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We all make decisions all the time, sometimes instinctively, sometimes after rational deliberation. Some decisions aren't made by individuals, but by groups of people instead. This paper addresses two problems that are common when a team makes a decision: first, group-think can lead to important aspects being ignored, and second, reaching an agreement within a group can become a lengthy and tedious process. Both patterns can be applied in wide range of situations, but will be especially useful for IT projects.

Decision-making is part of our everyday lives. Successful decision-making is fundamental for our ability to adapt to changing environments, which in turn is a key factor for longevity. In a world that's moving fast we are expected to be flexible and make decisions quickly. On the other hand, there is also a need for stability. Changes in direction, if they occur too frequently, cause ambiguous and inconsistent behaviour. Decisions that are constantly revised lead into chaos. This paper presents four patterns that address the tension between flexibility and steadiness. The first two patterns explain two principles that each foster flexibility and swiftness, while the two remaining patterns go on to support stability and steadiness. Together, they aim at a continuous flow of decision-making at the right pace. The patterns are explained both with analogies from natural sciences and with several concrete examples.

Making decisions is part of our everyday life. Unfortunately the quality of our decisions is often impaired as human intuition is subject to numerous biases. This paper presents a few lessons learned from cognitive psychology and presents them as patterns. The principles described can be applied to arbitrary situations where a decision needs to be made. Naturally this includes the decision-making processes found in IT projects, as is shown with three concrete examples from an IT context.

As opposed to intuitive thinking, quantitative analysis gives the impression of being objective and reliable. While some of this is true, it's also wise to apply quantitative methods with care. Statistics can be difficult to apply and are in fact easily misused. This paper presents some fundamental rules well-known from scientific statistics. These rules are nowhere near sufficient for a fully-fledged statistical analysis, however, they do represent a set of very helpful instruments in many practical cases. Shaped as patterns, these rules can be applied whenever numbers enter our everyday reasoning. Among other domains, they can be applied in IT projects, as is shown with three concrete examples.

When an existing application is replaced by a new one, its data has to be transferred from the old world to the new. This process, known as data migration, faces several important requirements. Data migration must be accurate, otherwise valuable data would be lost. It must be able to handle legacy data of poor quality. It must be efficient and reliable, so as not to jeopardise the launch of the new application. This paper presents a collection of patterns for handling a data migration effort. The patterns focus on the design of the migration code as well as on process issues.