Master Data Management in SAP APO | Part 1

4,459 views

Published on

www.plan4demand.com | info@plan4demand.com | 866-P4D-INFO

Thanks to "Big Data" coming in from various sources, organizations can make better decisions faster and increase their bottom line. HOW that data gets to a point where it is ready for analytics is where most of the work needs to be done.
Join APO Expert, Jerry Sanderson, for Part 1 of this 2 part series, as he lays out the foundation for successful Master Data Management and highlights practical tips to weed through the big data jungle and grow for long-term success.

We’ll provide insights into:
1. Unit of Measure (UOM) – Why UOM needs to be defined, understood, and common across your data
2. Data Definitions – What information and assumptions are used to create master and transactional data
3. Data Scrubbing – When data scrubbing and cleansing be done
4. Data Validation & Tools – How to get business users involved with data quality validation and ways to provide data exception tools to assist

Check out this webinar on-demand at http://www.plan4demand.com/Video-Webinar-SAP-APO-Master-Data-Management-Tips

Published in: Business, Technology
4 Comments
5 Likes
Statistics
Notes
No Downloads
Views
Total views
4,459
On SlideShare
0
From Embeds
0
Number of Embeds
30
Actions
Shares
0
Downloads
0
Comments
4
Likes
5
Embeds 0
No embeds

No notes for slide

Master Data Management in SAP APO | Part 1

  1. 1. June 5th, 2013 plan4demandMaster Data Leadership Exchangepresents:The web event will begin momentarilywith your host:
  2. 2. Proven SAP Partner More than 500 successful SCPengagements in the past decade. We’re known for driving measurableresults in tools that are adopted acrossour client organizations. Our experts have a minimum of 10 yearssupply chain experience. Our SAP team is deep in both technologyand supply chain planning expertise; havemanaged multiple implementations; havea functional specialty.“Plan4Demand has consistently putin extra effort to ensure our Griffinplant consolidation and demandplanning projects were successful.”-Scott Strickland, VP Information SystemsBlack & Decker
  3. 3. 1. Unit of Measure (UOM) - Why UOM needs to be defined, understood, andcommon across your data2. Data Definitions - What information & assumptions are used to createMaster and Transactional data?3. Data Scrubbing – When should data scrubbing and cleansing be done?4. Data Validation & Tools – How do you get business users involved with dataquality validation and how do you provide data exception tools to assist?5. Data Latency – Why is Transactional data timing important?6. Master Data Source – Where should master data should be pulled from?7. Planning Data Storage – Where should scrubbed master / transactionalplanning data be stored?8. Data Maintenance – How can Users maintain?9. Data Ownership – Who should own the process?3
  4. 4. • While Master Data may seem like a jungle to besuccessful you need to think of it as a garden instead.• Easier to manage• Tamed and pruned• Tended to4a*Plan4Demand is in no way an advocate of deforestation
  5. 5. 1. Unit of Measure (UOM) - Why UOM needs to be defined,understood, and common across your data2. Data Definitions - What information & assumptions are used tocreate Master and Transactional data?3. Data Scrubbing – When should data scrubbing and cleansingbe done?4. Data Validation & Tools – How do you get business usersinvolved with data quality validation and how do you providedata exception tools to assist?5
  6. 6. UOM needs to be defined, understood, and common across your dataBusinesses deal with multiple units of measure (UOM) from raw materials, work inprocesses, finished goods, and saleable goods.From an APO Planning perspective, a common UOM is required tocommunicate the operating plan across all business functions. Businesses typically have multiple units of measure for the products they sell,distribute, procure, and manufacture Too many planning units of measure lead to inaccurate planning results Integrated SAP APO solutions are nullified by manually updated conversion tables6
  7. 7. A base unit of measure (UOM) must be establishedfor APO Planning purposesThe unit of measure must be applicable from a sold to customer standpoint suchthat projected business requirements can be passed along to all planningfunctions. Usually the selling product UOM is used as the planning base unit of measure Establish a common base UOM for planning purposes If other business areas operate with a different UOM, conversions factors must bemaintained to tie back to the planning base unit of measure7
  8. 8. APO Planning UOM failuresare symptoms of bigger issuesExample:A Major CPG Company’s APO implementationcame to a stand still when multiple UOMconversions took place with in the APO-SNPplanning engineQuestions To Ask: Why are so many UOM conversionsrequired ? Are we using the correct APO Applicationfor the desired planning function ? Are we maximizing the overall businessefficiency or a specific planning areaefficiency ?Designing a Master Data ManagementSolution is like planting a garden tofeed your APO Planning SolutionKeep the Master Data Gardensimple and easy to maintain8
  9. 9. 1. Unit of Measure (UOM) - Why UOM needs to be defined,understood, and common across your data2. Data Definitions - What information & assumptions are used tocreate Master and Transactional data?3. Data Scrubbing – When should data scrubbing and cleansingbe done?4. Data Validation & Tools – How do you get business usersinvolved with data quality validation and how do you providedata exception tools to assist?9
  10. 10. Understand what information and assumptionsare used to create master and transactional dataDuring many APO implementations, project teams are in such a hurry to pullmaster and transactional data from ECC (or legacy applications) they fail toclearly understand how the data is defined and IF the mapped data fullymeets the business requirements. One of the major reasons APO projects fail to go-live as planned is because masterand transactional data requirements were not clearly defined up front Each data element must have a clear definition of what characteristics are includedin the definition Project teams are too quick to locate an ECC field name by a key word instead oflocating the master / transactional data source10
  11. 11. Approach APO data definition requirements like business requirements.Clearly define the base data requirements (focus on what should be included andexcluded) map the data elements available (documenting all characteristics associatedwith the data) and perform a GAP analysis between data requirements and dataavailable.11 Clearly document each data element requiredto support your APO solution (baseline datarequirements) Once the APO data requirement sourcemapping to ECC (or legacy application) iscomplete, perform a thorough analysis onsource data definitions and characteristics Conduct a GAP analysis between the APObaseline data requirements and available ECCsource data to determine data scrubbing effort
  12. 12. Questions to Ask What data characteristics arerequired for APO Planning ? Is my APO data mapped to the ECCdata origin source ? What are all the data characteristicsof the ECC origin source data ?When planning your Data ManagementGarden (Solution), make sure youselect the plants that meet the needsof your garden.12Example:An Industrial Goods company was pulling OrderHistory to drive their APO-DP solution. After Go-Live they noticed their forecasts were consistentlyoff by 1-2 periods. Turns out their Order Historywas pulling Order Creation Date and not CustomerRequested DateData is not defined equally from business tobusiness: Details Matter!Once you have selected your“master data plants”,understand each plant’s(Data Set’s) characteristicsto ensure a bountiful garden
  13. 13. 1. Unit of Measure (UOM) - Why UOM needs to be defined,understood, and common across your data2. Data Definitions - What information & assumptions are used tocreate Master and Transactional data?3. Data Scrubbing – When should data scrubbing and cleansingbe done?4. Data Validation & Tools – How do you get business usersinvolved with data quality validation and how do you providedata exception tools to assist?13
  14. 14. When in the Implementation Process did DataScrubbing Occur?Answer on your screen – Select all that ApplyA. Before the Project StartedB. After Data Mapping was CompletedC. During Integration TestingD. During UAT TestingE. None of the Above14
  15. 15. Allow plenty of time for data scrubbing and cleansing!Often times “data scrubbing” or “data cleansing” activities are performedmultiple times on the same master / transactional data elements.These additional scrubbing activities are reactive and unplanned actions thateat into project delivery time. When data issues are discovered during testing activities, it usually means notenough time was spent on clearly defining the APO data requirements Multiple data cleaning activities are time consuming, expensive, and waste valuableproject resources If the APO Project fails to allocate enough data scrubbing time during datavalidation, project testing costs increase by 2x – 3x!15
  16. 16. Do not rush through or over look theData Requirements / Data Validation APO Project stage.This critical project stage creates a solid Master and Transactional Datafoundation for the APO Planning Solution.16 Data Scrubbing activities must be well plannedwith a specific set of instructions Specific Data Cleansing actions are driven byGAPs identified during the data requirementsprocess Data scrubbing is not complete until the APOinput data is verified and determined fit for use
  17. 17. Data Scrubbing requires detail instructionsdriven by data GAP analysisKeys to Success Develop robust data definitions withbusiness user input Document source data logic andassumptions Document and develop test criteria forall data scrubbing elements Create a robust data scrubbingalgorithm with verifiable resultsThe APO input “data garden”requires weeding and attention toachieve the desired resultsAPO data must be pristine to properlysupport the high demands on theIntegrated Planning Solution17
  18. 18. 1. Unit of Measure (UOM) - Why UOM needs to be defined,understood, and common across your data2. Data Definitions - What information & assumptions are used tocreate Master and Transactional data?3. Data Scrubbing – When should data scrubbing and cleansingbe done?4. Data Validation & Tools – How do you get business usersinvolved with data quality validation and how do you providedata exception tools to assist?18
  19. 19. Get business users involved with data quality validationMany APO Projects create a false sense of security during Data Validation.Project teams either use an unqualified resource to sign-off on DataValidation or fail to provide validation instructions and tools. APO Data Validation is not a rubber stamp process where someone reviews areport, a spreadsheet of data, or a database file and then provides an “OK TheData Looks Good” When APO teams fail to allocate enough data validation time, the burden is pushedto integration / user acceptance testing In many cases, APO data validation is performed by someone who is not familiarwith the data and how it will be used to Demand or Supply Plan the business19
  20. 20. A robust APO Data Validation testing strategy is required to successfullydeliver a reliable planning solution, on time, and on budget.20 Before APO Data Validation can begin, abusiness planning resource must be heldresponsible for validating the APO planning data By re-using the APO Master and TransactionalDate Requirements Definition documents, theValidation Team can ensure testing scenarioscontain the appropriate data characteristics Repeat APO Data Validation and scrubbingiterations until master and transactional data isvalidated 100%
  21. 21. APO Data Validation starts duringimplementation and continues throughthe life of the APO Planning SolutionKeys to Success Develop robust data validationmethodology Re-use Data Requirements DefinitionDocument to script Validationscenarios Re-use Data Scrubbing logs to assistwith Data Validation Cross Check Validation Results Establish ongoing automatedvalidation checksAPO data requires constantmonitoring and attention tomaintain quality beyond Go-LiveData Validation is required to ensure the APOinput data garden is healthy and strong21
  22. 22. Provide data exception tools to assist business users with data quality validation22 Many times APO Project teams do notprovide data validation tools for thebusiness users Business planners are left to use their ownpersonal skills, knowledge and expertiseto determine how to validate the APOdata provided by the project team In many cases, business users are providedwith a data dump and asked to validatethe data as they see fitAnother area where APO Projects fail to deliver is when proper Data Validation Tools arenot provided to validation team members. Either validation tools are not provided orTesters are not properly trained to use data validation tools.
  23. 23. Data Validation Tools are enablers to the overall Data Validation MethodologyAPO data validators must have proper Data Validation Tool training, access to thebase test data, and a place to document the results. In order to select the appropriate Data Validation Tools, clearly document the APOProject Data Validation methodology strategy Next, ensure the business validators have access to the baseline and scrubbed data The validation team must be trained how to use the selected validation tools Finally, clearly define the acceptance criteria and documentation requirements forvalidated data23
  24. 24. Once the appropriate tool is chosen, it isimportant to know how to use itSelecting the right tool for the jobis only half of the solutionIf you do not know how to use the toolsto tend to the APO data garden you willdo more harm than good24Keys to SuccessThere are many data validation toolsavailable, choose the one that best fitsyour testing needs HPQC SQL Queries Access Excel
  25. 25. 1. Unit of Measure (UOM): UOM needs to be defined, understood, andcommon across your data2. Data Definition: Understand what information and assumptions areused to create master and transactional data3. Data Scrubbing: Allow plenty of time for data scrubbing andcleansing4. Data Validation: Get business users involved with data qualityvalidation5. Data Validation Tools: Provide data exception tools to assist businessusers with data quality validation25
  26. 26. If you use SAP to Plan… Think
  27. 27. SAP, R/3, mySAP, mySAP.com, SAP NetWeaver®, Duet®,PartnerEdge, and other SAP products and servicesmentioned herein as well as their respective logos aretrademarks or registered trademarks of SAP AG inGermany and in several other countries all over theworld. All other product and service names mentionedare the trademarks of their respective companies.Plan4Demand is neither owned nor controlled by SAP.Page 27
  28. 28. For Additional Information or a PDF CopyContact:Jaime Reints412.733.5011jaime.reints@plan4demand.com

×