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 SeeDimension

Hierarchy

Level

Member

Child members

Report StudioData 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