More Mistakes To Do...

In my article ‘Plan to do New Mistakes’ published here, in this blog in Feb. 2008, I had listed ‘Classic Mistakes’ from Steve McConnell’s book ‘Rapid Development: Taming Wild Software Schedules’ and mentioned that a Project will definitely be a success, if the Project Manager ensures that the team doesn’t do the ‘Classic Mistakes’. Towards the end of the article, I had also mentioned that by doing new mistakes, a Project Manager can also contribute to the history of software engineering, in jest.

Well, Steve McConnell and his team have actually revised the classic Mistakes and updated the list. :)

The following Classic Mistakes were added.

• Confusing Estimates and Targets
• Excessive Multi Tasking
• Assuming Global development has a negligible impact on the total effort.
• Unclear Project Vision
• Trusting the map more than the territory
• Outsourcing to reduce the cost
• Letting a team go dark

The addition had produced a total list of 42 classic Mistakes.

I have listed the Top 10 Classic Mistakes here from the updated list. These are also called the ‘Most Damaging Classic Mistakes’.

1. Unrealistic Expectations
2. Overly Optimistic schedules
3. Short Changed Quality Assurance
4. Wishful Thinking
5. Confusing Estimates with Targets
6. Excessive Multitasking
7. Feature Creep
8. Noisy Crowded Offices
9. Abandoning Planning Under Pressure
10. Insufficient Risk Management.

It may be funny, but nothing related to ‘Technology’ figures in the top 10 list. I believe it sends the message to people that Good Project Management Practices along with good technical practices alone will lead a Project to success. The relationship between ‘Technology and Project Management’is an AND condition not an OR condition.

If you want to know all the 42 classic mistakes, their definitions, how these mistakes rank and the methodology used , please refer to the paper here. ( note registration is needed).

A Game for Project Managers

How can someone learn the sophisticated skills of planning, budgeting, executing, and keeping on deadline a complex project? ‘On the job’ means that more often than not, they end up learning, by burning more than their hands. Many a times, the PMs don’t even get a second chance to implement what they learned. So 'On the job' is effectively ruled out.

Folks at Singapore-MIT Gambit Game lab think that project management skills can be taught through a cooperative puzzle game. In Tipping point, players assume the roles of Project Managers, and work together to complete projects before they go too far past their deadline. The game is won by completing a set number of projects without letting any project fail.

While being fun, the site claims that it also ensures that the players are taught the following.

• Projects are completed through a mix of Concept and Production work. Note: "Concept Work" represents the analysis and planning done in the early phases of a project, while "Production Work" represents implementing the project, such as building a product.
• The need for a balance between Concept work and Production work.
• Production work is more useful in the short term. Concept work is more useful in the long term.
• Understand the benefits of long-term planning.
• The importance of Collaboration.
• How to handle multiple projects.
• One hasty decision can quickly change several small projects into a big mess that is almost impossible to manage.

Try the game and let me know what you think. It is free and can be downloaded from the page above.

Notes to Self - Three different concepts of probability…

In the series, ‘Notes to Self’, I will be noting down concepts I read some where and relate it to my experience, just for my reference. These posts do get enriched as I learn more on the topic. The first topic I have chosen is probability.

Whenever we talk about probability, tossing a coin or picking a card from a deck of cards come to our mind. Both of these belong to what is known as Classical probability. Classical interpretation of probability is a theoretical probability based on the physics of the experiment, but does not require the experiment to be performed. For example, we know that the probability of a head or a tail on a balanced coin is 0.5 without performing the experiment. Here, probability of an event is defined as the ratio of the number of outcomes favorable to the event divided by the total number of possible outcomes.

Sometimes, a situation may be too complex to understand the physical nature of it well enough to calculate probabilities. How ever by running a large number of trials and observing the outcome, we can estimate the probability. This is called Empirical probability. Larger the number of trials, the more accurate is the estimate of probability. Calculating the load bearing strength of a bar by subjecting the bar to stress test or predicting the time to complete a task by a person, based on historic data are examples of subjective probability.

(Note: past outcomes need not always predict the future outcomes. Take the example of a turkey being fed (prepared) for thanks giving. If one goes by the time when the turkey is fed, beheading of the turkey is never a possibility. The scenario in this example is more suited for Subjective Probability. Hence we have to be very careful when using Empirical probability and more about this in another post).

A manager faces situation in which neither of the above are useful. For example, in the case of a product launch, the probability of success can neither be calculated nor estimated from repeated trials. In such a scenario, people are forced to take educated guesses regarding the outcome. More educated the guess is, better is the probability of the event. This is known as Subjective probability. In the absence of better information to rely on, subjective probability may be used to make logically consistent decisions. But the accuracy depends on the knowledge of the factors involved and the accuracy of their impact on the final outcome.

The difference between Subjective and Empirical is that Subjective can't be modeled like Empirical, due to the large number of factors involved. Even if you try to simulate it using computers, it still may not be possible to cover all the paths involved.

In conclusion: In real life, we rarely come across theoretical probability. Real life is too complicated to allow us to use theoretical probability. Situations that we face either call for Empirical probability or Subjective probability. Understanding the difference between these two helps us to understand the limits of our knowledge and hence our own fallibility. It also helps managers to take the right decisions.

Concept as read and understood from http://www.quickmba.com