I'm starting to gather my thoughts for the October issues of
CM Crossroads which has a theme of "
Overcoming Resistance to Change." Some of my favorite books on the topic of enabling change are:
While I can't possibly cover all of the ground that these books do, I can share some observations I've made while trying to help teams to do things differently, such as adopting Scrum or developing a more agile release management approach.
A common reason people resist change is because they follow "rules" that they don't understand no longer apply. When these "rules" are embedded in the culture of an organization the challend to change is greater, since its often more pardonable to fail when you've followed the established practices, than failing when trying something new. This is a big challenge to overcome, and there is no easy answer to addressing this challenge, other than to be aware that this behavior may be happening. But there are a couple of things that help make enable change:
- Leading by example. Often others just don't understand that other ways are possible.
- Gather (visible) data. Often others ignore uncomfortable facts.
As an example of the first point, I've been on teams where unit testing was dismissed as simply too hard to do, or a waste of time. By a small group adopting the practice in small cases (and refactoring the more difficult code to enable unit testing), they can demonstrate how the presence of the tests helps improve quality and speed of delivery. In this way a small group of enthusiasts can lead the way for the more timid. (
Really Dumb Tests discusses a similar point.)
In other cases, the team isn't aware of a problem. A colleague of mine recently said "you can't change what you can't measure," and gathering data is essential to making a team aware of a need to change. Once you have the data, you then have the ability to make decisions, and then measure whether those decisions have the desired effect.
To make this concrete, imagine a team that is estimating tasks based on a 7 hour ideal day (and an 88hour work day). At the end of each 2 week sprint the team either isn't meeting its goals or is feeling overworked, yet none of the tasks seemed more complicated than expected. One possibly is that that the teams days aren't really 7-hours. To measure this you could keep a chart on the wall, measuring team and organization meetings, adding marks to a bar chart after each activity that seems unrelated to coding. If at the end of a two week sprint this number is much higher than 10 hours (
10 days*(8hours@work-7hours-coding)), then you have some options to consider:
- Run meetings more effectively
- Decide if all of the meetings add value
- Decide to estimate based on a shorter ideal day
- Decide that the work day needs to be longer than 8 hours.
- (etc)
It's important that:
- The data collection be lightweight and that everyone understand that the data need not be entirely scientific to be useful. Too much effort to gather data can derail a change effort because of perceived cost.
- The data be visible and incremental. A hand drawn chart on a wall can be more effective than spreadsheet data that lives on someone's laptop. (But electronic data can also be made visible in the right context)
- The team evaluates the data with a goal of improving, not blaming. Maybe the extra time was spent in meetings was well-intentioned, or even necessary.
- The team consider a number of options to change the situation. (See Finding and Evaluating Options for more on evaluating options.)
What's useful about data is that it avoids arguments about who has the most accurate memory. Collecting data may not solve the problem; the data may leave you with more questions than answers, but without data you'll have no good way to decide what to try changing, and if the change had the desired effect.
Change is hard often because people often don't understand the need for change, or the possible changes. By demonstrating the alternatives and their value, and by gathering data to evaluate current practices, you can start the process.
In 2008, the 12207 standard for software life cycle processes and the 15288 standard for system life cycle processes were revised in an effort that finally harmonized system and software processes as well as bringing the respective IEEE and ISO/IEC standards into complete agreement. Some users mistakenly believe that these standards are targeted only to large organizations able to make a substantial investment in a complete suite of software and systems processes. However, these standards are also the best entry point for beginning users who desire guidance on as few as a single process. This three-part article will explain how entry-level users can apply the two standards.
by Jerry Weinberg
(and other books in the QSM Series) b Jerry Weinberg