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

1. Data Warehouse Approaches with Dynamics AX
UBAX12
Joel S. Pietrantozzi
Executive Vice President
Client Strategy Group
CLIENT STRATEGY GROUP

2. Agenda
• What is a Data Warehouse
• Data Warehouse Approaches
• Why Invest in a Data Warehouse
• Getting Started
• BI Models
• BI Solutions

3. Introduction
• Joel S. Pietrantozzi
– Executive Vice President, Client Strategy Group
– O: 216.524.2574
– Email: joel@csgax.com
CLIENT STRATEGY GROUP

4. Introduction
• Client Strategy Group
– Revive
• Implementation Turnaround
• AX Performance Tuning
– Enhance
• Business Intelligence
• Increased Value
– Upgrade
• Strategy & Planning
• Implementation
CLIENT STRATEGY GROUP

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. WHAT IS A DATA
WAREHOUSE?

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. 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. DATA WAREHOUSE
APPROACHES

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. 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. 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. 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. WHY INVEST IN A DATA
WAREHOUSE

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. 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. 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. GETTING STARTED

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. 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. 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. (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. 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. 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. 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. Business Intelligence Options
• Native Dynamics AX Tools
• SQL Server stack
• Third Party

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. 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. BI MODELS

30. BI Models
• All-In-One
Role Centers
Database Engine
(AX)RS Reporting
Cubes

31. BI Models: All-In-One
Dynamics AX 2012
KPIs
Cubes
Reporting
Database Engine

32. BI Models: Replication
Dynamics AX 2012
KPIs
Cubes
Reporting
Database
Engine
Dynamics AX 2012
KPIs
Cubes
Reporting
Database
Engine
Replication

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. BI SOLUTIONS

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. 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. Which one?
• Transactional volume
• Hardware/Infrastructure
• Legacy/Other systems
• Staff/Partner skillset

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. External Data Warehouse Model

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
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