Data Warehouse approaches with Dynamics AX

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]


Introduction

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•  Implementation Turnaround •  AX Performance Tuning

–  Enhance •  Business Intelligence •  Increased Value

–  Upgrade •  Strategy & Planning •  Implementation


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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?

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

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


•  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


•  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

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

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”

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

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

BI Models

•  All-In-One

Role Centers

Database Engine

(AX)RS Reporting

Cubes

BI Models: All-In-One

Dynamics AX 2012

KPIs

Cubes

Reporting

Database Engine

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

BI SOLUTIONS

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”

External Data Warehouse Model

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Data Warehouse approaches with Dynamics AX

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