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Data Warehouse Approaches with Dynamics AX UBAX12 Joel S. Pietrantozzi Executive Vice President Client Strategy Group
CLIENT STRATEGY GROUP
Agenda
• What is a Data Warehouse • Data Warehouse Approaches • Why Invest in a Data Warehouse • Getting Started • BI Models • BI Solutions
Introduction
• Joel S. Pietrantozzi – Executive Vice President, Client Strategy Group – O: 216.524.2574 – Email: [email protected]
CLIENT STRATEGY GROUP
Introduction
• Client Strategy Group – Revive
• Implementation Turnaround • AX Performance Tuning
– Enhance • Business Intelligence • Increased Value
– Upgrade • Strategy & Planning • Implementation
CLIENT STRATEGY GROUP
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
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…
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
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
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
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
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
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.
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.
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.
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)
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?
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
(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
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
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
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”
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?
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)
BI Models: Replication
Dynamics AX 2012
KPIs
Cubes
Reporting
Database Engine
Dynamics AX 2012
KPIs
Cubes
Reporting
Database Engine
Replication
BI Models: External DW
Dynamics AX 2012
KPIs
Cubes
Reporting
Database Engine
Data Warehouse
KPIs
Cubes
Reporting
Database Engine
SSIS
Non-AX DS
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
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?
Which one? • Transactional volume • Hardware/Infrastructure • Legacy/Other systems • Staff/Partner skillset
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”
Continue the Conversation
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- 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