As readers of this occasional blog know, this blog has been less of a 'web log' and more a series of small essays on the topic of development analytics. I have decided to start writing less formal entries more frequently and have realized I would be comfortable doing that on my own web site, murraycantor.com.
I want to be entirely clear. IBM has in no way looked over my shoulder in the writing of the blog and has been very generous in providing me a forum. Nevertheless, I will be freer sharing my opinions when there is no opportunity of confusing my often idiosyncratic opinions of those of the company's.
So check out the new blog at www.murraycantor.com/blog. I hope to write something at least twice a month, maybe more often.
Meet you there and thanks!
Murray
Modified by mcantor@us.ibm.com 110000CH6X

In my previous couple of blog entries, I used triangular distributions for examples. For many who suffered through (or maybe enjoyed) their stat classes (what are the odds?), this might be a surprising choice. They were taught the default choice would be a Gaussian distribution. For those more attuned with modern business analytics, they are likely to be familiar with triangular distributions. In this entry, I'll briefly the reasoning beyond each of them. First, as you hopefully recall, both are distributions associate with random variables (Those who don't recall migh benefit from the series of tutorials at The Khan Academy site). Each are nonnegative functions with integral (area under the curve) one. (There are fancier mathematical definitions, but no matter.) Each describes the likelihood of each of set of possible outcomes of some random variable. The difference in shape between Gaussian (aka Normal) and triangular distributions reflects the nature and use of the random variables. Briefly, normal distributions are often arise as the histogram of a set of measurements. They have some central value (called the mean) and some dispersion (called standard deviation) around the mean. Anyone who took a stat class studied these distributions. They show up in a many contexts:  The distribution resulting from tabulating the histogram of repeated, but imprecise measures of some quantity and then divided the entries by the sum of the measures is often assumed to be normal. The mean of the distribution is the estimator of the actual measure.
Statisticians like the normal distribution for several reasons. First, it is easy to parameterize. If you know the mean. mu (μ), and the standard deviation, sigma (σ), you have completely characterized the distribution. For example, the likelihood of a measurement occurring is often characterized as being within some number of σ's from the mean. Figure 1 shows how this works. The likelihood of a value falling in a range is given by the area under the curve. For example, the probability of a value of the normally distributed random variable falling within one standard deviation of the mean is 68.2%. Normal distributions have one really cool feature called the Central Limit Theorem, which states that under remarkably general conditions, the sum of a set of random variables will be close to normal. Notice, in the previous blog entry, when we added two triangular random variables, the sum appeared smooth and in fact started to look normal. All that said, I do have have a pet peeve. Normal distributions are overused. Most things in nature and economics are not normally distributed. For example, as as documented in Wikipedia, these phenomena are nowhere near normal, but are closer to a Pareto distribution:  The sizes of human settlements (few cities, many hamlets/villages)
 File size distribution of Internet traffic which uses the TCP protocol (many smaller files, few larger ones)
 Hard disk drive error rates
 The values of oil reserves in oil fields (a few large fields, many small fields)
 The length distribution in jobs assigned supercomputers (a few large ones, many small ones)[citation needed]
 The standardized price returns on individual stocks
 Fitted cumulative Pareto distribution to maximum oneday rainfalls
 Sizes of sand particles
 Sizes of meteorites
 Areas burnt in forest fires
 Severity of large casualty losses for certain lines of business such as general liability, commercial auto, and workers compensation.
Getting back to our topic, let's turn to triangular distributions. They are not used to describe a set of measured outcomes from an experiment. They are used to describe what we know or believe about some unknown random variable. For example, the sales of a new product one year after delivery generally can not be determined by measuring the sales of a bunch of new products. As pointed out by Douglas Hubbard, treating the future sales as a single fixed variable is unreasonable (although all too common). What is more reasonable is setting the low (L), high (H) , and most likely (E) values of the future sales. As I wrote in an earlier entry, these are the values that specify a triangular distribution. I.e. triangular distributions are set to zero below a given low value, L, and above the high value, H, and peaks at the expected value E. The distribution is then a describe be a triangular curve so that the total area is 1. Here is the distribution for L = 1, E=6, and H=7. Some would argue there is a 'real' distribution of the future sales random variable and it is unlikely to be triangular. My response is for all practical purposes, it does not matter. The triangular distribution is a goodenough approximation to whatever the real distribution might be. By 'good enough' I mean they may be used to support decision making: they are a big improvement over using single values. They are also practical as they easy to specify and there is no assumption of symmetry, No wonder they are common in business analytics. To wrap up, normal distributions are occasionally useful to describe outcomes of measurements while triangular distributions are useful for giving rough estimates of one's belief of the liklihood of outcomes based on the evidence on hand. More generally, normal distributions are useful in frequentist statistics and triangular in Bayesian statistics. See this Wikepedia article for a discussion of the kinds of statistics. Much of what we do in development analytics is more Bayesian than frequentist. I hope to write more about that in the near future.

I would like to build on the theme of reasoning about what to measure. The goal of business analytics is to track what matters to the organization (what it is you are trying to manage) and respond to the measure in some way to gain improvement. The science of measuring outcomes and In manufacturing and some service delivery domains is statistical process control (SPC), SPC lies at the heart of the Six Sigma movement. Even so, there will be no need to have a 6six sigma belt to participate in this discussion . While there is reason to believe that not all of the Six Sigma practices do not apply all that well to our domain, the idea of tracking outcomes, applying statistical analysis to detect change change, and applying some sort of controls to affect the change applies in all business domains, including software and system development and delivery. Briefly then, the outcomes are the operational goals and controls are the actions you take to achieve the outcomes, So naturally we need too kinds of measures.  Outcome measures  tracking the measures of effectiveness of the business organization
 Control tracking whether  tracking whether the controls are in fact enacted.
Here is a thought experiment:. Imagine there is a potato chip factory with an operational goal of achieving the right amount of salt on its chips. There is an target amount and the factory needs to stay within small limits for market acceptance. So everyday they grab a sample of chips and record the saltiness. They apply salt by running the recently deep fried chips under a salt shaker. The two controls are the frequency of the shaker and the speed of the belt. Both the shaker frequency and belt speed are measured to confirm the controls are properly responded to. In this example the saltiness is the outcome measure and the shaker frequency and belt speed are control measures. The simplest way, for me at least, to think about SPC is to measures trends in outcome measures and control measures to determine the likelihood that the controls are in fact affecting the outcome. In our potato chip example we might find that we cannot control the outcome well enough by the shaker and belt controls. In that case, we might look for some other factor to control, say the factory humidity. If you look at many measurement programs in software and system you often find that outcome and measures are confused. In fact even sorting the measures into the two buckets is hard. No wonder measured process improvement for our domain has been so hard.Anyone have good examples of measurement patterns or antipatterns of measuring controls and outcomes? Again stay tuned for more....

When I started this experiment in blogging, I wrote that I am not a natural blogger. I am not the affable, chatty web presence who on a daily or weekly basis shares one's thoughts. I have learned since then what kind of blogger I am. My style is to write little essays that might take weeks to prepare, given the priorities of my day job. I have also found I do enjoy writing the blog as it gives me a chance to share some issue that is top of mind. So here goes: A few weeks ago, my good colleague displayed a chart in one of his PowerPoint decks entitled something like "How to Understand Murray." The chart was an explanation of probability distributions. It was both flattering and a bit of a wakeup call. As Arthur mentioned and the readers of this blog know, much, if not all, of my writing assumes an understanding of probability and probability distributions (aka probability densities). My experience in discussions with folks from our industry is that most of them have vague memories from some stat class in college and so can generally follow the discussions, but most could use a refresher. I could simply refer readers to a good Wikipedia article, but, instead, let me given a domainspecific example. Let's go with a topic I wrote about in a previous entry: time to ship. For explanation purpose, let's take a fictional example. Suppose you are starting a project expected to ship in 110 days. That said, we cannot be 100% certain of being ready to ship on exactly that day, no sooner, no later. In fact, it is very unlikely we will exactly hit that day. Maybe we will be ready the day before, or maybe the day before that. Since being ready on or any day before day 110 is success, we can sum up the probabilities on being ready on any of those days to get the probability we really care about. All that said, the probabilities for each of the days matter because we need them in order to get the sum. The set of probabilities for each of those days is the probability distribution or density that is our topic. Let's look at a simple example. In this example of a triangular distribution there is 0% probability that the product will be ready before day 91 and we are 100% certain we will be ready before day 120. We think the days become more probable and reach a peak as we approach day 110 and fall off after that. This graph then shows the probability, day by day, of being ready on exactly that day. You might notice that the peak is less than 0.07 (actually 0.06666...). This makes sense since we are assuming that the project may be complete on any of 30 different days and so the densities would be in the neighborhood of 1/30 = 0.03333. In our case, some are above and some below. These distributions are the basis of calculating the likelihood of outcomes. The principle is very simple: The probability of being ready within some range of dates is the sum of the probabilities of being ready on exactly one of those dates, i.e. , we add up the density values for those days. As I explained above, if we want to compute the probability of being ready on or before day 110, we would add up all of the densities for days 90 to 110 to get 0.7. Using the same reasoning the probability of being ready on some day before day 120 is the sum of all the densities which comes to exactly 1.0, which was one of our going in assumptions. In fact the property that the sum of densities for all possible outcomes equals 1 is a defining property of distributions. Those who want to try this out at home could use this spreadsheet.. For example, can you find on what day, being ready on or before that day is an even bet? For most development efforts, the overall state of the program (some would say 'health') is characterized by the shape of the distribution, This shape changes every day. Every action the team takes changes the shape. So, one key goal of development analytics would be to track the shape of the distribution throughout the lifecycle, a daunting task. More on this (probably ) in future postings.

Today, April 1, seems like a good day to bring forward an important new idea. In fact, I think this may be the next big thing. One of the wellunderstood problems with software development project management is that it is often impossible to completely specify the complete work breakdown with certainty. The longer the project and the more innovative the project, the more uncertain the work breakdown items. This is addressed in iterative, agile planning by identifying the summary work items and then adding detail as the project evolves. Another source of uncertainty is the dependency between the summary items. This uncertainty in turn makes critical path analysis for such programs problematic. In fact there is a whole ensemble of project critical paths, each with some likelihood. For the physics literate, this ensemble of paths is much like Feynman Path Integrals in quantum theory. The math is pretty hairy (see this elementary description). Fortunately, as Feyman also pointed out, one can simulate quantum mechanics with quantum computers. I am no expert in quantum computing, but even so I have a proposal: Quantum Informed Projects (QuIPs). The idea is to represent work items as QItems using QBits from quantum computing.. Then we can represent the project as a set of entangled QItems and using a suitablly large quantum computer to calculate the wave function for the critical path. My understanding is that we do not yet have large enough enough quantum computers to make this practical. However, the same is true for implementing other useful quantum algorithms ( see this example). So we can start by building algorthms. There is no time like the present ( not accounting for the quantum uncertainty of measuing time) So on this special day, lets turn our attention to QuiPs.

It should not be a surprise that I have been following the
BP oil spill with much interest. In fact, as I starting typing this entry, I
was watching the grilling of the BP CEO, Tony Heyward, by Congress. Rep. Stupak
is focusing on the BP’s risk management.
Some of you have read my earlier posting on my thoughts of
the BP decision process that led to the Deepwater Horizon blowout. So far,
information uncovered since that posting is remarkably consistent with my
earlier suppositions. In this entry I would like to step back a bit and discuss
what broader lessons might be learned from the incident. While it is all too
easy to fall into BP bashing, I would rather use this moment to reflect more
deeply on risk taking and creating value. (BTW, some of you might now that my
signature slogan is ‘Take risks, add value’.)
In our industry we create value primarily through the
efficient delivery of innovation. Delivering innovation, by definition,
requires investing in efforts without initial full knowledge of the effort
required and the value of the delivery. This incomplete information results in
uncertainties in the cost, effort, schedule of the projects and the value of
the delivered software and system, i.e. cost, schedule and value risk.
Deciding to drill an oil well also entails investing in an
effort with uncertain costs and value. In this case, the structure of the
subsystem and productivity of the well cannot be know with certainty before
drilling. As I pointed out in an earlier blog, a good definition of risk is
uncertainty in some quantifiable measure that matters to the business. So in
both our industry and oil drilling we deliberately assume risk to deliver
value.
So, what can we learn from the BP incident? Briefly, one
creates value by genuinely managing risk. One creates the semblance of value
for a while by ignoring risk.
Assuming risk, investing in uncertain projects, provides the
opportunity for creating value. That value is actually realized by investing in
activities that reduce the risk. The model that shows the relationship is described
in this entry. So, reducing risk has economic value, but reducing risk
takes investment. In the end, the quality risk management is measured with a
return on investment calculation. This in turn requires a means to quantify and
in fact monetize risk.
I wonder what was there risk management approach was
followed by BP. A recent Wall Street Journal article suggested they used a risk
map approach – building a diagram with one axis a score of the ‘likelihood of
the risk’ and the other a score of the ‘severity of a failure’. So with this
method, they would score the risk of a blowout as very low (based on past
history) with a very high consequence. So, such a risk needs to be ‘mitigated’.
(Some actually multiply the scores to get to some absolute risk measure.) Their
mitigation was the installation of a blowout preventer. They could then
confidently report they have executed their risk management plan. Note these
scores are at best notionally quantified and not monetized.
Paraphrasing my good colleague, Grady Booch (speaking of
certain architecture frameworks), risk maps is the semblance of risk
management. As pointed out by Douglis Hubbard in The Failure of Risk Management (and in
an earlier rant in this blog), this sort of risk management is not only
common, but dangerous: It is a sort of business common failure mode that leads
to bad outcomes. Also, Hubbard points out, useful risk management entails
quantification and calculation using probability distributions and Monte Carlo
analysis. I would add that since risk management in the end is about business
outcomes, risks need to be monetized as well as quantified. I am willing to bet
a good bottle of wine that BP did no such thing. Any takers? The business
common failure mode was overreliance on the preventers, even though there are
several studies showing they are far from ‘failsafe’.
Further, it appears BP assumed risk by consistently taking
the cheaper, if riskier. design and procedure alternative, the one with greater
uncertainty in the outcome, even when the cost of an undesired, if unlikely,
outcome was possibly catastrophic.
The laundry list of such decisions is long; some outlined in Congressman
Waxman’s letter to Tony Hayward.
CEO’s of Shell and Exxon testified before congress that their companies
would have used a different, more costly designs and followed more rigorous
procedures. According the congressional and journalistic reports, this behavior
is BP standard operating procedure. So BP assumed risk by drilling wells but did not invest in reducing
the risk.
For quite a while they got away with the approach of
assuming but really reducing risk, and appeared to be creating value as
reflected in stock and value and dividends to the investors. The BP management
raised the stock price from around $40/share in 2003 to a peak of around of
$74/share prior to the Deepwater Horizon incident. At this writing the stock is
trading at $32/share and the current dividend has been cancelled. Investors
might rightly wonder if there is another latent disaster and so discount the
apparent future profitability with the likelihood of unknown liabilities. The
total loss of stockholder value is over $100B, which is in the ballpark of the
eventual liability of BP. So, whatever approach BP used to manage risk failed.
BTW, some may recognize this same pattern in the management
of financial firms that participated in the subprime mortgage market. In that
case, they ‘mitigated risk’ by relying on the ratings agencies. Those who
actually built monetized models of the risk realized there was a great
opportunity to bet against the subprime mortgage lenders and made huge fortunes
(See, e.g. The Big Short: Inside the
Doomsday Machine by Michael Lewis .).
Readers of the blog will notice a recurrent theme is some of
the postings. It is essential that we assume and manage risk. To repeat a
favorite quote, “One cannot manage what one does not measure.” The risk map,
score methods, while common are insufficient to the needs of our industry; they
do not measure, nor really manage risk. We as a discipline need to step up to
quantifying, monetizing, and working off risk in order to be succeed as drivers
of innovation. We need to step up to the mathematical approach found in the
Douglas and Dan Savage’s (see
this posting) texts.
I came to this same realization probably a decade ago. I
held off at first because I had not deep enough understanding of how to
proceed, and I knew I would encounter great skepticism. I tested the waters in
2005 and posted my first
paper on the subject in 2006. I indeed received a great deal of skepticism
and resistance, but enough acceptance to go forward. I have learned some
important lessons from all that. In my next blog, I will share my experiences
of bringing more mathematical thinking to risk management for SSD.

Yesterday, I was at the Conference on System Engineering Research (CSER) held this year at Stevens Institute. I sat through a talk which stimulated my curmudgeon tendencies. In the spirit of hopefully generating some contraversy, I will not hold back. The talk was about an expertsystem based engineering risk management system. Essentially, the authors got a set of experts together to identify catagories of risks (people, delivery, product ...), risks in the categories, and a method for identifying level of risk and their consequence and then summing the products of the levels and the consequences. The end is the total amount of category risk. Looking at the output is supposed to give you insight of the overall program risk and the contributing risks. My problem is that I cannot parse the last sentence. In fact I do not understand terms like "program risk" and say "people risk". There may be a clash of cultures here; to many those terms seem reasonable. My argument starts here: One can ask 'What is my risk of going over budget?' or 'what is my risk of missing the delivery date?' The answers to these sort of questions are answered using stardard business analytics. See, for example, Mun's text on risk analytsis that defines risk as statistical uncertainty of a quantity that matters. For example, 'time to complete' is a quantity that does matter to a project. The uncertainty in making the date can be measuresd as the variance (or standard deviation) of the estimate of the timetocomplete. (Note, for the math aware, timetocomplete is what the statisticians call a continuous random variable.) So the answer to the question, 'what is my schedule risk?' has an unambiguous, quantified answer. What is 'my people risk' has no such answer. In fact, 'people risk' is not a concept defined in business analytics. Of course, it does make sense to ask what contributes to the schedule risk. One might fear that the inability to staff the project contributes to the schedule risk. Fair enough. In my mind, that does not make staffing a 'risk', but say a schedule risk factor. I am not sure why I am so adamant about this, but I am. It could be that I believe that the less precise use and measurement of risk is holding our industry back. Anyone want to comment or defend the socalled risk management practice underlying the talk I found so annoying ?

In an earlier blog entry, I mentioned my article Calculation and Improving the ROI of Software and System Programs. I am pleased to announce to that it has been published in the September 2011 issue of the Communications of the ACM.

Since the last entry introducing the concept of liability, I have had the opportunity to discuss it on several occasions colleagues in IBM, In the course of this discussions I formulated what seems to be a useful way to explain the idea. In particular, I presented this idea at the Managing Technical Debt Workshop held on October 9. The following is a preview of what I will present as a lightning talk at the Cutter Consortium Summit next week.
Imagine an insurance agent comes into your office with the following offer: "Our company will indemnify your code against the following risks:

Security breaches

Data losses

Excess support costs (above some deductible)

Down time

...
The policy will only cost $X a year. You realize that code insurance is much like auto liability insurance. In the auto case, the insurance protects you financially against the possible unfortunate outcome of driving the car, in the code case the insurance protects you against against some unfortunate outcome of running the code. So code liability insurance is like automobile liability insurance. This leads to the definition:
'Technical Liability' is the financial risk exposure over the life of the code.
(Thanks to my colleague, Walker Royce, for this crisp definition.)
Note auto insurance and code insurance have some significant differences.

The context for driving  city streets, highways, parking lots, ...  is more limited than the range of contexts that code can operate. Software is truly everywhere from which being embedded in an avionics system to Angry Birds on a smartphone.

The risk for auto insurance is spread among small numbers of large relatively homogenous populations: young drivers, safe drivers, highrisk drivers, etc. So rates can be computed from population experience. We have no such insurance markets for software.
Generally, firms faced with assuming a liability have a choice: Either they buy a policy indemnifying them against the risk or they selfinsure. When they self insure, it is often reported in the annual reports.
If you ship software, you are assuming a liability. As far as I know, code insurance is either rare or nonexistent. If it did, the cost of the policy would be charged against the financial value code. So we are left with selfinsuring,
Here is the main point. In order to truly assess the economic value of the code, one should, as best one can, estimate the technical liability and a fair price, X, for the indemnification. Even a rough estimate of X is better than ignoring the liabilities assumed by shipping code.
So how to estimate X? My first observation should be of no surprise to readers of this blog. Since technical liability involves the future, there are a range of outcomes of future exposure, each of which has some probability. Technical liability has a probability distribution and so is a random variable. X is a statistic (perhaps the mean) of the distribution.
As suggested above, code liabilities comes in flavors: There are exposures resulting from security, reliability, integrity, and so on. Each of these flavors is characterized by its own random variable. The overall liability is the sum of the liabilities that apply to the particular code. As I mention in a previous entry, this sum of random variables is also a random variable found using Monte Carlo simulation.
Now, reasoning about code liability is not unprecedented. Car manufacturers estimate warrantee exposure, telephone switch manufacturers reason about the economic value of going from .99999 reliable to .999999 reliable. There are Bayesian models of the likelihood of a security breach. To estimate technical liability, we need to agree upon the taxonomy of flavors of liability, not a daunting task, and then assemble good enough models of each into an overall framework.
Modified by mcantor@us.ibm.com 110000CH6X

Last week I briefed an IBM customer on some of our recent thoughts on the role of estimation in business analytics. I feel the briefing was not entirely successful. The customer asked about a use of estimation I had not considered previously My first reaction is that the approach desired by the customer was 'not possible'. I then realized it might work in some cases, but I was emotionally opposed to the idea. Then I realized I should not let my emotions interfere and think through the question and its implications. Hence this blog: In Agile projects or in maintenance organizations, workers are assigned 'work items'. Often workers are asked to estimate the time it will take to complete the work item. Asking an employee to commit to a timetocomplete is both reasonable and unreasonable. Team leads and managers need to have some idea when the current work will be done to plan resource assignments, manage content, make commitments and the like. The management also wants to identify the more reliable, productive
workers. After all, development teams are meritocracies. It is right
that the more productive employees are identified and rewarded. So we
need a way for employees to make reasonable estimates while providing a
way for (cliche aler!) the cream to rise. It is unreasonable in that the worker is asked to guess and, in fact, commit to a time to complete. In some cases, the worker may be confident in the estimate. In some cases, there will be less confidence for a variety of good reasons: The task may have dependencies, the solution to fixing a bug report may not be apparent and so on. So asking to commit to a fixed time is unreasonable and measuring the worker against these commitments is oppressive. Under these circumstances, the intelligent worker will pad the estimate so to insure that the commitment is meant. This unintended consequence of asking for the duration is longer than needed estimates and, since people work to the commitments, lower productivity.
In the Agile Planning feature shipped in Rational Team Concert (RTC), we provided means to somewhat mitigate this phenomenon. RTC provides the mechanism for letting the worker enter the best case, likely, and worse case for the time to complete the task. This way the worker can enter numbers that reflect her or his uncertainty. This supports more reasonable commitments and less adversarial conversations. In the tool, the numbers are rolled up using a Monte Carlo algorithm that accounts for task dependencies and shows the likelihood of completing the iteration or scrum. A benefit of this approach is that the worker can be held accountable not to a single value, but to staying within the range of estimate and so need there is no need for padding. There remains the problem of knowing if the estimate is reasonable and how to find the meritorious, which finally brings us to the client request.
The client asked if we could turn this around. Could we use some sort of algorithm to compute the expected time to complete for the task? In other words, the system tells the worker the amount of time it should take to complete the task and the worker then is measured against this expectation. As I said at the beginning of the blog, my first reaction is 'probably not' and this is undesirable. Lets dive deeper. First, like the RTC agile planner, this computation can and should include some best, likely, and worse case in order not to be overly oppressive and roll up to show iteration and/or project schedule risk. Further, building out this approach raises the following statistical question: "Can we sort work items into equivance classes of similar enough tasks, so that we use these classes as populations to build timetocomplete statistics?" If we could do this, then we could properly set expectations on the worker, detect the superior and inferior workers, reward the former and better train the latter. Further, we could measure improvements over time in the execution of the tasks due to team or proecess improvements. All good things. However, this approach needs to be implemented very carefully and not over applied or it could lead to more oppresion and untended consquence.
I suspect the more creative architecture and design tasks simply do lend themselves to this sort of analysis. So teams that create new platforms and build new applications will rely more on expert opinion for the estimates and not predictions solely based on historical data. Not everyone would agree with this. For example, there are some estimation tools provided by various vendors that in fact do try to estimate design and architecture tasks effort and duration by using parametric models or classifications. However, there is so much variation in the amount of novelty of the efforts and the team skill and experience, the uncertainties in the estimates are large enough that they that they should be applied to projects with great care and to individuals not at all.
On the other hand, most of what development organizations do is more routine and for those tasks something along the lines of what the customer asked for might be possible. One would need a way of characterizing the different task classes, track the timestocomplete and do the statistical measures. With this in place, one could explore not only automated task estimates, but also process optimzation by what I believe is a novel application of statistical process control.
In summary I believe we need to pursue task analytics and estimation, but I have serious misgivings. Automated analyticsbased business processes can go seriously wrong. We need to ensure that some judgment and subjectivity is part of the process. The misuse of analytics in the subprime mortgage business is a case in point
I realize something along the lines I am describing may already be available. Has anyone heard of a tool that supports this method?

Over the last couple of years I have been more or less following the technical debt community's discussion on what exactly is technical debt. Some ague that technical debt is limited to what it would cost to address deficiencies such as those found by code inspection tools such as Sonar. Other writers such as Chris Stirling introduce aspects or kinds of technical debt: quality debt, design debt; ....
My interpretation of the Ward Cunningham metaphor on incurring debt by shipping is broader, including the wide range of afterdelivery costs. This entry is continue that discussion and suggest one path forward.
I argued that technical debt should reflect the fact that the very act of shipping software incurs all sorts of possible liabilities, any one of which may incur some future cost.

Future service costs

Executives getting on planes to deal with critical situations

Fines resulting from privacy violations

Loss of business from failing a compliance audit

Loss of intellectual capital due to security flaws

etc, etc.
The nature of the liabilities very from domain to domain. Shipping the next rev of a mobile game like angry birds entails much less liability that next rev of avionic software for a commercial jet.
The costs of fixing the code may be the least of it and underestimates the assumed labilites. Reasoning about whether these liabilities outweigh the benefits of shipping the code is key to the ship decision.
Since I wrote that entry I have been watching the technical debt space and see that I may be the minority, but not alone, with this perspective, Some people argue that technical debt is solely the cost of addressing shortfalls in the code. Others adopt a broader definition. In fact, in a conversation I had with Capers Jones, a longtime expert in software measurement, he shared a conversation he had with Ward discussing the same points. I have seen others make a distinction between software debt and technical debt. I have decided not to weigh in on this argument, but suggest we call all of the liabilities, (wait for it ...) technical liability.
There is a key difference between standardlydefined technical debt and technical liability: Technical debt involves code quality and can be determined. The liabilities involve possible future events and so entail predictions of the future. Some might even consider technical debt knowable and technical liability unknowable.
Readers of this blog know where I am going. Technical liability, unlike the more limited technical debt, involves a range of future possibilities and so each of the components of liability should be specified as a random variable with a probability distribution. The security violation might or might not occur. But if it does, the possible expense could sink the company. Reasoning about the risk takes some advanced techniques like setting the price of an insurance policy.
Finally, the economic decision if it makes sense to ship a piece of software, one needs to balance the value expected from the ship against the assumed liabilities. Note that the future value is also a random variable. In that case the decision to ship should be based on the techniques found here. I will elaborate the reasoning ibehind technical liability n a future blog (promise).
In summary then, technical liability gives a more complete picture of the economics of shipping a piece of code than technical debt, but it requires more sophisticated analysis.
Modified by mcantor@us.ibm.com 110000CH6X

Folks who have heard me present will recognize the following
discussion as a variation of what I have used as an example to explain the
importance of variance in software and system estimates. Imagine this time you
are a development organization manager given the following artificial
opportunity. You can agree to the following deal: Have the teams at your own
expense develop some application, each meeting a given set of requirements. The
client really wants the applications and will accept them if acceptable and
perfectly will to be consulted throughout the projects. Here is the catch: if
you deliver the projects on time in 12 months, you will receive $1M per
application. If you are a day late, you get nothing. You have to decide whether to take the deal.
Lets suppose you take the projects to your estimators and
they tell you the estimated time to complete is 11 months and the estimated
cost to complete is $750K for each of the projects. So you stand to make an
estimated $250K per project. So you staff up as much as you take on three
projects looking forward to your bonus. Was this a good deal?
Those who have read The
Flaw of Averages by Sam Savage and Dan Denziger already know the answer.
Those who haven’t read the book should. This book nicely captures the sort
of statistical reasoning that underlies IBM Rational’s approach to business
analytics and optimizations (found in the RTC agile planner and the ROI
calculations in Focal Point). Some key rules:
 Uncertain quantities are captured by curves
called distributions (e.g. the bell shaped curve of normal distributions)

Most distributions for uncertain quantities are
not normal, bell shaped curves, i.e. normal distributions are abnormal.
 Calculating with averages in any case yields the
wrong answer with business critical effects. Rather one should calculate with
the distributions. This is done with Monte Carlo methods.
Back to the example: The time to complete is an uncertain
quantity and so must be described by a distribution. Often, the estimate
returned by the estimator is the mean of that distribution. The distribution
may be pretty wide and so may look like Figure 1 of the attached document. (I
have had bad luck trying to embed figures in the blog and I have put the figures in this this attachment.) Note that 40% of the distribution lies beyond 12 months.
Assuming the $750K cost to complete estimate is dead on,
lets apply some simple high school probability to get the distribution of
profit (See Figure 2):
·
The chance of succeeding at all three projects and
getting $3M is revenue.is (0.6)^{3}=0.216,
·
The chance of succeeding at exactly two projects
and getting $2M in revenue is 3(0.6)^{2}(0.4)=0.432
·
The chance of succeeding at exactly one project and
getting $1M in revenue is 3(0.6)(0.4)^{2}=0.288
·
^{ }The chance you will fail at all three projects yielding
no revenue is (0.4)^{3}=0.064.
The weighted average of the distribution of revenues is
(0.216)($3M) + (0.432)($2M) + (0.288)($1M) + (0.064)($0) =
$1.8M
So the likely outcome of your (3)$750K = $2.25M expense is a
loss
of $450K.
But wait, it is worse. The distribution is probably not
normal. Programs are more likely to late than early and so are skewed to the
right. In this case the average (i.e. the mean) is less than the 50% point. So,
as shown in Figure 3, it is possible to have the estimate of 11 months and the
likelihood of failure is 50%. The revenue distribution is given in Figure 4. In
this case, the weighted average of the distribution of revenues is
(0.125)($3M) + (0.375)($2M) + (0.375)($1M) + (0.125)($0) =
$1.5M
In this the expected loss
is $750K.
But wait, it is still worse. The cost to complete is also
uncertain. To keep things as simple as possible, lets suppose the cost to
complete for each of the projects is described by three values: best case is
$700K, the likely case is $750K, and the worse case is $1M To compute the
expect profit in this case requires using this values as parameters for a
triangular distribution (see Figure 5) and then apply Monte Carlo methods to do
the calculation to get the distribution that describes the profit. The result
is shown in Figure 6. Briefly in this case:
·
The most likely outcome is a loss of $945K
·
There is a 90% certainty of losing at least
$805K
·
There is a 10% chance of losing more than $1.1M
So taking this deal is at best career limiting!
Notice by ignoring the rules, one is tempted to make a bad
deal. Applying each of the rules with more discipline shows how bad the deal
is. The moral of all this is that making business decisions based on
calculations of averages can lead to disastrous outcomes.
This moral needs to be taken to heart by our industry. Far
too often, managers when faced with making funding projects or business
commitments insist, “Just give me the number.” What they need is a distribution;
the number they are given is likely to be an average. Decisions based on the
number will likely go sour. No wonder the software and system business outcomes
rarely delight their stakeholders. The good news is that there are robust,
proven techniques to avoid the flaw of averages.

I have mentioned in the first posting, I am still getting the hang of blogging. I guess one use of blogs is to share what on my mind while staying in the neighborhood of the topic of analytics. So, I have been putting a lot of thought to Toyota's diemma about how to deal with the reports of dangerous acceleration in their cars. The recent reports of Prius incidents (see this article in the New York Times) confirmed some of my earlier suspicions and hence this blog. First, I need to come clean; all I know is from news accounts. I have had no contact with Toyota or any IBMers working with Toyota. Further, I need to say the the opinions here, in my opinion not controversial, are my own and do not reflect any IBM position. So what do we know:  The two models with reported acceleration problems, Camry and Prius, have bus interfaces from the petal to the electronic control unit (ECU) that manages the acceleration. (see Electionic Design News Article)
 There are confirmed cases where the floor mat is not not the cause (see the NYT article in the previous link)
 Toyota has has millions of Camrys on the road and the Prius has been the largest selling hybrid.
 Toyota has stated they cannot reproduce the problem.
So, here is how it seems to me. The models with the problem are those that have embedded software. Further, the incidence of the reports is consistent with the failure rate of software that meets usual software quality standards. It is well known that the time and cost of testing goes up dramatically as the defect density gets low. Further the defect may involve interactions between the ECUs. From an analytic perspective, the combined state space of the combined ECU's is very large and as the defects get removed, the bug states are sparse and so the likelihood of a set of interactions getting the integrated ECUs into a bug state is very low and unlikely to be found in conventional testing. So there may well be some latent defect in the embedded code that they
have not found after whatever amount of testing they find affordable,
particularly given the pressures of getting into the market. In conventional software, when the cost of finding the next defect is too high, the code is released in beta so that a crowd of volunteers can further exercise the code, putting in the hours to find the next set of defects. One cannot beta test an automobile. Now, say there is one chance in a million miles of driving of the latent defects manifesting. They may be impossible to find with standard testing and will inevitably happen every so often to drivers. This is the standard insight that with large volumes unlikely events become inevitable. So with Toyota's large sales, they may be the victim of their success. The avionics community has developed a discipline around safetycritical software. There are design and model testing methods to validate that the embedded software is good enough to stake people's lives on the code running correctly. (There is a good article is the latest Communications of the ACM on model checking for avionis) It seems Toyota and the entire auto industry needs to adopt these safetycritical disciplines going forward. The cost of these practices is overshadowed by the costs of costs of the highly publicized incidents, the suits, and other liability.

One of the things that characterizes software or systems development is that the project manager routinely commits to deliver certain functionality on a given date at an agreedupon level of quality for a given budget. It is the role of the project manager to make good on the commitment, The software and systems organization leadership may count on the commitments being met in order to meet their business commitments or there may be an explicit contract to deliver on time for a fixed budget. The measure of a good project manager is the ability to make and meet commitments.
iIn this blog entry, I will discuss the nature of that commitment and how it relates to project analytics. First of all, lets define 'commitment' in this context. Of course, I do not mean the confinement to a mental institution, I mean, as suggested above, the promise to deliver certain content with acceptable on or before a certain date.
The first thing to notice is that the future is never certain, and so we are in the realm of probability and random variables, i.e. a quantity described by probability distribution. Going forward, I will assume the reader is familiar with the concepts of random variables and their associated distributions . Soon, I will devote a blog entry just that topic.
Meanwhile, the best way to describe the likelihood of meeting a commitment is the use of a random variable. Consider the distribution of the time it will take to meet the commitment. It might look something like this:
A similar distribution would apply to cost to complete.
Recall, the probability then of the commitment being met is the area under the curve that falls before the target date:
The manager, in making the commitment, is essentially betting (perhaps his or her career) that he or she will meet the commitment. According to this measurement, the odds are about 5050. The key measurement then is the amount area of the random variable that lies prior to the target date, which in turn relies on the the ability to calculate the probability distribution. I also will discuss some techniques to do that in a later entry.
Now consider for example, "project health". What I believe what is meant is the likelihood of meet the commitment to deliver the project on time.
If it highly probable the project will ship at the target date, the project is 'green' otherwise it is 'yellow' or 'red' like in the following figure.
There are three reposes to a yellow or red project. One can move the target date, move the distribution, or change the shape of the distriburion, again a topic for a later bog.
Modified by mcantor@us.ibm.com 110000CH6X

First, some personal disclosure: In the late 1980’s, I
worked for a while at Shell Research, developing seismic modeling and data
imaging algorithms. ( See
this link.) While there I received training on oil exploration. I
remain awed by the passion, expertise, daring, and discipline of the engineers,
scientists, technicians, and skilled laborers who take responsibility for
providing the hydrocarbons we completely rely on.
Oil exploration is remarkably costly and risky. Even then in
the late 1980’s, it was not uncommon to spend $1B on an exploration well,
hoping to find oil based on the seismic data only to find it dry. At Shell, I was on a team that developed, for the time, a
highly computeintensive algorithm for imaging seismic data captured in complex
subsurfaces. They literally bought
us a Cray since running the algorithm might make a marginal difference in the
success rate of exploration drilling.
Hence, I am not an oil industry basher, far from it. So I
have been watching the BP, Deepwater Horizon gulf catastrophe with great
interest. In this entry, I will share what I have gleaned from various news
sources. (I have found the Wall Street Coverage very credible). So here is my
net:
Recall, the blowout occurred shortly after capping an
exploration well (a will drilled solely to confirm the presence of a oil
resevior). The depth of the well, reported 18,000 ft, is no big deal. The depth
of the water, 5000 ft, is far from the record of around 8000 ft. So the well itself was routine for
the industry. So what happened?
BP had drilled many of these wells. In fact, ironically the
blowout occurred while BP executives were celebrating their safety record. However,
over the last few years have become profitable by building a very
costconscious culture. Such a culture is likely to cut corners, repeatedly
taking small risks in business operations. Such behavior may be rational if you
believe the total liability is bounded. There is reason to believe BP’s
liability is ‘capped’ at $75M. This culture seemed to be at work on the oil
platform:
I bet that BP managers routinely
made the same decisions for years with no adverse outcomes. They were probably
rewarded for this behavior. Such a culture makes such disasters inevitable over
time. A great case study of such
cultures is found in Diane
Vaugh’s The Challenger Launch Decision:
Risky Technology, Culture, and Deviance at NASA. She studies the NASA
culture that led to the decision to launch the shuttle that exploded on launch killing
‘the first teacher in space’ while tens of thousands of school children watched
on television. The managers overruled the engineers who advised them that the
temperature was out of spec for a launch. As she explains, the managers had
gotten away with taking similar risks in the past and had decided to bow to
political pressures and approve the launch.
So what they were thinking is something like, “These risks
are no big deal and the savings matter.”
A key moral is that over time the unlikely becomes
inevitable. Further, experience and past data lead to exactly the wrong
behavior: they reward the risk taking, not the caution. Many have made exactly
the same point about behavior of the financial firms during the financial
meltdown.
Internalizing this moral and acting accordingly is key to
our industry. Increasing, we will be building life critical, economic critical
systems that are very complex, will operate over long periods, and whose
failure could be catastrophic. There is no turning away from this
inevitability. So, we all need to understand that cost savings must be balanced
by a clear understanding of the overall risks of failure, their consequences
and the real return of investment in failure avoidance. This of course takes some math. In
particular, thinking about averages is not useful. That is topic of my next
entry.
