PLN08 BO3 Hubbard How to Measure Anything

Copyright HDR 2008 Copyright HDR 2008 [email protected]@hubbardresearch.com

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How To Measure Anything:

Finding the Value of Intangibles in Business


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How to Measure Anything• In the past 12 years, I have conducted

over 60 major risk/return analysis projects that included a variety of “impossible” measurements

• I found such a high need for measuring difficult things that I decided I had to write a book

• The objective today is to explain an approach to making the “immeasurable” measurable and the surprising things we find when we accomplish this

What is AIE?

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Modern PortfolioModern PortfolioTheoryTheory

AppliedAppliedInformationInformationEconomicsEconomics

EconomicsEconomics

Decision/Game Decision/Game TheoryTheory

StatisticsStatistics

Information TheoryInformation TheoryOptionsOptionsTheoryTheory

Operations ResearchOperations Research

Applied Information Economics (AIE) is the practical application of scientific and mathematical methods to quantify the value of management choices - regardless of how difficult the

measurement challenge appears to be.

Applied Information Economics (AIE) is the practical application of scientific and mathematical methods to quantify the value of management choices - regardless of how difficult the

measurement challenge appears to be.

• “Quantifying the risk and comparing its risk/return with other investments sets AIE apart from other methodologies. It can substantially assist in financially justifying a project -- especially projects that promise significant intangible benefits.” The Gartner Group

• “AIE represents a rigorous, quantitative approach to improving IT investment decision making…..this investment will return multiples by enabling much better decision making. Giga recommends that IT executives learn more about AIE and begin to adopt its tools and methodologies, especially for large IT projects.” Giga Information Group


A Few Measurement Examples• Risk of IT• The Risk of obsolescence• The value of a human life• The value of saving an

endangered species• The value of better

information• The value of public health• Forecasting fuel demand

for the battlefield


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• The value of better security

• The effects of an initiative when many other variables affect performance

• The future demand for space tourism

• The risk of a .com venture capital startup

• The risks of a major construction project


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Three Illusions of Intangibles(The “howtomeasureanything.com” approach)

• The perceived impossibility of measurement is an illusion caused by not understanding:– the Concept of measurement– the Object of measurement– the Methods of measurement

• See my “Everything is Measurable”article in CIO Magazine (go to “articles”link on www.hubbardresearch.com


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Uncertainty, Risk & Measurement

• The “Measurement Theory” definition of measurement: “A measurement is an observation that results in information (reduction of uncertainty) about a quantity.”

• An Actuary's approach to Risk Measurement: “To quantify probability and loss of an undesirable possibility”

• The value of a Measurement: “The monetized reduction in risk from making decisions under less uncertainty”

• We model uncertainty statistically – with Monte Carlo simulations

Measuring Uncertainty, Risk and the Value of Information are closely related concepts, important measurements themselves, and precursors to most other measurements

Ideal vs. Real-world Measurements

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Normal Distribution

Uniform Distribution

Lognormal Distribution

Hybrid

Threshold confidence 15% 85%

Ideal Values: Point

Real-world Meas.

Assumptions: Most values in business cases are represented as exact values – even though exact values are almost never known

No Assumptions: Most things we DO know are better represented by ranges and probabilities – we don’t have to assume anything we don’t really know.



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An Approach That Works1. Define the relevant decision and clarify the

inputs2. Model what you know now3. Compute the value of additional information4. Measure where the information value is

high5. Update the model and optimize the decision


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Defining the Decision• The EPA needed to compute the ROI of the Safe Drinking Water

Information System (SDWIS)• As with any AIE project, we built a spreadsheet model that connected the

expected effects of the system to relevant impacts – in this case public health and its economic value

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Model What You Know• Decades of studies show that most managers are

statistically “overconfident” when assessing their own uncertainty– Studies showed that bookies were great at assessing odds

subjectively, while doctors were terrible

• Studies also show that measuring your ownuncertainty about a quantity is a general skill that can be taught with a measurable improvement

• Training can “calibrate” people so that of all the times they say they are 90% confident, they will be right 90% of the time


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Calibration Test Examples• How many grams does the average Jelly Belly Jelly

Bean weight? – _____ to ____ (90% Confidence)

• What is the height in meters of the Sears Tower?– _____ to ____ (90% Confidence)

• Idaho has a larger area than Iraq. – True/False ____% Confidence

• In the English Language, the word “strategy” is used more often than the word “celebrate”.• True/False ____% Confidence


90% Confidence IntervalCalibrated probability assessment

results from various studies

Calibrated Estimates: Ranges


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Giga Analysts

Giga Clients

Statistical Error

“Ideal” Confidence

30%

40%

50%

60%

70%

80%

90%

100%

50% 60% 80% 90% 100%

25

75 71 65 58

21

17

68 15265

4521

70%Assessed Chance Of Being Correct

Perc

ent C

orre

ct

99 # of Responses

Calibrated Estimates of Events• 1997: An experiment Hubbard conducted with Giga Information

Group proves people can be trained to assess probabilities of uncertain forecasts

• Hundreds have been calibrated since then• Calibrated probabilities are the basis for modeling the current state

of uncertainty

The Value of Information


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EVI p r V p r V p r V p r EVi j j ij

z

j j i l j j ij

z

j

z

i

k

=⎡

⎣⎢

⎤

⎦⎥ −

= ===∑ ∑∑∑ ( ) max ( | ), ( | ),... ( | ), *, , ,1

12

111Θ Θ Θ

The formula for the value of information has been around for almost 60 years. It is widely used in many parts of industry and government as part of the “decision analysis” methods – but still mostly unheard of in the parts of business where it might do the most good.

What it means:1.Information reduces uncertainty2.Reduced uncertainty improves decisions3.Improved decisions have observable consequences with measurable value

The EOL Method• The simplest approach computes the change in “Expected Opportunity

Loss”• “Opportunity Loss” is the loss (compared to the alternative) if it turns out you

made the wrong decision• Expected Opportunity Loss (EOL) is the cost of being wrong times the

chance of being wrong• The reduction in EOL from more information is the value of the information.• In the case of perfect information (if that were possible) the value of

information is equal to the EOL.• Simple Binary Example: You are about to make a $20 million investment to

upgrade the equipment in a factory to make a new product. If the new product does well, you save $50 million in manufacturing. If not, you lose (net) $10 million. There is a 20% chance of the new product failing. What’s it worth to have perfect certainty about this investment if that were possible?

• Answer: 20% x $10 million = $2 million


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• The value of information is computed a little differently with a distribution, but the same basic concepts apply

• For each variable, there is a “Threshold” where the investment just breaks even

• If the threshold is within the range of possible values, then there is a chance that you would make a different decision with better measurements

90% Confidence

IntervalThreshold Mean

5% tail 5% tail

ThresholdProbability

Information Value w/Ranges


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Normal Distribution VIA• The curve on the other side of the threshold is divided up into hundreds of

“slices”• Each slice has an assigned quantity (such as a potential productivity

improvement) and a probability of occurrence• For each assigned quantity, there is an Opportunity Loss• Each slice’s Opportunity Loss is multiplied by probability to compute its

Expected Opportunity Loss• The total EOL for all slices is the EVPI of the uncertain variable

Productivity Improvement in Process X

# effected by virus: 15%Opportunity Loss: $1,855,000Probability: 0.0053%EOL: $98.31

# effected by virus: 15%Opportunity Loss: $1,855,000Probability: 0.0053%EOL: $98.31

25%20%15%10%5%


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Total of all EOL’s = $58,989

Dol

lar V

alue

/Cos

t EVI

MaximumENBI

ECI

ENBI

Increasing Value & Cost of Info.

• EVPI – Expected Value of Perfect Information

• ECI – Expected Cost of Information

• EVI – Expected Value of Information

• ENBI – Expected Net Benefit of Information

$0

$$$

Low accuracyHigh accuracy

EVPIAim for this range


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• The value of information levels off while the cost of information accelerates• Information value grows fastest at the beginning of information collection• Use iterative measurements that err on the side of “small bites” at the steep

part of the slope


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The Measurement Inversion

Typical Attention

Econ

omic

Rel

evan

ce

Measurement Attention vs. Relevance

• After the information values for over 3,500 variables was computed, a pattern emerged.

• The highest value measurements were almost never measured while most measurement effort was spent on less relevant factors

– Costs were measured more than the more uncertain benefits

– Small “hard” benefits would be measured more than large “soft” benefits

• Also, we found that, if anything, fewermeasurements were required after the information values were known.

See my article “The IT Measurement Inversion” in CIO Magazine(its also on my website at www.hubbardresearch.com under the “articles” link)


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Next Step: Observations• Once we’ve determined what to measure, we

can think of observations that would reduce uncertainty

• The value of the information limits what methods we should use, but we have a variety of methods available

• Take the “Nike Method”: Just Do It – don’t let imagined difficulties get in the way of starting observations


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Practical Assumptions• Its been measured before• You have more data than you think• You need less data than you think• Its more economical than you think• Your subjective estimate of possible

measurement errors is exaggerated

“It’s amazing what you can see when you look”Yogi Berra

The “Math-less” Statistics Table• Measurement is based on

observation and most observations are just samples

• Reducing your uncertainty with random samples is not made intuitive in most statistics texts

• This table makes computing a 90% confidence interval easy


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Measuring to the Threshold• Measurements have

value usually because there is some point where the quantity makes a difference

• Its often much harder to ask “How much is X” than “Is X enough” Samples Below Threshold

20%

30%

40%

50%

0.1%

1%

10%

4 5 6 7 8 9 10

2 4 6 8 10 12 16 20Number Sampled

Cha

nce

the

Med

ian

is B

elow

the

Thre

shol

d

1 2 3

1814

2%

5%

0.2%

0.5%

0


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Statistics Goes to War

• Several clever sampling methods exist that can measure more with less data than you might think

• Examples: estimating the population of fish in the ocean, estimating the number of tanks created by the Germans in WWII, extremely small samples, etc.


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Reducing Inconsistency• The “Lens Model” is another method used to improve on expert intuition• The chart shows the reduction in error from this method on intuitive estimates• In every case, this method equaled or bettered the judgment of experts

0% 10% 20% 30% 40%

Cancer patient life-expectancy

Life-insurance salesrep performance

Graduate students gradesChanges in stock prices

Mental illness using personality tests

Student ratings of teaching effectiveness

IQ scores using Rorschach tests

Psychology course grades

Business failures using financial ratios

Reduction in Errors

Battlefield Fuel ForecastsIT Portfolio Priorities My StudiesMy Studies

Source: Hubbard Decision Research


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The Simplest Method• “Bayesian” methods in statistics use new information to update prior

knowledge• Bayesian methods can be even more elaborate that other statistical

methods BUT…• It turns out that calibrated people are already mostly “instinctively

Bayesian”• The instinctive Bayesian approach:

– Assess your initial subjective uncertainty with a calibrated probability– Gather and study new information about the topic (it could be qualitative or

even tangentially related)– Give another subjective calibrated probability assessment with this new

information• In studies where people were asked to do this, thier results were

usually not irrational compared to what would be computed with Bayesian statistics – calibrated people do even better

Comparison of Methods• Traditional non-Bayesian

statistics (what you probably learned in the first semester of stats) assumes you knew nothing prior to the samples you took - this is almost never true in reality

• Most un-calibrated experts are overconfident and slightly overemphasize new information

• Calibrated experts are not overconfident, but slightly ignore prior knowledge

• Bayesian analysis is the perfect balance; neither under- nor overconfident, uses both new and old information


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Ignores Prior Knowledge; Emphasizes new data

Ignores New data; Emphasizes Prior

Knowledge

Under-confident (Stated

uncertainty is higher than

rational)

Overconfident (Stated

uncertainty is lower than rational)

Calibrated Expert

Bayesian

Typical Un-calibrated

Expert

Non-BayesianStatistics

StubbornGullible

OverlyCautious

Vacillating,Indecisive


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Risk/ROI w/ “Monte Carlo”

Administrative Cost Reduction

Total Project Cost

Customer Retention Increase

5% 10% 15%

10% 20% 30%

$2 million $4 million $6 million

ROI-50% 50% 100%0%

• A Monte Carlo simulation generates thousands of random scenarios using the defined probabilities and ranges

• The result is a range ROI not a point ROI

Quantifying Risk Aversion


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Acceptable Risk/Return Boundary

Investment Region

• The simplest element of Harry Markowitz’s Nobel Prize-winning method “Modern Portfolio Theory” is documenting how much risk an investor accepts for a given return.

• The “Investment Boundary” states how much risk an investor is willing to accept for a given return.

• For our purposes, we modified Markowitz’s approach a bit.

Investment

Define Decision Model

Define Decision Model

Calibrate EstimatorsCalibrate

Estimators

Conduct Value of Information Analysis (VIA)

Conduct Value of Information Analysis (VIA)

Measure according to VIA

results and update model

Measure according to VIA

results and update model

Populate Model with Calibrated

Estimates & Measurements

Populate Model with Calibrated

Estimates & Measurements

Analyze Remaining Risk

Analyze Remaining Risk

Optimize DecisionOptimize Decision

“Full” RRA in 7 Steps

3030Copyright HDR 2008 Copyright HDR 2008 [email protected]@hubbardresearch.com


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Forecasting Fuel for Battle

• The US Marine Corps with the Office of Naval Research needed a better method for forecasting fuel for wartime operations

• The VIA showed that the big uncertainty was really supply route conditions, not whether they are engaging the enemy

• Consequently, we performed a series of experiments with supply trucks rigged with GPS and fuel-flow meters


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Reactions: Fuel for the Marines• “The biggest surprise was that we can save so much

fuel. We freed up vehicles because we didn’t have to move as much fuel. For a logistics person that's critical. Now vehicles that moved fuel can move ammunition.“Luis Torres, Fuel Study Manager, Office of Naval Research

• “What surprised me was that [the model] showed most fuel was burned on logistics routes. The study even uncovered that tank operators would not turn tanks off if they didn’t think they could get replacement starters. That’s something that a logistician in a 100 years probably wouldn’t have thought of.” Chief Warrant Officer Terry Kunneman, Bulk Fuel Planning, HQ Marine Corps


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Final Tips• Learn how to think about uncertainty, risk and

information value in a quantitative way• Assume its been measured before• You have more data than you think and you

need less data than you think• Methods that reduce your uncertainty are more

economical than many managers assume• Don’t let “exception anxiety” cause you to avoid

any observations at all• Just do it


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Questions?Doug HubbardHubbard Decision [email protected] 858 2788• If you want electronic copies of this presentation and

copies of supporting articles I mention, please leave me a business card with “Presentation” written on the back

PLN08 BO3 Hubbard How to Measure Anything

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