Quantitative Methods Make A Difference

Overview Quantitative methods (probabilistic analysis, operations research, etc.) are widely used in other industries, but mostly lacking in IT investment analysis Over the past 7 years, we have been focusing specifically on the application of more advanced quantitative methods to IT This presentation will review the key findings from the application of quantitative methods to over 30 projects

Quantitative Methods Include: Computing uncertainties and risks Computing the economic value of information Measurement methods for many items usually considered intangible Optimizing solutions when there are huge combinations of options for: Roll-out priorities of systems Selection of a portfolio of functions Any other problem where different alternatives about different aspects of the investment generate lots of possibilities

Empirical Decision Theory Method: AIE Applied Information Economics is the practical application of scientific and mathematical methods to quantify the value of IT-enabled business investments Economics Decision/Game Theory Empirical Decision Theory Operations Research Applied Information Economics Modern Portfolio Theory Statistics Information Engineering Information Theory Software Metrics

Some HDR Clients Booz-Allen & Hamilton Accenture w/ the State of North Carolina Giga Information Group American Express The Discovery Channel U.S. Federal Government: Department of Treasury Bureau of The Census Department of Veterans Affairs General Services Administration Housing and Urban Development The Axa Group – 6 major companies The Banking Industry Technology Secretariat Blue Cross Blue Shield of Illinois

What Do the Critics Say? “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 “AIE-like methods must become the standard way to make (IT) investment decisions.” Forrester Research, Inc.

Five Key Revelations Quantifying risk radically changes IT investment priorities Current measurement priorities are “upside-down” when compared to priorities based on economic value of information “Technology regret” is a significant and overlooked factor in the the value of IT investments The true cost of “scope creep” is much higher than most would think The value of quantitative analysis would make it the best investment in most IT portfolios

Finding 1: Risk Analysis When IT computes risk in the same way that an actuary would, many IT investments will look completely different We define risk a “The probability of a quantified loss” Risk analysis of IT investments involves a probabilistic analysis of all uncertain variables

Real-world Measurements vs. Ideal Values Ideal Values: Point Real-world Meas. Normal Distribution Uniform Distribution Lognormal Distribution Hybrid Threshold confidence 15% 85%

Calibrated Estimates Measuring your own uncertainty about a quantity is a general skill that can be taught with a measurable improvement Studies show that most managers are statistically “overconfident” when assessing their own uncertainty Training can “calibrate” people so that when they provide a 90% confidence interval, it still has a 90% chance of being right (even though it is subjective) When asked to provide a subjective 90% confidence interval, most managers provide a range that only has about a 40%-50% chance of being right Perceived 90% Confidence Interval Actual 90% Confidence Interval

Distribution-based ROI Administrative Cost Reduction Total Project Cost % Improvement in Customer Retention 5% 10% 15% 20% 30% $2 million $4 million $6 million ROI -50% 50% 100% 0% Inputs

Analyzing the Distribution ROI = 0% “Expected” ROI Risk of Negative ROI Probability of Positive ROI The “cancellation hump” -25% 0% 25% 50% 75% 100% 125% Return on Investment (ROI)

Plotting the Risk and Return 40% A proposed IT investment with a 15% risk and 54% return 30% Probability of less than a risk-free return 20% X 10% Return 10% 20% 30% 40% 50% 60%

Example of Risk Effects 50% These are real IT investments of $2M-$3M plotted against a client’s investment boundary The 27% ROI investment is actually preferred to the 83% ROI investment Region of Unacceptable Investments 40% 30% Chance of a negative IRR 20% Region of Acceptable Investments 10% 0% 0% 50% 100% 150% 200% Expected IRR over 5 years

Risk Increases Required ROI’s Adjusting for risk causes some previously-acceptable projects to be rejected Also, some low return but low risk projects would now be acceptable More projects with “intangible” benefits are now economically justified The net result: A completely reshuffled deck of IT project approvals 10% 100% 1000% 20% 40% 60% 80% 30% 50% 200% 300% 500% Size of the Project Relative to the Entire IT Portfolio (i.e. 50% = project makes up half the work in the entire portfolio) Required Minimum Return (IRR over 5 years) Most Risk Averse Approximate Median Most Risk Tolerant Range of Typical “Hurdle Rates”

Using Risk Analysis to Improve Decisions If the Risk is significant (it usually is), consider doing the following: Reduce the size and functionality of the proposed system - focus on fewer high-return features Wait until specific uncertainties in the environment subside - e.g. major mergers, reengineering, etc. Wait to tackle big projects until proper skills are developed and methods are in place Consider purchased packages that aren’t a perfect fit but close enough - they may look more attractive now Invest more on a proper economic analysis of the largest IT investments - this should reduce uncertainty about critical quantities Include deferred benefits in any estimate of scope creep costs

Finding 2: Measurements Information has a value that can be calculated with a formula known for 50 years Computing the value of additional information on uncertain variables causes some surprising changes in what gets measures

The Economic Value of Information The Decision Theory Formula: What it means: Information reduces uncertainty Reduced uncertainty improves decisions Improved decisions satisfy business objectives (by definition)

The IT Measurement Inversion Receives Most Attention Least Relevant to Approval Decisions Costs Initial Development Costs Ongoing Maintenance/Training Costs Benefits A specific benefit (productivity, sales, etc.) Utilization (when usage starts and how quickly usage grows) Chance of Cancellation Economic Relevance Typical Attention Receives Least Attention Most Relevant to Approval Decisions See my article “The IT Measurement Inversion” in CIO Magazine (its also on my website at www.hubbardresearch.com under the “articles” link)

Finding 3: Technology Regret Most business cases treat IT investments implicitly as a “now or never” choice Technology regret is an economic quantity associated with committing to a technology after which, for whatever reason, becomes regrettable The equivalent of “buyers remorse” Technology regret becomes and important consideration in any environment where changing technology is a constant The issue becomes balancing technology regret (which tends to defer IT investments) vs. the opportunity loss of deferring the benefits of making the investment now

Relative Computing Power Changing Technology 32% Annual Growth Rate 350 Some Areas of Growth: Processors & Memory (Moore’s Law) Storage Communications (Payne’s Law) Internet Users Sensory devices 300 250 Relative Computing Power Per $ (1980=1) 200 150 100 50 1980 1985 1990 1995 2000 Year Competition makes capitalizing on new technology more critical to survival

Changing With Technology How often should you change with technology? Avoiding “technology regret” is often a major driver in IT decisions. Upgrade 2 A Critical Technology Measure Upgrade 1 Time

“Real Option” Theory - + The option value tells us when an investment, even if it looks positive now, should be deferred until the opportunity is better In the case of IT, waiting for the possibility of better technology around the corner should be considered - Re-evaluate in the next decision cycle Reject the investment Invest this cycle, low priority, may be deferred if resources are strained Single Period Option Value (Value of Waiting one period) Invest in this cycle, high priority + Net Value of the Investment

The Effects of Tech Regret Very long duration IT projects that commit to a proprietary solution tend to look much less favorable Short turnaround projects based on non-proprietary standards tend to look better Large scale commitments to the fastest improving technologies (like data storage, bandwidth) tend not to be favorable

Finding 4: Scope Creep The cost of adding one additional function to an software development project is rarely addressed properly If anything, the only cost of new features considered is development cost Long term maintenance, increased risk of cancellation plus deferred benefits is much more significant

True Scope Creep Costs 24%: Initial development costs 24%: Future maintenance costs (computed over 5 years) 1%: Incremental contribution to probability of total project cancellation 51%: Deferred benefits of the other functions delayed by the proposed new function 24% 51% 24% 1%

Finding 5: Value of Quant. Organizations have successfully adopted more advanced quantitative methods for evaluating IT investments The cost of analysis routinely comes in below 1% and has always been under 2% of the investment size - including initial training This is still less than non-IT investments of similar size and risk It is also sometimes less time-consuming than the previous non-quantitative analysis techniques used by the firm (One of the reasons this analysis is efficient is we conduct a Value of Information Analysis - we only measure what is economically justified) Using the standard VIA calculation for the value of AIE analysis, AIE itself was the best investment of all the IT investments we analyzed - very conservative measures of payoffs put $20 to every $1 spent on AIE

Overview of RRA Analysis Classification 5% 10% 15% 20% 30% $2 mill $4 mill $6 mill Measurements Value of Info. $ Intangibles “Customer Satisfaction” “Strategic Alignment” “Technology Risk” “Information Quality” etc. $$$ $$ Measurables Errors in Decision X Change to Strategic Measure M Productivity in Activity Y Chance of cancellation, etc. Each company will decide on its own what “compliance”, “strategic” and “economic” mean precisely. Also, they will determine their own boundaries on the classification chart and the specific analysis requirements in each “zone” of the chart. Risk Calculate Risk/Return Position "expected" ROI 50% 100% 150% 200% 250% -50% 0% Probability of a negative ROI Probability of a positive ROI Organization's investment limit Acceptable region of investment Return

In Conclusion… Quantitative methods like AIE cause major IT decisions to be very different – and better Advanced methods can and have been learned and adopted by IT organizations More quantitative analysis can be the highest return investment in your IT portfolio