, Enrichment Unforming De-activate/Archive, Cleansing in current systems Cleansed data Data for Enrichment Get Clean System 1 System 2 xref Data Hub Consolidation/ PDH Customer Stay CleanItems erp crm edw
  • 8. 11i Pre-validation or Correction Manual extract Source System Extraction Source Iterative Correction appMIGRATE eBS Environment & Validation CRP Non-PROD appMIGRATE PROD SIT FIT UAT Non-PROD appMIGRATE PROD appMIGRATE Post ProductionIterative Cleansing & Migration When to do DQM? Right NOW !!!
  • 9.          Date Extraction Data Cleansing & Validation Data Consolidation Data Load Error Handler Staging Table FTP file Transfer Scheduler Schedule Engine Transaction Log & Notification Tool Approach appMIGRATE the Data Management Tool
  • 10. appMIGRATE Value Proposition…  Reduce Overall Project Timeline ~ Immediate Data Visibility & Reduced Cut-over Times  Minimize Data Risks ~ Functional Business Process Testing  Budget Savings ~ Significant ROI over Traditional Programing Methods
  • 11. Define Stage Define Business Need Based on: 1. Lost revenue and missed 2. 3. 4. opportunity Unnecessary or excessive costs Increased risk Shared processes and data 1. 2. 3. Analyze Information Identify data subject to the business issue Create a detailed data list for the data of interest Understand the data model
  • 12. Measure Stage Define Business Need Based on: 1. Lost revenue and missed 2. 3. 4. opportunity Unnecessary or excessive costs Increased risk Shared processes and data 1. 2. 3. Analyze Information Identify data subject to the business issue Create a detailed data list for the data of interest Understand the data model
  • 13. Solution Approach Organization Creation  To create new organizations, synchronize entities from source system to the target eBS instance; appMIGRATE will be leveraged to minimize the amount of time needed to create new organizations with cleansed data. Cleanse Data based on Source and Target Validations. Future organizations will contain cleaner data. Target Source eBS A ORG ORG ORG ORG ORG Source 2 Target Target Target ORG Source 1 Target Target Target ORG ORG ORG ORG Target Target Target ORG ORG Target ORG Target Target ORG Target Target Target ORG
  • 14. CE Project Inventory Management Supply Chain Functional Currency Risk Management Optimize Global Effective Tax Rate
  • 15. Analyze Stage Assess Data Quality 1. Finalize your data capture 2. 3. 4. 5. Extract the data into data mart Profile the data Analyze the results Duplication 1. 2. 3. Assess Business Impact Collect examples of poor data Illustrate the effects of poor data Compare potential benefits of investing in data quality with anticipated costs.
  • 16. Analyze Stage Duplication 1. 2. True Match Non Match 3. 4. False Negative False Positive threshold MatchNo Match False PositivesFalse Negatives
  • 17. Solution Approach Organization Acquisitions  During an acquisition, legacy data is frequently brought into the existing eBS environment. appMIGRATE will allow an easy way to extract the data from source system to the target eBS instance; appMIGRATE will be leveraged to minimize the amount of time needed to create new organizations with or without data. Data can be profiled, consolidated and cleansed. Rules based algorithms ensure that future data stay’s clean! Target eBSSource B Source 1 Source 2 Legacy or eBS Target ORG ORG ORG ORG Target Target ORG Target Target Target ORG
  • 18. Summary of IBM & Chain-Sys Engagements
  • 19. Improve Stage Prevent future data errors 1. Focus on root causes 2. Implement improvement plans 3. 4. Improvement should not affect existing functionalities Project Planning and effective communication Correct current data errors 1. 2. 3. 4. Identify the data to be updated or deleted Existing functionality should not get affected. Use Manual or automated tools to fix Document the change
  • 20. Solution Approach Organization Creation  During improvement project, appMIGRATE will be leveraged to minimize the amount of time needed to create new organizations with cleansed data. Cleanse Data based on Source and Target Validations. Rapidly respond to Changing needs: costing, financial, regulatory Target Source eBS C ORG ORG ORG ORG ORG Source 2 Target Target Target ORG Source 1 Target Target Target ORG ORG ORG ORG Target Target Target ORG ORG Target ORG Target Target ORG Target Target Target ORG Target ORG Target ORG Target Target ORG
  • 21. Established Thermal Power Generation Company Part of US$ 4 bn. Avantha Group Operational Capacity of 191 MW of Power and Generating Capacity of 2460 MW under various stages of implementation Demerged from BILT in 2012 Captive Power Plants operating since 1955 Costing, Financials, Regulatory Concerns
  • 22. Control Stage Implement Controls 1. Use the data quality dimensions to control the data quality. 2. Survey knowledgeable business users for issues in the data. 3. Measure the data quality improvements that have been implemented. 4. Identify next potential area of improvements. Communication 1. 2. 3. Determine who need to be communicated Increase the communication Agreement and consensus.
  • 23. Solution Approach Organization Creation appAugmenter is an application to apply dynamic personalizations to OAF pages of Oracle EBS, built on the OA framework and pages can be augmented from a single interface. It is developed per OA Framework standards and guidelines Target Source eBS C ORG ORG ORG ORG ORG Source 2 Target Target Target ORG Source 1 Target Target Target ORG ORG ORG ORG Target Target Target ORG ORG Target ORG Target Target ORG Target Target Target ORG Target ORG Target ORG Target Target ORG
  • 24. The appMIGRATESolution Features…  Over 500 Pre-build Extract & Load Templates for Oracle eBS R12 covering Master, Reference, Setup, Transactional and Historical Data  Extract for Multiple Sources, Output into Excel, CSV or xml Formats & Load +Million Records in “One Shot”  Utilizes Oracle Security Model & Provides Extensive Audit Trails & Notification  Purge, Archive, Transform, Cleanse, Validate & Refresh Data as Required  Scalable Java/J2EE Technology & Hub & Spoke Model minimizes Infrastructure No Programming Needed, Simple Configurations, Intuitive & Easy-to-Use & Deploy
  • 25. Questions? Visit our Booth.
  • 26. Cleanse & Transform Data as Required • • • appMIGRATEincludes several data cleansing tools. Data may be compared to located duplicate records with Alias Key Identifiers based on sound, key words (Levinstein Distance Method) and custom configured logical relationships. All Objects/Attributes in the Data Mart are available for edit. An audit trail is created for all changes. • appMIGRATEprovides a robust logical data transformation tool. It is capable of logically modifying existing data or creating data logically in pre-defined fields. If a new segment is added to the general ledger chart set the entry can be created using this feature.
  • 27. Pre-Validate Data & Correct Errors As Required • • • An on-line error report is presented and the user may make corrections as required. A tool to assist in the correction of errors (by suggestion) is also provided. All data correction data is collected and maintained and is available in an electronic document form. • • appMIGRATEwill perform a pre- validation of the data in the Data Mart using the same criteria as the target. In addition special validation rules may be added to the appMIGRATE pre- validation if needed.
  • 28. • • appMIGRATEincludes a capability to select records based on any column, including transaction type, transaction date, etc. Records can be segregated into files for Purge & Archive Data as Required (Migrating Historical Data) both Purging & Archiving as well as for load to the target. • Historical transaction data can be migrated in the same manner that it was originally entered so the integrity of the audit trail is maintained. • This capability makes migration of historical transaction data easy and efficient.
  • 29. Load Data to Target Instance • • • Where standard API’s are not provided by eBS, appMIGRATE uses custom developed API”s & Open Interface Rules to provide for data validation. Load executions may be scheduled automatically using File Transfer Protocol (FTP) procedures standard to eBS. Helps with cut-over. Load execution is run as a “background task” permitting other applications to be used simultaneously . • appMIGRATE uses standard eBS Application Programming Interfaces (API’s) to ensure that data is validated in accordance with the eBS rules.
  • 30. Interfaces Loading Template Adapters Extract Adaptors appLoad Adapters Log & Notification Handler appMIGRATE Schedule Engine appExtract Service FTP Service appLoad Service appINTERFACE Template Processor Loader Processor Pre-validation Engine Error Correction Cleansing Handler appLOAD Query Parser Extract Engine app EXTRACT Instance Handler Data Processor Validation Engine Log & Notification Handler Base Components appBI Interface Adaptors Migrate Adapters Log & Notification Data Consolidation Tactical Log & Notification Handler & Integration Next Generation Data Migration Data Extraction and Error Handler Chain-Sys Product Overview
  • 31. Summary of eBS Template List Master Data: • Manufacturing Master Data • WIP, BOM, Routing • Quality • Inventory • Order Management • • • Fixed Assets Master Data Pricing Purchasing • OPM Transactional Data • Payables • Receiveables • Purchasing • Order Management • Items • Costing • General Ledger Setup Data • General Ledger • Payables • Receiveables • Fixed Assets • Purchasing • Order • • • management Inventory Pricing OPM INV • • • • OPM MFG OPM QA OPM FIN Admin 100+ Master data Templates 500+ Total Templates 100+ Transaction Templates 200+ setup Templates
  • 32. Chain-Sys is Featured in Oracle’s Profit Magazine
  • 33. Oracle Partnership CHAIN SYS - Oracle Platinum Partnership… Chain-Sys is recognized by Oracle for achieving key specializations in functional and technology expertise. Chain-Sys is a recognized Accelerate Partner. Chain-Sys appLOAD suite is validated and has achieved “Ready” status from Oracle.
  • 34. appMIGRATEClients

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