Posts Tagged ‘economic governance’

More Honest predictions

Monday, March 18th, 2013

While quoting the most likely outcome, i.e., the mean, or median or mode of a probability distribution, may be a rough prediction, a more honest representation of the prediction would quantify the full range of possible outcomes. For example, the most likely outcome of 150 days is an accurate portrayal of the expected target date in the two upper graphics of the figure in my last post. However, by expressing how sure we are of that guess—a coin flip in one case and a confident commitment in the other—we are much more honest and transparent in communicating that information to others.

(more…)

Bayesian reasoning

Tuesday, March 12th, 2013

Setting any project’s expected delivery date or its planned resources is a kind of prediction. Many project managers have been faced with the dilemma that it is impossible to predict the future, but that is our job. The best way to improve predictions is to apply what is called Bayesian reasoning.

(more…)

The Crux of Economic Governance

Wednesday, March 6th, 2013

Suppose you are the project manager for a software product that your organization needs to be delivered in 12 months to satisfy a critical business need. You gather your leadership team, perhaps an architect a development manager and a test manager, and analyze the project scope and constraints to estimate the resources and time needed. You employ empirical models that estimate the project should take 11 months. Excellent! What do you do with that information?

(more…)

Transforming to Economic Governance

Monday, February 25th, 2013

Highly complex systems like societies, economies, and evolution are also non-deterministic because they represent unpredictable life forms interacting with each other in chaotic ways. Long term weather forecasting, hurricane prediction and earthquake prediction are similarly complex and non-deterministic. These large-scale systems have emergent behaviors where we can create probabilistic models that predict the range of outcomes and likelihood of outcomes, but never the exact outcome. Why? There is uncertainty inherent in the interacting elements (i.e., humans with free will, random or unpredictable acts of nature, mutations, innovations, etc.). This does not mean that these predictive models are not useful. It simply means that we must reason about things differently and understand both the distribution of outcomes and the uncertainty inherent in that distribution to make better decisions.

(more…)

In Software delivery, economics trumps engineering

Tuesday, February 19th, 2013

Successful software outcomes are highly dependent on continuous negotiations, accurate predictions, value judgments, innovations, team collaboration, architects, agility, market conditions and user demand. Success is much less dependent on quality of contracts, Gantt charts, critical path schedules, earned value measurement, laws of physics, material properties, mature building codes and certified engineers. Software delivery is more a discipline of economics than it is of engineering because it is a complex endeavor that is inherently non-deterministic…there is much more uncertainty.

(more…)

Economic Governance (intro)

Monday, February 11th, 2013

The principles of agile software development are more than 10 years old. Many practical ideas have been, piloted, practiced, and evolved. Agile practices are now neither novel nor extreme. What differentiates the enterprises that significantly improve software productivity from those that flounder? One significant discriminator in achieving scalable agility is transforming to an appropriate governance model that complements the dynamics of agile principles and practices. We call it economic governance: a quantified foundation for planning, decision-making, measuring, and assessment that resolves uncertainty earlier and unifies constituencies on managing a shared set of expected target outcomes.

(more…)

Economic Governance of Software Delivery

Monday, February 11th, 2013

My Blog posts for the next couple months will be extracted from a draft white paper I am writing with Murray Cantor on development analytics. Here is the abstract.

(more…)

Integration first leads to more agile outcomes

Monday, February 4th, 2013

To transform successfully from conventional engineering governance to more agile economic governance requires a significant cultural transformation. This is best achieved through the pursuit of one simple change theme: Integration testing should precede unit testing. In practice, this theme is overly simplistic and a bit stark: Integration and unit testing actually proceed in parallel. However, to accelerate the transformation to increased agility, it is best to simplify and clarify that the highest priority is to achieve intermediate milestones of executable test cases of integrated functionality.

(more…)

Transforming to Economic Governance

Monday, January 28th, 2013

Highly complex systems like societies, economies, and evolution are non-deterministic because they represent unpredictable life forms interacting with each other in chaotic ways. Weather forecasting and earthquake prediction are similarly complex and non-deterministic. These large-scale systems have emergent behaviors where we can create probabilistic models that predict the range of outcomes and likelihood of outcomes, but never the exact outcome. Why? Because there is uncertainty inherent in the interacting elements (i.e., humans with free will, random or unpredictable acts of nature, mutations, innovations, etc.). This does not mean that these predictive models are not useful. It simply means that we must reason about things differently and understand both the distribution of outcomes and the uncertainty inherent in that distribution to make better decisions.

(more…)

Software Delivery: Economics trumps engineering

Thursday, January 24th, 2013

In making the transformation to economic governance, one needs to make a counter-intuitive leap from deterministic decisions to non-deterministic reasoning. This is not easy for most of us. Especially project managers who are experienced and trained in traditional project management disciplines of detailed planning, critical path analysis, earned value management and the like, have a very rough transition. They must move from a world of managing certainty, details and precision to a world of resolving uncertainty based on probabilistic judgments and investments.

(more…)