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Data Warehouse approaches with Dynamics AX

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Dynamics AX의 BI 구축을 위해 필요한 Data Warehouse 내용입니다.
• What is a Data Warehouse
• Data Warehouse Approaches
• Why Invest in a Data Warehouse
• Getting Started
• BI Models
• BI Solutions

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Data Warehouse approaches with Dynamics AX

  1. 1. Data Warehouse Approaches with Dynamics AX UBAX12 Joel S. Pietrantozzi Executive Vice President Client Strategy Group CLIENT STRATEGY GROUP
  2. 2. Agenda •  What is a Data Warehouse •  Data Warehouse Approaches •  Why Invest in a Data Warehouse •  Getting Started •  BI Models •  BI Solutions
  3. 3. Introduction •  Joel S. Pietrantozzi –  Executive Vice President, Client Strategy Group –  O: 216.524.2574 –  Email: joel@csgax.com CLIENT STRATEGY GROUP
  4. 4. Introduction •  Client Strategy Group –  Revive •  Implementation Turnaround •  AX Performance Tuning –  Enhance •  Business Intelligence •  Increased Value –  Upgrade •  Strategy & Planning •  Implementation       CLIENT STRATEGY GROUP
  5. 5. AXUG Premier Partner AXUG Training Academy Classes 1.  AX 2012 – Upgrade your code 2.  AX 2012 – Upgrade your data 3.  AX 2012 – Understanding the Data Model 4.  AX2012 – Understanding the Security Model 5.  AX 2012 – Performance Optimization 6.  AX 2012 – Managing your Environment 7.  AX 2009 – Performance Optimization
  6. 6. WHAT IS A DATA WAREHOUSE?
  7. 7. What is a Data Warehouse? •  Means different things to different people •  Complexity factor –  Does not have to include ETL •  Consider Replication for reporting •  Usually fed from many different data sources •  Contains a large amount of current and historic data •  Allows for flexible reporting, trending and analysis…
  8. 8. What is a Data Warehouse? •  Can simplify the complexity of ad hoc reporting/analysis •  Bottom line: –  Does it meet reporting/analysis needs –  Is the data consistent –  Is it flexible in its design? –  Can it grow with the organization
  9. 9. DATA WAREHOUSE APPROACHES
  10. 10. Data Warehouse Approaches (Storage) •  Two major approaches –  Dimensional – Ralph Kimball •  Facts and dimensions •  Typically easier to use and understand •  Can be complex to maintain/change –  Relational – Bill Inmon •  Database normalization •  Straightforward to add data •  Schema paralysis
  11. 11. Data Warehouse Approaches (Design) •  Bottom-up –  Result of initial business-oriented top-down analysis –  Data marts are created to provide reporting and analysis for specific business processes –  Separation of data into segmented data marts –  Allows for creation of smaller, less-complex models
  12. 12. Data Warehouse Approaches (Design) •  Top-Down –  Data is stored at the lowest level of detail •  Atomic –  Generates consistent view of data –  Creation of new data marts is relatively simple –  Up-front cost can be higher than the bottom-up approach
  13. 13. Data Warehouse Approaches (Design) •  Hybrid –  Often resemble a hub and spoke architecture –  Legacy, ERP and other production systems can feed •  PLC line data –  Operational data store + cube set
  14. 14. WHY INVEST IN A DATA WAREHOUSE
  15. 15. Why invest in a Data Warehouse? •  ERP systems are designed for transactions, not reporting. –  Building reports can lead to system performance degradation and can be quite complex. –  Report development is usually an IT Department task. •  Business Intelligence systems are designed and optimized for reporting and analysis. –  Data is cleansed. –  Data can be pulled from several different sources for true enterprise analysis. •  A business intelligence system is company specific. –  It is designed based on requirements.
  16. 16. Why invest in a Data Warehouse? •  Provides a “common truth” for a company’s information. •  Provides flexibility for dynamic, proactive analysis as opposed to a static view of information. •  Allows users to create analysis/reports pertinent to their needs. •  The need for similar reports is eliminated.
  17. 17. Why invest in a Data Warehouse? •  Should remove reporting performance hits from Production AX •  Multi-dimensional structure in cubes •  Eliminates the need for “Rogue” applications •  The need for similar reports is eliminated.
  18. 18. GETTING STARTED
  19. 19. Getting Started….. •  DW topics to consider: –  Data Latency Requirements •  Operational Reports (Live…picking tickets, labels, etc.) •  Business Reporting (Near Live... open orders, etc.) •  Analytical Reporting (Day-1… sales analysis, etc.) –  Identify Measures & Dimensions by Functional Area(s) –  Cross Functional Data Analysis –  Change Management Flexibility (external data, new requirements)
  20. 20. Getting Started….. –  How many production data sources? •  What is the authoritative data from overlapping production systems? –  Don’t let Reports become the ‘authoritative data source’ •  Ex. Allocations – should be setup in AX instead of external cubes or reports •  Maintenance & Security become on-going issues –  Determine Enterprise Definitions for Reporting •  How are discounts and returns reported? •  How is margin calculated? Yield?
  21. 21. Front End Options •  DW Design should be FE agnostic –  Don’t determine DW solution based on ‘pretty’ FE •  Transactional Reports –  Reporting Services Reports –  Excel Worksheet –  Management Reporter –  Third Party •  Analytical Reports –  Reporting Services Reports –  KPIs –  Excel Worksheet –  Third Party
  22. 22. (Some) Excel BIFE Issues •  Excel is (almost) everywhere •  Usage in even large enterprises is common •  Let’s face it: –  Powerful –  Easy to learn –  Embedded –  Quick •  However, it can be: –  Manual –  User Error prone –  Historical data refresh issues –  Size limitations
  23. 23. Cube Overview •  Cubes –  Multidimensional data structure •  Non-transactional –  Cubes contain pre-aggregated data pivoted at the intersection of the dimension keys •  Aggregation provide significant speed –  Can contain data from one or more fact tables •  Different levels of aggregation can be confusing •  Consider separating measure groups into different cubes
  24. 24. Cube Overview •  Fact Tables –  Lowest level of grain of source data, rolled up into aggregations in SSAS stored in cubes –  The quantitative part (measures) of the OLAP analysis –  1 or more required per cube –  Tend to be fairly narrow but long tables
  25. 25. Cube Overview •  Dimensions –  This is the qualitative piece of the OLAP analysis –  Dimensions can (and should) be shared •  Time & Territory are examples –  Hierarchies and levels are created to provide higher level groupings •  Time – Day, Month, Quarter, Year –  The relationships that are defined between dimensions and measure groups in a cube determine how the data in the cube is “sliced”
  26. 26. Business Intelligence Options •  Native Dynamics AX Tools •  SQL Server stack •  Third Party
  27. 27. Third Party BI Solutions •  Perform a through Evaluation & Selection process based on your reporting and analysis requirements. –  How do they load historical and external data? •  Authoritative data conflicts? –  What is the toolset for change management? –  What FE Tools are available? –  What is the licensing structure? Maintenance? –  Implementation estimate & schedule?
  28. 28. AX 2012 BI Considerations •  MorphX reports deprecated •  All Dynamics AX 2012 reports have been rewritten to (AX)RS •  Utilize Visual Studio 2010 for report development •  External/Historical Data Requirements –  Conversion –  Storage –  Non-SQL Data Sources –  IDMF (Intelligent Data Mgmt Framework)
  29. 29. BI MODELS
  30. 30. BI Models •  All-In-One Role Centers Database Engine (AX)RS Reporting Cubes
  31. 31. BI Models: All-In-One Dynamics AX 2012 KPIs Cubes Reporting Database Engine
  32. 32. BI Models: Replication Dynamics AX 2012 KPIs Cubes Reporting Database Engine Dynamics AX 2012 KPIs Cubes Reporting Database Engine Replication
  33. 33. BI Models: External DW Dynamics AX 2012 KPIs Cubes Reporting Database Engine Data Warehouse KPIs Cubes Reporting Database Engine SSIS Non-AX DS
  34. 34. BI SOLUTIONS
  35. 35. Cubes Available (AX 2012) •  Accounts payable cube •  Accounts receivable cube •  Customer relationship management cube •  Environmental sustainability cube •  Expense management cube •  General ledger cube •  Production cube •  Project accounting cube •  Purchase cube •  Sales cube •  Workflow cube
  36. 36. Planning and Architecture Considerations •  Host the OLAP database on a different server from the OLTP server •  Security for cubes is set up separately from security for Dynamics AX via roles in Analysis Services •  Security for cubes is not synchronized with security for Dynamics AX •  How often should the cubes be processed? •  Do you plan to create custom cubes?
  37. 37. Which one? •  Transactional volume •  Hardware/Infrastructure •  Legacy/Other systems •  Staff/Partner skillset
  38. 38. Best Practices •  Acquire a business sponsor •  Start “small” •  Acquire expertise (hire, grow, contract) •  Create a solid design –  Flexible •  Ensure data quality –  ETL •  “Don’t put the cart before the horse” •  “Don’t put the FE before your data”
  39. 39. External Data Warehouse Model
  40. 40. Continue the Conversation Online user community for knowledge sharing: •  http://community.AXUG.com AXUG events: •  http://www.AXUG.com - Webinars and Special Interest Groups (SIGs) •  Social Media #AXUG #CONV13 #MSDYNAX And don’t forget to complete your session surveys on the Convergence website, your feedback is appreciated

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