No facts about the future

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One of my favorite quotations, going back 15 years or so, is all about what we know and what we don't know (Shouldn't that be obvious? Perhaps. But it's not)

There are no facts about the future

Dr David Hulett

Profound in its simplicity, this quotation always seems to beg two questions:

1. If there are no facts about the future, what then do we know about the future?; and
2. Where are the facts aboutt, if not the future?

The answers should be self-evident, but just in case, here they are for the record:

1. There are only estimates (not facts) about the future, and the estimate is only as good as our conception (or model) for the future. Using facts (history) to project a trend line or construct a regression curve doesn't change this in any way. There are still no facts about the future
2. The facts are in the past, but they are subject to interpretation; so facts may not be so factual. Well, actually, a fact is a fact, but the cause-effect may be in doubt, so also correlations. All we know with a fact is that it's a posterior consequence of some prior circumstance.

We see these things at work every day, in public life, private advice, and project plans:

• According to Scientific American, we learn that: "In 1798 Thomas Robert Malthus famously predicted that short-term gains in living standards would inevitably be undermined as human population growth outstripped food production, and thereby drive living standards back toward subsistence. We were, he argued, condemned by the tendency of population to grow geometrically while food production would increase only arithmetically."
• Financial planners solemly declare that you will (or will not) be able to retire and not outlive your savings and pensions
• The EAC (or ETC) is really not an estimate of "estimate-at-completion" if calculated with earned value formulas; somehow, EAC becomes a fact. Nonesense. It's still an estimate, even if calculated with linear equations with known (historical) coefficients. The EV equations assume a model of the environment, the staffing, the quality of the requirements, the attitudes of the sponsor, and so on.  Change some element of this model and the equations are for naught, or at most they are for a case that's changed and may no longer be highly likely.

This whole discussion is probably at its worst in the public policy domain. Each side, no matter the issue, purports to know the facts. They don't. In the US, to fix short-term and near-sighted budgeting, we've gone to 10-year budget/benefit estimates. The effect is to set up policy debates that are valid under a specific set of assumptions, but the assumptions are more often than not lost on the general populance, leaving only the budget/benefit as a 'fact'.

The worst of the worst occurs when dealing with so-called wicked projects, projects for which the solution actually defines the problem (because no one else can because of so many circular conflicts). In the wicked situation, facts--such as they are--should probably be treated almost like 'sunk costs' (costs on which you shouldn't base a decision because they really have no impact on the future). Wicked facts are very weak as a driver for future outcomes.

Climbing back down from my soapbox....

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Kano and Agile

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Kano analysis is a new product feature/function evaluation tool that gives visualization to feature/function relative merit over time as trends change. The usual presentation is a four-sector grid with trend lines that connect the sectors.

The grids are defined by the horizontal and vertical scales that are easily set up on a white boad in the war room (don't take the word 'scale' too seriously; for the most part this is uncalibrated opinion):

• Vertical: customer attitude
• Horizontal: some quality (or metric) of the feature/function that's important to the customer.

The trends need not be linear, and need not be monotonic, changing direction as customer/user attitudes change (Again, an equation can be defined for these lines, but the focus here is not on the exact formula of the line, just the general notion).

Agilists use the Kano board with sticky notes to show how feature/function in the form of stories might play out over time.

 And, we take the trouble to do this because:

• There's only so much investment; it needs to be applied to the best value of the project. Presumably that's the "ah-hah!" feature, but the "more is better" keeps up with competition; and, some stuff just has to be there because it's expected
• Trends may influence sequencing of iterations and deliveries. Too late, and decay has set in and the market's been missed.
• The horizontal axis may be transparent to the customer/user, but may not be transparent to regulators, support systems, and others concerned with the "ilities". Thus, don't forget about it!

Now, wouldn't you like to have been a fly on the wall in the Apple war room a few years ago when they debated doing away with the floppy drive; or, more recently, the spinning disc. I wonder how they drew the trend lines and made their investment decisions?

How far ahead of the trend can you be and not be too far ahead? Just a rhetorical question to close this out.

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5 milliseconds!

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Would you take this one on as a project?

• There is an existing legacy capability
• It takes about 46 milliseconds to execute a transaction with the legacy
• Your project objective is to reduce this by 5 milliseconds.

Doesn't sound too bad, improving things by 10%.

However, in a posting by Azimuth, we learn this project is about straightening the route of Atlantic submarine cable from Halifax, NS to London so that it's 310 miles shorter (5 ms in electronic terms)

It's all about flash trading, to be made flashier still:

In fact, the battle for speed [on Wall Street and in The City] is so intense that trading has run up against the speed of light.

For example, by 2013 there will be a new transatlantic cable at the bottom of the ocean, the first in a decade. Why? Just to cut the communication time between US and UK traders by 5 milliseconds. The new fiber optic line will be straighter than existing ones:

“As a rule of thumb, each 62 miles that the light has to travel takes about 1 millisecond,” Thorvardarson says. “So by straightening the route between Halifax and London, we have actually shortened the cable by 310 miles, or 5 milliseconds.”

That's interesting, but not too threatening. On the other hand, Azimuth goes on to describe stuff that has real risk, especially given the performance of the software project recently put on line by Knight Traders:

But that’s not all. When you get into an arms race of trying to write algorithms whose behavior other algorithms can’t predict, the math involved gets very tricky. In May 2010, Christian Marks claimed that financiers were hiring experts on large ordinals—crudely speaking, big infinities!—to design algorithms that were hard to outwit.

Yikes! I hope they do a bit of structured requirements analysis, the old fashioned kind (sorry, Mr DeMarco!), when we wanted to know if it would really work. And, more better, how about a FMEA (Failure Mode Effects Analysis)? This is kind of thing NASA did when there was more than just money on the line.

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Rule of thumb: the change curve

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I ran into a blog item on change the other day, at a blog site called Rule of Thumb.

The posting entitled "The Change Curve", depicts a project management adaption of the change model proposed by Elisabeth Kubler-Ross in her book "On Death and Dying" when she described the "Five Stages of Grief"

Rule of Thumb proposes this adaptation for project management of the Five Stages into these six ideas:

•Satisfaction: Example – “I'm happy as I am.”
•Denial: Example – “This isn’t relevant to my work.”
•Resistance: Example – “I’m not having this.”
•Exploration: Example – “Could this work for me?”
•Hope: Example – “I can see how I make this work for me.”
•Commitment: Example – “This works for me and my colleagues.”

And, this figure accompanies the posting.  It illustrates the familar "dip" that occurs after change before the positive affects of change go into effect.  However, it's annotated with the model ideas [given above]. 

Of course there are many other models of both change and change resistance. One useful model of change (not change resistance) is by Kurt Lewin; I like it because it's similar to Deming's PDCA (plan, do, check, act). Lewin's model is three steps:

1. Unfreeze previous ideas, attitudes, or legacy
2. Act to make the change
3. Freeze the new way in order to institutionalize the change.

And, A.J. Schuler, a psychologist, has his 10 reasons about why change is resisted. You'll find them here in a paper entitled "Overcoming Resistance to Change: Top Ten Reasons for Change Resistance". His lead-off idea is doing nothing is often perceived as less risky than doing something--in other words, Plan A (do nothing) trumps Plan B (do something). 

But the one I like is that people fear the hidden agenda behind the reformers ideas! Amen to that one.


Even if you don't find a lot new here, sometimes rearranging the deck chairs provides new insight.

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Blind optimism

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The stress laid upon the unquestionable advantages which would accrue from success was so great that the disadvantages that would arise in the not improbable case of failure were insufficiently considered

Field Marshall Sir William Robertson

"Soldiers and Statesmen"

 

 

If you're like me, you have to read that quote a couple of times to be sure you grasp the point, given the somewhat stilted prose of a nineteenth century aristocrat (although this was written around 1920)

But, Sir William gives us the working definition of blind optimism:

• Blind to a reasonable consideration of risks and
• Blind to the advice of others and
• Blind to any proposition other than success

Sir William is also telling us that's what you get when driven by overwhelming sponsor pressure (or customer or market pressure, take your pick)

 

Of course, if you are driven to succeed in such a "best the enterprise" adventure, you are a hero, an acknowledged risk taker, and a shrewd judge of the impossible.

 

And there will be many who will step forward and help you celebrate success!

 

But if you fail, there will be many who will line up to criticise group think, highly discounted risks, and over optimistic success factors, to say nothing of blinded by pressures. They may well be correct.

 

Oh, and you'll be alone with the outcome!

 

Who does not remember this bit of wit from JFK:

• "Victory has a thousand fathers, but failure is an orphan"

 

So, is this a discouragement of risk taking? Hopefully not. Just a discouragement of blind optimism.

 

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The case for contradiction

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Abraham Lincoln said that a house divided against itself cannot stand. He was right about slavery, but the maxim doesn’t apply to much else. In general, the best people are contradictory, and the most enduring institutions are, too

David Brooks

So, if Brooks thinks the best and brightest are often self-contradictory, and institutions that last and endure are also, what do we make of this when we scale it project management?

• Can we support de-centralized and centralized in the same project, say for change management?
• Can we support agile and traditional methods in the same project, say for different technologies?
• Can we support self-managing teams and intervene with the team leader selection?
• Can we promote the principal of subsidiarity and yet insist on weekly reports?
• Can sponsors insist on risk management, and yet deny funding to follow-through with risk response? 

Well, of course, the answer to all of these is 'yes', with conditions. We can be contradictory in tactics yet be strategically coherent in direction.

For example, a project can be strategically coherent about tolerating change, but yet apply different tactics---decentralized and centralized CM---seemingly contradictory tactics, according to circumstances.

So, I see Brooks point, and I agree that it's wasteful, certainly not lean, and probably counterproductive to be hard-over on tactics in order to have a seeming harmony with strategy. Be aware: It's possible to tack away from the mark (tactics) and still get to the mark first (strategic objective)

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Porting success

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 What God hath woven together, even multiple regression analysis cannot tear asunder.

Anonymous

For those who are a little vague on regression analysis, here's a quick refresher example so you will understand the point we're making:

• Let's say you have a bunch of observations of real outcomes, like unit test results.
• And, let's say you've grouped them as they occur: Test set 1, Test set 2, Test 3, and so forth out to TS 'N', for the Nth test set.
• And, let's say that each test set itself has a metric, like some kind of scalar size, so that the size of TS 1 is less than TS 2, and so forth
• And, for each test set, let's say there is a metric you are interested in, like "discovered but unresolved errors".
• That's a bit of an awkward phrase, so let's short hand with "quality factor 1", or QF1 for short.

We could then ask the project data analyst who lives in the PMO to plot QF (errors) versus TS (size) on a graph. And, we could ask the analyst to "fit" a line through the data such that the average distance between an observation of QF error and the line is minimized. The analyst would give us back something that looks like the following:

Now, there are two questions you should be interested in:

1. Is the variability in quality (metric A) is strongly related to the TS size (metric B) or not?
2. And, for the next TS, with a size within the sizes already observed, will it's QF be on or near the line?  

If you've not already guessed, the line is a "regression" line or curve. Here's the "tear asunder" part: does the regression line fit to the observations (of God's work?) really reveal the constituent influences on the outcomes?

In less grandiose terms, regression analysis is simply used to predict the next outcome, given that the next outcome occurs in the same circumstances as the prior observations. (You can't do regression predictions outside of the domain or limits you have in the observations). Given another value for Metric B, regression predicts the value of Metric A.

But, here's the next big thing: Can you take your regression curve with you to your next project? In otehr words, if you understand all the parts that went into the success of the outcomes, can you expect the same results if all the parts port over to the next project?

There's actually no closed-form answer on this; the best you can say is maybe. The most important thinng to understand is that you probably don't know or understand all the constituents that went into the former success. Thus, regression is helpful, but often incomplete in revealing the true secrets of success.

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