high performance driver based modelingat att
TRANSCRIPT
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Financial Forecasting & Planning SummitSeptember 9-10, 2010 | InterContinental | Boston
High Performance Driver Based Modeling at AT&T
Curtis Neumann CPAAT&T Corporate FP&A Systems
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Disclaimer
These slides represent the work and opinions of the presenter and do not constitute official positions of Oracle or any other organizations.
This material has not been peer reviewed and is presented here with the permission of the presenter.
This material should not be reproduced without the written permission of AT&T.
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Table of ContentsPage Description
4 Overall Theme
5 Trigger for a Modeling Process Change
6 New Opportunity for Driver Based Modeling
7 Reporting Change Impacts Planning
8 More Granular Data from Reporting to Modeling
9 New Reporting impact to planning
10 Product Allocation Model Objectives
11 What is a Multi-dimensional Database?
12 AT&T Utilizes Oracle Hyperion Essbase
13 Essbase Benefits over Spreadsheets
14 Essbase vs. Excel: Huge Scalability
15 Essbase vs. Excel: Calculation Performance
16 Essbase vs. Excel: Accuracy
17 End User Expectations
18 Modeling Flexibility
19 Data Integration and Reliability
20 Quick Demo
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Overall Theme
With cost management analysis being a key component of financial management in today’s economic environment, "Working Smarter , Not Harder” is imperative in conducting performance analysis
This applies to the budgeting/planning process as well
This theme refocuses analysts away from 80% of time spent on budgeting mechanics (tasks) toward 80% insightful analysis.
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Trigger for a Modeling Process ChangeReporting Change:
Assign and Allocate Products
within Profit Centers
Assign and Allocate to Consumer
and Business Profit
Centers
IT Billing Operations Cost Center
Monthly Wage
Expense
IT Billing Ops$500M
Consumer $300M
Voice$50M
Mobile Prod1$250M
Business$200M
Data Prod 1$100M
Data Prod 2$100M
Before Reporting Change
After Reporting Change
PC P/L $M Consumer Business
Revenue 1,000 2,000
Selling Costs 100 50
New: IT Billing Wages
300 200
Operation Margin 600 1,750
New Accountability Reporting initiated by leadership at AT&T
Profit Centers no longer measured on Incurred Cost Basis
Instead, Cost Center expense assigned to Profit Centers at product level, based on a new allocation engine built from time and motion studies
Post change, Product volumes drive revenue and expense levels
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New Opportunity for Driver Based Modeling
Previously, operating expenses like wages were tied to Headcount, which did not vary with revenue, thus making it more difficult to analyze expenses as revenues changed
The new reporting methodology, resulting from the allocation of expense to Profit Centers by Product, created the opportunity to analyze the business by product drivers
New iPhone Contract Expense per iPhone
New iPhone expense
Same Volume Drivers
New iPhone revenue
Reve
nue
per
iPho
ne
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Reporting Change Impacts Planning
Set Goals
Model
Plan
Monitor
Analyze
Report
Continuous Performance Management Cycle
Shared Info
A
B
Changes to reporting necessitated changes to the modeling component of the “Continuous Performance Management Cycle”
A.Planning Group has to model in the exact same approach as reported results using product volume drivers
B. This facilitates the comparison of actual results to expectations
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More Granular Data from Reporting to Modeling
Level of reporting detail data before and after change
After
Before
Reported data becomes input to bottoms up product driver based modeling
$ $
$
$
$$
Level of modeling detail data after change
1,000,000
1,000 cells
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Granularity Impact to the Planning Process?
Before the new reporting process, the legacy modeling process was completely Excel driven and contained dozens of spreadsheets
After the reporting change to introduce cost center and product allocations, and the desire to expand driver based modeling, the data set expanded exponentially rendering Excel unusable
Dozens to 100’s of spreadsheets
Linking nightmares, etc.
Modeling Methodology Change
Legacy Spreadsheet Process
Requirements
(What)
Solution(How)
Outcome
(Why)
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Product Allocation Model Objectives
Presentation Focus: Transitioning from Spreadsheets to a Multi-Dimensional Database Solution
Construct a more robust Corporate Planning process more closely aligned with product drivers
Optimize the planning process with the most innovative technology.
Provide an improved Corporate Planning user experience
More granular forecasting to structurally align with cost allocated actuals, thus achieving better accountability reporting
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What is a Multi-dimensional Database?
A multi-dimensional database uses the concept of a data cube to represent dimensions, which provide the capability to manipulate and analyze data from different perspectives
Years
Mon
ths
Accounts
Categories, like Months, Years, and Accounts are stored in different dimensions which display multiple views of the same data in a pivoted format
Within each dimension, hierarchies define parent/child relationships. This structure facilitates drill-down within Excel retrievals
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AT&T Utilizes Oracle Hyperion Essbase There are different vendors for multi-dimensional
databases. The two largest are Oracle and Cognos.
AT&T utilizes Oracle Hyperion Essbase which has been used exclusively for financial reporting
Essbase stands for Extended Spreadsheet DataBASE
Not replacing Excel as a tool for analysis
Essbase is a database on a server behind Excel used to crunch millions or billions of cells in seconds to minutes
Innovative tool for FP&A Systems to access, update, and share data at various levels of detail, leveraging the powerful calculation engine
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Essbase Benefits over Spreadsheets
Calculation
Performance
#2
Accuracy
#3
User Experienc
e
#4.0Modeling
Flexibility
#4.1
Data Integrati
on
#4.2
Reliability
#4.3
Huge Scalabilit
y
#1
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Essbase vs. Excel: Huge Scalability
Benefit EssbaseExcel
by Itself
1. Model can grow from Megabytes to 100’s of Gigabytes
Yes. If Model source data grows 1,000X, database can accommodate
within days
Limited
2. Supports many large custom dimensions
Yes. Aggregate Storage Option
allows millions of members per
dimension
Limited
3. Effective for large enterprise wide deployments
Yes. With a thousand user
concurrent access
Not Really
#1
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Essbase vs. Excel: Calculation Performance
Benefit EssbaseExcel
by Itself
1. Data Crunching
Millions to billions of
intersections in seconds to
minutes
Limited. Lots of spread- sheets
with links
2. Very fast query time to retrieve data into Excel
Yes. Depending on site of Excel retrieval, two seconds to a few minutes
N/A. Formulas Results
get ref’d
3. Formula Replication
None. Formula stored in a
single Calculation
Script
Slow to calculate thousands of cells on a PC
#1
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Essbase vs. Excel: Accuracy
Benefit EssbaseExcel by
Itself
1. Formula Consist-ency
OLAP engine executes script
instructions perfectly as it cycles each intersection
Formulas unique to each cell,
so mistakes happen
2. Robust built-in math functions
Yes. Built for Finance.
Includes NPV, IRR,
Regression, etc.
Yes
3. Output Central-zied
Single Version of the Truth
Principle
Who’s got the final spread-sheets?
#1
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End User Experience
Excel Add-In
Use Data Immediately
No IT Involvement
Retrieve from a cube
Send data to a cube for modeling inputs (factors…)
Build calculations from retrieved data directly into Excel
No formatting or parsing
Easy to learn
No more IT generated reports
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Modeling Flexibility
Dimensions are represented as rows and columns and are stored in a central Outline
To add a column or row takes a few minutes, verses hours, days, or months for Excel
Model Structure
Cu
stom
Bu
ilt
Pro
cess
Not limited by software design. Just like Excel, cube Outlines and Calc’s are built from scratch
Calculations occur in sequential stages, allowing for input of factors between Per Unit x Unit (PxQ) Calculations
Calculation Scripts are housed in one place on the server Calculations can be focused on one cell,
slices of the cube, or the entire cube Most Common calc’s are PXQ and Allocations Allocation logic “pushes down” parent data
to children very quickly
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Data Integration and Reliability
Input Data comes from any source. Adapters available for SAP, Oracle, etc.
Planners load data from Excel models and crunch data on the fly. Retrieve the results minutes later
Seamless Data
Movement
Backup and
Security
All cubes backed-up nightly. Lowers risk of data loss
Robust security down to the cell level and read vs. write access
Data access security
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Quick Demonstration
The Dr. Cube Show
Work Smart -> Work Less -> Stay Married
1.Load historical wage expense and product units data
2.Calculate historical per units
3.Apply payraise factors and load forecast units
4.Calculate forecast expense
5.Apply productivity factor and recalculate expense
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1. Per Unit Calculation
2. Avg 1st Qtr Units
3. Copy the base 2009 rates to 2010.
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4. Calc new Per Unit with Payraise.
5. Calc 2010 PxQ=$
6. Calc 1st Qtr 2010 intersections.
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