Beyond PowerPlay: Choose the Right OLAP Tool for Your BI Environment (Cognos Dynamic Cubes vs Microsoft)

BEYONDPOWERPLAY: CHOOSINGTHERIGHTOLAP TOOLFORTHEJOB
MAKINGSENSEOFTHEVARIETYOFOLAP OPTIONSFORBI APPLICATIONS

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Resource Library
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•The Challenge –The need for snappy performance
•Reporting and analysis types to consider
•Architectural Options
•Available OLAP Tools
•Feature Set Comparisons
•Demo: Comparing the tools, plus slick features
•Decision-Making Tips
•Additional Resources & Upcoming Events
•Q & A
TODAY’SAGENDA
4
Copyright 2014Senturus, Inc. All Rights Reserved

John Peterson CEO & Co-FounderSenturus
Introduction: Today’s Presenters
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Pedro Ining
Senior BI Architect
Senturus
With thanks to:
Michael Farrell (CRC Health), David Shroyer, Albert Valdez, and Knowledge @ Senturus

WHOWEARE
SENTURUSINTRODUCTION

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Our Team:
Business depth combined with technical expertise. Former CFOs, CIOs, Controllers, Directors, BI Managers
SENTURUS: BUSINESSANALYTICSCONSULTANTS
8
Business Intelligence
Enterprise Planning
Predictive Analytics
Creating Clarity from Chaos

750+ CLIENTS, 1500+ PROJECTS, 13+ YEARS
9Copyright 2014 Senturus, Inc. All Rights Reserved

DOYOUUSEOLAP TOOLSTODAY? IFSO, WHICHONES?
QUICK POLL

THENEEDFOR"SNAPPY" PERFORMANCE
THE CHALLENGE

Standardized Reports
Self-run
Automated distribution
Drill-Down
Drill-Thru Reports
Ad-hoc Reports
One-off queries
Individualized, interactive dashboards
Scorecards
Speed-to-thought analytics
Visualizations
Mobile reports and analysis
REPORTINGANDANALYSISTYPESTOCONSIDER
12

Standardized Reports
Self-run
Automated distribution
Drill-down
Drill-Thru Reports
Ad-hoc Reports
One-off queries
Individualized, interactive dashboards
Scorecards
Speed-to-thought analytics
Visualizations
Mobile reports and analysis
USESTHATREQUIREFAST* RESPONSETIMES
13
Copyright 2014 Senturus, Inc. All Rights Reserved*Note: All uses require decent performance

Today‟s discussion will focus on OLAP used for enhancing and extending the capabilities of a BI System
Not on toolsused more for planning and write-back
For example: Cognos TM1, Oracle (Hyperion) Essbase
Also, Microsoft recently introduced SSAS “Tabular Model” version alongside the existing “Multi-Dimensional” version. This presentation will focus on the traditional version.
IMPORTANTPREFACE
14

The Big Challenge of TM1 & Essbase
15
Copyright 2014 Senturus, Inc. All Rights Reserved.
ERROR: Attribute is not unique.
* This stems from a Fundamentalarchitectural constraint

Properly Staged Data
BI Tools
Typical Best Practices BI System
16
Copyright 2014 Senturus, Inc. All Rights Reserved. * Also known as a Star Schema
Source Systems of Record
Conforming
Business Process
Dimensional Models*
Data Abstraction Model
Information Security
Report
Authoring
Dashboard
Authoring
Slicing &
Dicing
Ad Hoc
Querying
Threshold
Alerting
ERP
Standard
ReportsDashboards/ Scorecards
Self-Service
Reporting &
Analysis
Threshold- based Alerts
Web Portal
Architected Data Warehouse & BI Tools
Single Version of the Truth
Data Integration (ETL)
CRM
Planning
Other
Other
Other external
sources

Relational
Dimensionally Modeled Relational (DMR)
OLAP-based
Hybrid OLAP & Relational
POSSIBLEMODEL-LAYERARCHITECTURES
17

Option 1: Relational & DMR Approach
18
Data Mart
Or Data Warehouse
Cognos
FM
Ad-hoc
Reports
Cognos
Dashboards
Standard
Reports

Cognos
Transformer
Option 2: OLAP Approach
19
Data Mart
Or Data Warehouse
Cognos
FMAd-hocReports
Cognos
Dashboards
Standard
Reports, etc.

Option 3: Hybrid Approach (Preferred)
20
Data Mart
Or Data Warehouse
Cognos
FM
Ad-hoc
Reports
Cognos
DashboardsStandardReports
Cognos
Transformer

MOSTCOMMONOLAP TOOLSFORBI USE
THE BIG THREE

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•IBM Cognos PowerPlay
•Microsoft SQLServerAnalysis Services (SSAS)
•IBM Cognos Dynamic Cubes
•IBM Cognos TM1
•Hyperion Essbase
THEBIGTHREEOLAP TOOLS
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For Dimensional BI Uses
For Finance Use (less “free-form” BI)

Advantages
•Performance(vs. relational)
•Easyto use and develop
•ETL-like capabilities (limited) –i.e. no star schema needed
•Can act as meta-data layer
•Great relative-time calc capabilities (YTD, Rolling 13 months…)
•Less intensive hardware requirement
Challenges
•Significant cube size limitations
•Limited categoriesper dimension level
•Cube builds take time & Cubes exist as separate files (.mdc)
•Lacks capabilities now available in other OLAP tools
•Row-level (dimensional) security isvery challenging to maintain
•Unclear product supportgoing forward
COGNOSPOWERPLAY(TRANSFORMER)
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Advantages
•Scalability–Example:1 billion fact table records on a server with only 8 GIG of memory, and performance was decent.
•Handles large dimensions with drill to detail –It also handles dimensions with hundreds of millions of records.This allows for user “drill to detail” which allows users to see the fact table level detail.
•Live "count distinct" capability at any level (e.g. unique invoice header count)
•Dynamic Date Calculations –via MDX extensions
•Scripting–Allows for scripting.Can set up just about any type of calculation that is necessary.Very flexible.
•Dynamic Security –you can set up dimensional filtering so that all security is derived from sqltables.This is handy when you have a large cube with security that needs to be table driven
Challenges
•Limited to MicrosoftSQL Server platform
•Not all features get exposedthru other BI tool reporting layers
•MDX coding required for some common functions (e.g. relative time)
MICROSOFTSQLSERVERANALYSISSERVICES(SSAS)
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Advantages
•Scalability–limited only by database and RAM cache sizing
•Handles large dimensionswith drill to detail –It also handles dimensions with hundreds of millions of records.This allows for user “drill to detail” which allows users to see the fact table level detail.
•Allows Dimension attributes(see demo)
•Built-in Relative Time calcs(as of version 10.2.1)
•Scripting–Allows for scripting.Can set up just about any type of calculation that is necessary.
•Dynamic Security –you can set up dimensional filtering so that all security is derived from sqltables.
•Aggregate advisor helps tune database
Challenges
•Requires star or snowflake schema as data source
•Cacheneeds to be “warmed” for decent performance
•Requires 64-bit application server
•Relatively untested, as yet -Still working out some kinks
IBM COGNOSDYNAMICCUBES
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Dynamic Query Mode Architecture

HOWARETHETOOLSDIFFERENT?
FEATURE SET COMPARISONS

Nowhere is change more constant than in software feature sets
The following slides were based on the latest available information regarding Cognos 10.2.1 tool functionality
But even as I speak, these items are changing
Note: the focus of the following slides is on the use of OLAP for BI reporting performance and feature enhancement.
CAVEATAUDIENCE
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•SOFTWAREREQUIREMENTS& OS SUPPORT
•DATABASE, DATASOURCESUPPORT
•COGNOSFRONT-ENDSUPPORT
•DATASTORAGEMODELS& DATAVOLUMES/SCALABILITY
•OLAP FEATURES
•SECURITY
•EASE-OF-USE, FLEXIBILITY, DEVELOPMENTSPEED
•LICENSINGCONSIDERATIONS
•OTHERCONSIDERATIONS
FEATURESTOCONSIDER
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SOFTWAREREQUIREMENTS& OS SUPPORT
NOTE: All cube design/development tools are Windows applications
Cognos PowerPlay/
Transformer
SSAS - Microsoft SQL Server Analysis Services
Cognos - Dynamic Cubes
Cognos TM1 Cubes
DMR/OLAP Over Relational
Software Type
Requires Server software installation
X
X
X
X
Desktop Client (Windows)
X
X*
Designer Tools (Windows-based)
X
X
X
X
X
Designer Tools (Browser-based)
Operating Systems
Windows
X
X
X
X
Linux
X
X
X
UNIX
X
X
X
Bitness
32-bit
64-bit
64-bit
64-bit
32/64-bit
*TM1 Excel, CAFÉ, Excel Perspectives PLUGIN

DATABASE, DATASOURCE& COGNOSFRONT-ENDSUPPORT
* Now available in SSAS Tabular 2012
Cognos PowerPlay/ Transformer
Microsoft SSAS
Cognos Dynamic Cubes
Cognos TM1 Cubes
Database Support (for content storage)
Microsoft SQL Server Only
Various RDMS's
X
X
No Database needed for content storage
X
X
X
Data Source Support
Relational -Star
X
X
X
X
X
Relational -Transactional
X
X
X
X
Excel
X
X
Text Files
X
*
X
Cognos Front-End Support
Report Studio, Query Studio
X
X
X
X
X
Analysis Studio
X
X
X
X
X
PowerplayStudio & PowerplayClient
X
Workspace/Workspace Advanced
X
X
X
X
X
Microsoft Excel
X
X
X
X
X

DATASTORAGEMODELS& DATAVOLUMES/SCALABILITY
Microsoft SSAS
Cognos TM1 Cubes
Data Storage Models
MOLAP
X
X
*
X
HOLAP
X
*
ROLAP
X
X
X
Data Volumes/Scalability
Small (<2GB)
X
X
X
X
X
Medium
X
X
X
Large Volumes
X
X
Drill to transactional detail
Limited
X
X
X
X
Large Dimensions
X
X
X
User Scalability
X
X
X
* In-memory ROLAP

OLAP FEATURES
Microsoft SSAS
Cognos TM1 Cubes
NOTES
OLAP Features
Semi-Additive Measures (Last Period)
X
X
X
X
TM1 VIA CALCS
Write-Back
Limited*
X
*Supported but weak
Partitioning
X
X
X
TM1 NO
Relative Time -Built In
X
X
X
SSAS: via Named Sets; TM1 VIA DIMENSION SETUP;DC in 10.2.1.FP2
Date Dimension
X
X
X
X
X
TM1 has weak date dimension support; Starting in v10 Time dim supported
Live "count distinct" at any level
X
Incremental Processing
X
X
X
X
Process Current Month;DC10.2.1 FP2
Dimension Incremental Changes
X
X
X
X
i.e. when customer members move within dimension
In -Memory
X
X
X
X
DMR Dynamic Query member caching
Calculation Scripts, User Functions
X
X
X
TM! Realtimecalcsi.e. rules
Drill-Thru reports
X
X
Financial Functions
X
X
TM1 few basic functions
Alternative Hierarchies
X
X
X
X
Recursive Hierachies
X
X
Most cubes need ETL to process these hierarchies
Unbalanced Hierarchies
X
X
Business Attributes
Limited*
X
X
X
X
Short name, Longname,Label fields only
Pre-Aggregation
X
X
X
TM1 batch process
Agreggate Aware
X
X

SECURITY
NOTE: **SSAS: Performance impacts when using this
Microsoft SSAS
Cognos TM1 Cubes
Security
Dimensional Security
X
X
X
X
X
Dimensional Level/Member Security
X
X
X
X
X
Via Lookup tables
X
X
X
X
Attribute Security
X
X
Ease of Maintenance
Difficult
Easy
TBD
Mid
Easy

EASE-OF-USE, FLEXIBILITY, DEVELOPMENTSPEED
Microsoft SSAS
Cognos TM1 Cubes
Ease of Use & Development Speed
Ease-of-use (scale: 1-5, where 5 is most difficult)
1
2
2
4
5
Flexibility of Tool (scale: 1-5, where 1 is most flexible)
5
2
3*
2
4
Development Speed (scale: 1-5, where 1 is fastest)
1
3
4
4**
1
NOTES:
* MDX Expressions, user functions/scripts
** When using TM1 w/ BI dataset, DynCubesrequire star-schema
John and Pedro’s unscientific opinions

LICENSINGCONSIDERATIONS
•PowerPlayand Dynamic Cubes included in Cognos BI Licenses
•Microsoft SSAS included in SQL Server (extra cost if not using that platform)
•Cognos TM1 extra (*but comes with Cognos Express)

•In all cases, it pays off in spades to get your data into a dimensionally modeling structure PRIOR to feeding it into an OLAP tool. The OLAP modeling effort will be orders of magnitude easier, and features like drill through and security will be possible.
OTHERCONSIDERATIONS…
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BEYONDPOWERPLAYWITHSSAS ANDDYNAMICCUBES
DEMO

•MS SQL Server 2008 Database (source)
•MS Analysis Services
•IBM Cognos 10.2.1
–Plus FP1
–Plus “Feature Bundle”
–Plus FP2
•MS Sample „AdventureWorks‟ Star Schema
•Amazon AWS Instance
–4 Core
–16GB RAM
PLATFORMSPECIFICS
44

•Three OLAP Cubes built from the AdventureWorksStar Schema
–Cognos PowerCube(Transformer)
–SqlServer Analysis Services Cube
–Cognos Dynamic Cube
•Compare and Contrast three key features
–Tool similarities to end-user
–Scalability against large dimensional sets and fact tables
–Count (Distinct)
–Relative Time
–“Relative-relative” Time
–Attributes
DEMOAGENDA
45

SOMEDECISION-MAKINGTIPS
Roadmap & Wrap-up

IBM COGNOSOLAP ROADMAP
IBM Cognos TM1
Optimal for write-back, what-if analysis, planning and budgeting, or other specialized applications.
Is able to handle medium data volumes.
Aggregation occurs on the fly, which can impact performance with high data and high user volumes.
IBM Cognos Dynamic Cubes
Optimal for read-only reporting and analytics over large data volumes.
Aggregate-aware, with extensive in-memory caching for performance.
Star or snowflake schema required in underlying database (highly recommended to maximize performance).
IBM Cognos PowerCubes
Optimal to provide consistent interactive analysis experience to large number of users when the data source is an operational/transactional system, and a star or snowflake data structure cannot be achieved.
Cube (MOLAP) architecture that includes pre-aggregation is such that scalability requires careful management using cube groups.
Data latency is inherent with any MOLAP cube technology, where data movement into the cube is required.
Dimensionally Modeled Relational (DMR)
Optimal to easily create a dimensional data exploration experience over low data volumes in an operational/transactional system, and where latency needs to be carefully managed.
Caching on the Cognos Dynamic Query server helps performance.
Processing associated with operational/transactional system impacts performance.
* Source: IBM Cognos PowerplayFAQ-2012 (http://public.dhe.ibm.com/software/analytics/cognos/support/en/migration/PP_FAQ_2012.pdf)

A FEWDECISION-MAKINGTIPS
There are several excellent alternatives to PowerPlay
Stick with PowerPlayif your dataset sizes (both facts and dimensions) and build windows permit
If you are a “Microsoft SQL Server shop” with large data sets, seriously consider SSAS, but be aware that some features may not be exposed by the BI tools
And be careful if choosing the new Tabular Model option
Dynamic Cubes look very promising, particularly the latest version
But beware of “pioneer arrows” (e.g. cache control, etc)
Prototypes and pilotsare HIGHLY recommended
Beware of trying to simply layer TM1 or Essbaseon top of a BI dataset (uniqueness problem) –but for write-back, these are your only options
Be careful with DMR, especially since other alternatives exist
As always, push logic and relationships “upstream” into architected Data Mart(s), instead of relying on OLAP tools to maintain complex logic

FROMIBM ANDSENTURUS
ADDITIONALRESOURCES

•IBM Cognos Dynamic Cubes Redbook
–https://www.redbooks.ibm.com/Redbooks.nsf/RedbookAbstracts/sg248064.html?Open
•IBM Compatibility Reports
–http://public.dhe.ibm.com/software/analytics/cognos/support/en/migration/PP_FAQ_2012.pdf
–http://publib.boulder.ibm.com/infocenter/prodguid/v1r0/clarity- reports/report/html/osForProduct?deliverableId=1330380859450
•IBM Cognos Cookbooks
•Senturus.com
–http://senturus.com/resources/
–jpeterson@senturus.comor jfrazier@senturus.com
ADDITIONALRESOURCES
51

www.senturus.com
WEBINARS:
The Impact of Hadoop and other Big Data Technologies on Your BI Investment June 5
Cognos Report Studio Advanced Authoring Tips & Tricks June 10
Cognos Metadata Modeling Tips & Tricks, June 19
Advanced Active Reports, July 10
The Right Cognos Reporting Tool for the Job, July 24
Dynamic Cubes Tips & Tricks, August 7
UPCOMINGEVENTS
52

*Custom, tailored training also available*
Cognos Training Options
53

More Information on www.senturus.com
54

Thank
You!!
www.senturus.com888-601-6010info@senturus.com
Copyright2014bySenturus,
Inc.
Thisentirepresentationiscopyrightedandmaynotbereusedor
distributedwithoutthewrittenconsentofSenturus,Inc.

Beyond PowerPlay: Choose the Right OLAP Tool for Your BI Environment (Cognos Dynamic Cubes vs Microsoft)

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