The Great OLAP
Debate!
TM1, PowerPlay & DMRs
April 29, 2011
Panel Presenters
Michael Langton
Scott Luck-Baker
Mike Roberts
Pedro Mendoza
Panel Debate Format
 Each one of the panelists will present
evidence that their approach is the best
way to handle OLAP reporting
 Your job as a participant is to ask
questions to challenge each OLAP
approach …
Goals For Session
 IBM provides several options for OLAP reporting
 Does one size fit all?
 We will review each technology:
 Description
 Product Background
 Key Functionality
 Business Use Case “Sweet Spot”
 Usage Notes, Design and Deployment Considerations
IBM TM1
IBM TM1 - Overview
 Developed in the late 80’s as a backend for spreadsheets
 Multi-dimensional database designed to simplify complex
spreadsheets and separate the data from the formulas
 TM1 cubes are essentially collections of business hierarchies
(dimensions); numeric and text data can be stored at the
intersections of every dimension element
 TM1 cubes sit in RAM so that data consolidation and formulas
(cube rules) are performed in “real-time”
 TM1 clients include Excel, TM1Web, Contributor (for workflow),
Executive Viewer, and Cognos BI
TM1 Web
TM1 Contributor
Executive Viewer
IBM TM1 - Sweet Spot
 TM1 is designed for the WRITEBACK of numeric and text data
 It is ultimately flexible and models can be built from a variety of
data and meta-data sources to hold almost any type of data
 TM1 includes a rule language for writing complex formulas into
your model; rules are evaluated in real-time for instant feedback
 Non-technical users can perform administrative/modeling tasks
via wizards, drag & drop actions, or using customizable buttons
through the web
 Users can slice & dice cube views, and drill through to further
levels of detail
IBM TM1 - Usage Cases
 Replacing Excel as a planning tool
 Slicing & dicing aggregated data
 Comparing apples to apples
 Processes that require manual entry
 Processes that require real-time feedback/calculations
 Processes that require workflow/security
 What-if analysis / Driver-based planning
 New ways of rolling up your data
 Anywhere non-technical users need to build reports, add/remove
elements, launch imports/exports
IBM Cognos PowerPlay
IBM Cognos PowerPlay- Overview
 Originally developed by Cognos in 1989
 PowerPlay Transformer is used to define OLAP cube structures
and building static “cubes” for analysis or reporting, usually on
a scheduled basis
 PowerPlay Cubes contain summarized data organized into
dimensions and measures, can be built from very large datasets
and are highly optimized for data retrieval
 PowerPlay Cubes can be viewed via the web (Analysis Studio,
Query Studio, Report Studio, C10 Biz Insight/Advanced) or via a
full client (PowerPlay Client, CAFÉ Excel)
IBM Cognos PowerPlay- Sweet Spot
 Ideal where users have large datasets that require flexible
summarization and reporting options, as opposed to a list of
canned reports
 Transformer provides advanced multi-dimensional model
support and varied data sources (via Framework Manager),
incremental refresh options, alternate drill paths, automatic
category counts, time-based and volume-based partitioning
strategies
 Non-technical users can explore data through simple click and
drag operations and can gain insight through functions such as
rank, sort, nesting and calculations
 Users can drill from cube-to-cube or cube-to-database
IBM Cognos PowerPlay- Usage Cases
 Great for sales, marketing and financial analysis
 Users have large datasets, possibly in an existing database or
across multiple sources
 Users or IT want “self serve” analysis capabilities
 Users want “zero footprint”
 Users have no interest in budgeting or planning
 Users don’t need real time reporting
 Users don’t need to report on “non-dimensional data” elements
 Warning: Prone to User Misuse (esp. in S7)
DMR Framework Designs
DMR Framework Designs - Overview
 Introduced in Cognos 8
 Uses Framework Manager to model
Relational Data to appear “like a cube”
 Model can be used in Analysis Studio
and Report Studio Express
Define Regular Dimensions
 Consists of one or
more user-defined
hierarchies
 Each hierarchy
consists of
 levels
 keys
 captions
 attributes
Edit DMR in the Dimension Map
 View, create, or modify:
 regular or measure dimensions
 hierarchies or levels
 scope relationships
What the Authors See
Dimension
Hierarchy
Level
Member
Child
members
Report Studio
Data Tree
DMR Framework Designs - Sweet Spot
 You do not need another application to build cubes
 No need to wait while cube is being built
 Data changes in the underlying tables are immediately
available
 Complex security rules can be created in one place
(Framework Manager)
 Define multiple Hierarchies for a Dimension
 Define as many member attributes as you want
DMR Framework Designs - Usage Cases
 Implement Drill Up/Down in reports without cubes
 Analysis of Real-time data or data that would take
too long to build into a cube
 Models that have complex business rules that
would be difficult to implement in a cube
 Solutions where security is defined at the
database level
DMR – Deployment Considerations
 Aggregations are not stored in a cube. They are calculated
from detail every time a report or analysis is run.
 Performance is dependent on good hardware and design
 Databases must be optimized to maximize performance
 It may be necessary to employ a form of database vendor
materialization to improve performance
 DMR packages are usually built on top of existing FM
models and are deployed the same way.
DMR – Design Considerations
Design for Performance
 Physical data should be in star schemas to minimize complex joins
 Create summary tables to avoid aggregating on the fly
 Model for high level analyses and rely on drill-through reports to
give detail
 DMR works best with small narrow dimensions rather than large
wide dimensions
 Build on top of a good well-designed relational Framework.
 Build mandatory filters into your model to ensure that end users do
not accidentally retrieve excessively large data sets
Panel Debate