The document summarizes a presentation on optimizing Excel. It discusses common problems with spreadsheets like confusing formulas, unstructured layouts, and errors. It then introduces the FAST standard for building transparent, accurate Excel models. The standard advocates for clear formatting, labeling, and separating inputs from calculations. Finally, it discusses research on common spreadsheet errors and frauds enabled by weaknesses in spreadsheets.
3. 3experience. new thinking
Today’s agenda
Learning’s from implementing FAST at
Wilmar with Gerard Cooney
Common problems seen in practice with
Matthew Green
Spreadsheet Detective and its review
abilities with Anthony Berglas
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A review of the implementation of the
FAST modelling standard at Wilmar
Sugar – Gerard Cooney
(fomerly Sucrogen and CSR Sugar)
Implementing FAST
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The Problem
Excel is ubiquitous in business
User friendly
Very flexible
These attractive attributes cause problems:
Everyone thinks they can use excel
effectively
Models structured using the idiosyncrasies of
the user
The result is often a mess
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Key Problems
Every model is structured differently
Difficult to review and audit
Prone to errors
Difficult to modify
Difficult to understand
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1.4 metre long formula
=IF(AND($N97<>1,$O97<>1),$F97*$I97*IF(AND(HeavyVehInd=2,X
$4>=RoadStartYr),(1-$L97),(1-$K97))*(1-INDEX
(X$5:X$7,MATCH($Q97,$Q$5:$Q$7,0))),
IF(AND($N97=1,$O97=1),IF(OR(BiomassInd=1,AgInd=1),0,IF(X$4>
=MAX(BiomassStartYr,AgStartYr),
$F97*$I97*IF(AND(HeavyVehInd=2,X$4>=RoadStartYr),
(1-$L97),(1-$K97))*(1-
INDEX(X$5:X$7,MATCH($Q97,$Q$5:$Q$7,0))),0)),
IF($N97=1,IF(BiomassInd=1,0,IF(X$4>=BiomassStartYr,$F97*$I97*I
F(AND(HeavyVehInd=2,X$4>=RoadStartYr),(1-$L97),(1-$K97))*(1-
INDEX(X$5:X$7,
MATCH($Q97,$Q$5:$Q$7,0))),0)),IF($O97=1,IF(AgInd=1,0,IF(X$4>
=AgStartYr,$F97*$I97*IF(AND(HeavyVehInd=2,X$4>=RoadStartYr),(
1-$L97),(1-$K97))*(1-
INDEX(X$5:X$7,MATCH($Q97,$Q$5:$Q$7,0))),0))))))
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THE FAST STANDARD
What does it stand for?
Flexible
Accurate
Structured
Transparent
http://www.fast-
standard.org/
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FLEXIBLE
Design and modelling techniques must allow
models to be both flexible in the immediate
term and adaptable in the longer term.
Flexibility is born of simplicity.
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STRUCTURED
Rigorous consistency in model layout and
organization is essential to retain a model’s
logical integrity over time, particularly as a
model’s author may change.
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TRANSPARENT
Simple, clear formulas that can be
understood by other modellers and non-
modellers alike. Confidence in a financial
model’s integrity can only be assured with
clarity of logic structure and layout.
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Key Attributes of FAST model
Use of calculation blocks where the calculation ingredients are shown explicitly
and appear directly above the calculation
Calculation ingredients link directly to the source (either input data or
precedent calculation block). There is no daisy changing of links.
Link labels and units as well as numbers, and enter only once
Use of timing flags
Also:
Use of short formulae
Constants only entered once.
Parameters only calculated once.
Consistent formulae across a row.
Consistent use of columns within a sheet
All inputs are collected on input-only sheets and colour coded to show explicitly
Formatting consistency
Diligent use of units
Separate calculation engine from presentation output.
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Key Attributes of FAST model
Use of calculation blocks where the
calculation ingredients are shown explicitly
and appear directly above the calculation
Calculation ingredients link directly to the
source (either input data or precedent
calculation block). There is no daisy
changing of links.
Link labels and units as well as numbers,
and enter only once
Use of timing flags
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Secondary Attributes of FAST model
Use of short formulae
Constants only entered once.
Parameters only calculated once.
Consistent formulae across a row.
Consistent use of columns within a sheet
All inputs are collected on input-only sheets
and colour coded to show explicitly
Formatting consistency
Diligent use of units
Separate calculation engine from presentation
output.
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Excel example
REVENUE
USING A TYPICAL APPROACH
Sugar Revenue 5,870 $
Molasses Revenue 120 $
Total Revenue 5,990 $
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Common Complaints
Doesn’t a standard approach stifle creativity
It takes too long to model using the FAST
Standard
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Understanding the common issues
we see with Excel™ spreadsheets,
some conceptual insight into how
errors occur and some suggestions
on how to prevent them…
Matthew Green
Common issues with Excel™
spreadsheets and models
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My agenda
How prevalent is Excel™
Common problems
Error research
The need for graphical presentation of data
7 steps to review your spreadsheets
Practical learning's and takeaways
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Typical balance sheet
Excel used in the following key areas
and calculations:
Account reconciliations
Other asset listings and
amortisation
Fixed asset registers and
depreciation
Deferred and current tax
Intangible asset reconciliations
Impairment models
Debt covenants
Interest accruals
Employee benefits
Derivative reconciliations and to
cross check bank valuations
Spreadsheets for transactional
reports with Pivot tables for
further analysis
Consolidation schedules
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"I suppose it is tempting, if the
only tool you have is a hammer, to
treat everything as if it were a
nail."
Abraham Maslow, 1966
24. 24experience. new thinking
Excel & Accountants …
“like giving your kid a chainsaw, powerful tool, but
does he really understand what he’s got in his hands
and how to use it?” You have to ask yourself: “Is this
going to end well?” (And “No, he’s not getting one for Christmas!”)
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How many problems really exist?
“In our experience, most spreadsheets are
poorly developed. This is probably because
there isn’t much formal training on how to
build a spreadsheet and people don’t have
time to build them so that they are optimised
for their purpose and to support decision
making.”
Want to see some examples?
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Common problems
Confusing, complicated formula
Unstructured layout
Formula & Function errors
Range & Pointing errors
Hard coding
Remote references
Empty precedents
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Confusing and complicated formula
A real life discussion thread in an Excel™
specialist LinkedIn group.
Why would anyone want to nest more than
1 “If” statement, let alone 8?
Can you imaging how hard it would be to
unravel an error in these “If” statements?
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Inconsistent build
Sundry income inconsistently treated in six operating
sites in budget file taken from board papers.
In some sites, sundry income was included in total
revenue and gross profit.
In other sites, sundry income is excluded from both, but
is factored into net profit.
Site analysis based on GP would favour those with the
sundry income in their GP.
This error would not have been apparent to directors in
their decision making.
Source: corporate transaction, target budget file
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Inconsistent build
Budget model for 6
operating sites in board
papers.
Site A calculated direct
wages % based on net
metered win.
Site B calculated direct
wages % based on gross
profit.
Not a big difference, but
makes comparison
across sites difficult.
Why is there a business
reason for the two sites
to have different basis of
calculation
Site A
Site B
Source: corporate transaction, target budget file
30. 30experience. new thinking
Hard coding and format
Common example of a
quickly built spreadsheet.
Note the hard coded
information buried in
formula, in this case the
CPI increase rate for the
leases.
Note the inconsistent
number formatting, doesn’t
help the reader with
assessing information.
Whilst the outcome is
quantitatively accurate, the
poor design makes in hard
to review and leaves the
worksheet prone to error.
Lease commitments:
No later than 1 year
Office premises to 14 October 2018
annual rent 100000
Monthly Rent to 31 October 14 8333.333 83333.3333
annual rent after 1st increase 103000
Mothly rent to 31 December 14 8583.333 17166.6667
100,500.00
1 year to 5 years
Year 2 2015
Monthly rent to October 15 8583.333 85833.3333
annual rent after 2nd increase 106090
Mothly rent to 31 December 14 8840.833 17681.6667
Total rent yr 1 103,515.00
Source: Hanrick Curran audit file
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Hard coding
Commonly seen on ad hoc spreadsheets.
Typically involves a formula like:
= C5 * (A36 + 1.03) – 408 + 12
The reviewer can usually decipher that the 1.03 is
probably CPI, but what about the other
adjustments.
A better way is to put the CPI number in its own
cell as an input.
Hard coded adjustments should also be avoided. If
needed, build data entry cells for adjustments.
Example of problem use of hard coding
Source: Hanrick Curran client board reporting file
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Remote references
A “remote reference” is a reference in a formula to cells that
are remote from the worksheet the formula is on, either to
another worksheet in the same file or to a different file.
Remote references are difficult to review and prone to errors.
We recommend they be avoided by using a ‘links sheet’ in a
workbook for all ‘cross-file links’.
Formula within a workbook should also avoid remote
references by gathering all needed data and then using the
formula
Source: Hanrick Curran client board reporting file
Source: Hanrick Curran client board reporting file
Example of problem use of remote references
Example of corrected approach to use of remote references
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How big can the errors get?
In January 2010, academics
Carmen Reinhart and Kenneth
Rogoff published “Growth in a
Time of Debt”.
Their report was widely cited by
politicians as theoretical and
research based support for
reducing public debt and public
spending.
Later analysis reveals errors with
the underlying spreadsheet
analysis; countries are excluded
from the average because of a
‘range error’.
Great Brittan slashes spending by
£10 billion, in response to the
research and increase in debt
following in the GFC.
Source: Quartz website, http://qz.com/75119
Pasted from <http://qz.com/75119/how-to-avoid-making-
an-excel-mistake-like-rogoff-and-reinhart/>
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Allied Irish Bank – US$691 m. Fraud
AIB is one of the Irish big 4 commercial banks,
parent of Allfirst Bank, based in Baltimore,
Maryland, US.
John Rusnak, committed a US$691 million
currency trading fraud.
Rather than pay $10,000 fee for a Reuters feed
to the treasury compliance team (back office)
the data feed to the VaR calculation was based
on data from Rusnak’s computer.
The data was loaded into a spreadsheet which
Rusnak manipulated to ensure that trading
losses were otherwise hidden from the VaR
assessment.
Source: AIB p.l.c. SEC filing, March 12, 2002
Source: R. Butler, “The role of Spreadsheets in the Allied Irish Bank / Allfirst Currency Trading Fraud” (2009)
VaR = Value-at-Risk
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Allied Irish Bank – US$691 m. Fraud
AIB SEC Filing: “A simple check to see if the
holdover figures were captured in the next
day's trading activity would have caught
this scheme.”
At least two points of failure:
1. Data in spreadsheets was open to
manipulation
2. Compensating controls were not
strong enough to detect the
manipulation
Source: AIB p.l.c. SEC filing, March 12, 2002
Source: Hanrick Curran research
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Enabling Fraud
In summary, the inclusion of a spreadsheet in a reporting
chain enabled hiding of fraud, especially without adequate
compensating detective controls and reconciliations.
A similar fraud occurred in a Brisbane company between 2011
and 2013, resulting in a $2.4 million loss to the company,
related to overstatement of inventory balances (16% of PY
reported inventory).
Source: Hanrick Curran research
Data
spreadsheet
Reporting
Missing control checks and reconciliations
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Raymond Panko, University of
Hawaii, has undertaken significant
research into spreadsheet errors.
Panko’s research informs the
classification of errors in
spreadsheets.
Error taxonomy and research
39. 39experience. new thinking
Error research
There is a significant amount of research into
human error from fields as diverse as
mathematics, programming, aircraft accidents,
nuclear incidents, proofreading and linguistics.
A key insight from these fields is that
“human cognitive processes produce the
correct result nearly all the time but have a
small inherent error rate that stems from the
same processes that produce correct results.
In other words, the way we actually think …
is the heart of the problem, not simple
sloppiness.”
Source: Raymond Panko and Salvatore Aurigemma
“Revising the Panko-Halverson Taxonomy of Spreadsheet
Errors” (February 2010) p.4
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Mistakes, Slips and Lapses
When working with spreadsheets, errors can be
categorised as follows (Reason, 1990):
Mistake – an error in planning
Slip – an error during a sensory-motor action, such
as typing the wrong number in a cell (e.g.,
$120,000 instead of $210,000)
Lapse – a failure in memory, usually caused by
overloading the limited human memory capacity
In terms of error detection, planning and memory
errors that occur ‘off spreadsheet’ leave little if any
evidence for error detection.
Source: Raymond Panko and Salvatore Aurigemma
“Revising the Panko-Halverson Taxonomy of Spreadsheet
Errors” (February 2010) p.5
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Error frequency
Research from Allwood used students solving a mathematical
problem. Error rates identified included:
327 errors as they worked
60% of errors were execution errors (slips and lapses)
83% of execution errors were spontaneously identified
and corrected during work – the result, execution errors
only accounted for 29% of final errors
Logic errors (mistakes) accounted for only 25% of
errors, but low detection rates resulted in these
mistakes contributing to 40% of final errors.
Skip errors (missing a part of the solution) accounted
for only 9% of all errors made, but a nil detection rate
meant they contributed to 29% of final errors.
In short: “We don’t see what we don’t see”
Source: Raymond Panko and Salvatore Aurigemma
“Revising the Panko-Halverson Taxonomy of Spreadsheet
Errors” (February 2010) p.6
29%
40%
29%
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Qualitative error impacts
When considering errors, we need to assess their
impact on the final result. Panko suggests two
approaches:
1. Error magnitude – how big is the error
compared to the final correct bottom-line
number
2. Would a different decision be taken based on
correct versus incorrect results.
Panko and Halverson conclude that “most errors
are either too small to be important or still give
answers that lead to the correct decisions”.
Source: Raymond Panko and Salvatore Aurigemma
“Revising the Panko-Halverson Taxonomy of Spreadsheet
Errors” (February 2010) p.8
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Panko & Halverson error taxonomy
A revised error taxonomy is described by
Panko and Halverson.
Source: Raymond Panko and Salvatore Aurigemma
“Revising the Panko-Halverson Taxonomy of Spreadsheet
Errors” (February 2010) p.25
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Panko & Halverson error taxonomy
A revised error taxonomy is described by Panko
and Halverson.
Domain type errors (e.g., misunderstanding
requirements or not correctly reflecting
business requirements) are the most likely
error to remain undetected and to result in an
undetected error in the spreadsheet.
Execution errors (e.g., incorrect formula
references) are most likely to be corrected
during spreadsheet development and review,
but can also leave undetected errors in
spreadsheets (e.g., Reinhart & Rogoff).
Source: Raymond Panko and Salvatore Aurigemma
“Revising the Panko-Halverson Taxonomy of Spreadsheet
Errors” (February 2010) p.25
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Graphing outputs of spreadsheets
is important. Some examples of
why follow…
A segue into the graphic display
of information
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Anscombe’s Quartet
Four data sets with
similar characteristics
X average = 9.0
Y average = 7.5
X sum = 99.0
Y sum = 82.5
x y x y x y x y
10.00 8.04 10.00 9.14 10.00 7.46 8.00 6.58
8.00 6.95 8.00 8.14 8.00 6.77 8.00 5.76
13.00 7.58 13.00 8.74 13.00 12.74 8.00 7.71
9.00 8.81 9.00 8.77 9.00 7.11 8.00 8.84
11.00 8.33 11.00 9.26 11.00 7.81 8.00 8.47
14.00 9.96 14.00 8.10 14.00 8.84 8.00 7.04
6.00 7.24 6.00 6.13 6.00 6.08 8.00 5.25
4.00 4.26 4.00 3.10 4.00 5.39 19.00 12.50
12.00 10.84 12.00 9.13 12.00 8.15 8.00 5.56
7.00 4.82 7.00 7.26 7.00 6.42 8.00 7.91
5.00 5.68 5.00 4.74 5.00 5.73 8.00 6.89
sum
99.0 82.5 99.0 82.5 99.0 82.5 99.0 82.5
Average
9.0 7.5 9.0 7.5 9.0 7.5 9.0 7.5
I II III IV
Source: Wikipedia
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Anscombe’s Quartet
-
2.00
4.00
6.00
8.00
10.00
12.00
- 5.00 10.00 15.00
Series I
-
1.00
2.00
3.00
4.00
5.00
6.00
7.00
8.00
9.00
10.00
- 5.00 10.00 15.00
Series II
-
2.00
4.00
6.00
8.00
10.00
12.00
14.00
- 5.00 10.00 15.00
Series III
-
2.00
4.00
6.00
8.00
10.00
12.00
14.00
- 5.00 10.00 15.00 20.00
Series IV
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A common audit test
A common audit test is to graph revenue, looking for
spikes, trends and seasonality.
In these examples, two audit clients, displaying
seasonality in accordance with underlying business
model.
One factor we look for is a year-end spike.
$-
$50,000
$100,000
$150,000
$200,000
$250,000
$300,000
Letting fees
$-
$20,000
$40,000
$60,000
$80,000
$100,000
$120,000
$140,000
$160,000
$180,000
Management Fees
Source: Hanrick Curran audit file
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Discontinuity
Other common issues
include
discontinuities such
as spikes, slope
changes and steps.
Graphing outputs can
also help with
identifying spikes
from data entry or
formula errors.
Source: F1F9, 31 day on-line learning
50. 50experience. new thinking
Stephen Few, Perceptual Edge
Stephen Few’s work on visual communication is well
worth investigating as part of developing your team’s
use of excel.
Typically a board paper might include a table such as
exhibit A. The problem with this is that the data does
not provide the reader with any insight into the data.
Using Excel’s graphs, providing a visual presentation of
the graph allows insights (see next slide).
Sales ($'000) Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Domestic 1,893 2,343 2,593 2,283 2,574 2,838 2,382 2,634 2,938 2,739 2,983 3,493
International 574 636 673 593 644 679 593 139 599 583 602 690
2,467 2,979 3,266 2,876 3,218 3,517 2,975 2,773 3,537 3,322 3,585 4,183
Exhibit A: Sales data table
Source: Stephen Few “Visual Communication” IBM
Whitepaper, April 2009 (p. 2)
51. 51experience. new thinking
Stephen Few, Perceptual Edge
From the data at right for a typical sales graph,
we can observe:
Domesitc sales trend upwards across the
year
International sales are relatively flat across
the year
An exception in international sales is noted
in August
There is a cyclical pattern in domestic sales,
being lowest in the first month of the
quarter and then growing through the
quarter
From the graph, we might infer:
Sales staff may be going light in the first
month of the quarter and start working
harder as the quarter progresses in order to
meet their quarterly targets.
Perhaps there is an element of ‘channel
stuffing’ happening at the end of the
quarter.
Why the year-end spike?
Using Excel’s full potential enables this analysis. -
500
1,000
1,500
2,000
2,500
3,000
3,500
4,000
Jan Mar May Jul Sep Nov
Domestic International
Source: Stephen Few “Visual Communication” IBM
Whitepaper, April 2009 (p. 2)
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All is not lost, here is a 7 step
process to review your
spreadsheets.
7 steps to assure your
spreadsheets
53. 53experience. new thinking
7 steps to spreadsheet assurance
Control environment
Model design
Inputs and assumptions
Formula design and calculations
Output assessment
Change and version control
Reporting
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Control environment
Review what spreadsheets exist and how
they are controlled and developed
Review access and security arrangements
for spreadsheets
Consider risk assessment for in-use
spreadsheets
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Model design
Consider overall design and implementation
of spreadsheets
Consider domain related information
needed to understand model designs (e.g.,
are experts required such as geologists)
Consider periodicity and format consistency
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Inputs and assumptions
Review inputs and assumptions, consider
approval requirements for assumptions
included in spreadsheets
Consider inputs for data-entry errors
See: ASIC v MacDonald (No 11) [2009] NSWSC 287
(James Hardy case)
Do key assumptions need board level approval?
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Formula design and calculation
Check formula for calculation consistency and
accuracy
Examine model for potential errors including:
hard coding, reference failures
How long can this take?
Hanrick Curran were recently asked to review a
transaction model with 1,173 unique formula and
50,028 total formula.
At 1 minute per unique formula, that equates to
19.5 hours of review time or 2.6 days of just
looking at formula. And this doesn’t even allow
time to consider domain errors.
58. 58experience. new thinking
Output assessment
Review model outputs for consistency
Consider information accuracy
Does the model promote effective decision
making
59. 59experience. new thinking
Change and version control
Consider change controls implemented over the
reviewed spreadsheet, including password
protection
Consider version controls implemented over the
model and to whom ownership of the
spreadsheet is delegated
Tip: use a descriptive file name that includes information
regarding status and version or date of the spreadsheet.
Tip: put dates in YYYYMMDD order to enable auto-sorting
Example: “division budget review (v2.3)(DRAFT).xlsx”
Example: “20140612”
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Some suggestions on how to
implement some key lessons from
todays topics…
A brief summary and some
practical solutions
62. 62experience. new thinking
In summary
Errors will happen, if you plan well, their
impact can be minimised.
Most errors are not material, but do you
want to be the example that proves the
rule?
Most errors are not actually on/in the
spreadsheet.
63. 63experience. new thinking
Practical solutions (I)
Set organisational spreadsheet standards;
have a “this is the way we do it here.”
Implement a best practice standard
(i.e., FAST).
Implement training in how to use Excel and
how to design spreadsheets.
64. 64experience. new thinking
Practical solutions (II)
Demand better presentation of information … in
a way that supports decision making.
(“But this requires better training to start with.”)
Stocktake where you are using spreadsheets …
assess where your vulnerability lies and
address key risks.
Implement a review process for key
spreadsheets with external review if needed
(e.g., internal audit, external audit or domain
specialists).
65. 65experience. new thinking
Practical solutions (III)
At a basic level, for ad hoc spreadsheets:
Layout your work
Use styles
Format sheet well/properly
Don’t hard code
Don’t use remote references
Include graphs
Take time to check and review your work
Document information sources
66. 66experience. new thinking
Practical solutions (IV)
For more complex spreadsheets:
Use a standard format & style
Involve review and signoff of key inputs &
assumptions in the spreadsheet
Build-in error checks
Keep cross links to a minimum
Keep all links between worksheets on a
single page
Implement version and change controls
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Self-checking your spreadsheets to
avoid errors in decision making.
Anthony Berglas
Spreadsheet Detective
75. 75experience. new thinking
More resources
www.f1f9.com
“Well worth trying their 31 day free online course for a brush-up on your
excel skills.”
www.spreadsheetdetective.com
“Use the tools we use, to understand and self-audit your model.”
www.asap-utilities.com
“Great tools for every excel user. If you ever work with data, you need
these tools.”
www.perceptualedge.com
“For enlightening analysis and communication.”
This was the final straw. This formula was in a model prepared by a consultant that I was asked to review.
There had to be a better way.
Flexible in short term – run scenarios
Adaptable in long term – progressively add more detail as the project is developed
Often people will model the bit of the business they know about to the nth degree, when its impact is swamped by a few key variables eg sugar price
I can review any FAST model and readily understand how it hangs together and follow the logic of it
Interestingly, it is this structured approach that makes the model flexible
The 1.4m formula I showed earlier was not transparent. Transparency is born of simplicity.
A quick example to show the essence of the FAST Standard.
The spreadsheet engineering should not be creative. The creative process should be around the business questions to be answered.
There is a learning curve, but once learnt the task of building a model is a lot faster because of specific techniques used. Additionally, changes, adaptation and review processes are a lot quicker.