In the new knowledge economy, the winners are the ones who learn quickly. But learning means nothing, if it cannot be applied to the business. So the winners are the ones who learn, and use that knowledge by deciding where to go, and what to do next.
If decision making is the king maker, it can also come back to haunt us. Because high stakes can go either way.
Last time, we talked about how to pick a winning product. The picking part is about making decisions, and we’d like to make the right one every time.
Part of decision science comes from understanding how work works. You may think you know, but if you practice some kanban, and visualize the work, you know that what seems like a regular work, may not be that regular. In his book, Principles of Product Development Flow, Don Reinertsen goes deep science on the flow of work in an organization. I can testify that reading this book gave me chills. Years into agile and all that fluffy stuff that goes with it, finally a book comes along that measures work, with equations and laws, and queuing theory, and explains why things are the way they are. My inner engineer threw a party.
Among all the information in the book, comes the knowledge of how to improve decision making. You probably won’t be surprised that it is based on collecting existing information. But we’re really gotten used to making decisions based on partial data, or even no data at all, at worst. While the ability to make decisions without all the pieces is a virtue in our uncertain world, many times the information that can help us make better decisions is there for the picking, and we disregard it completely.
When to make a decision is as important as actually making it. Too early, and we’re committed, hard to change back. Too late, and we might miss the boat. Chris Matts and Olav Massen have written a whole book on the subject, called Commitment. Chris has introduced Real Options as a way to look at how to treat choices as options. Once you start doing that, you don’t go back. For example, you may look at your backlog as a set of prioritized requirements. In reality, they are all options, with a certain value (that may change in time), and expiration date (because sometimes feature A has value because feature B was not there. But after B is implemented, the value of A vanishes). The final part of Real Options – only commit when you know why – is hurtfully missing today, because we’re still making decisions based less on data, and more because of pressure, gut feeling, ego, and all kinds of voodoo.
Since I’ve already brought up backlogs, how about deciding what to do first? Even at the portfolio level, where risks are bigger? How do we prioritize work?
In our first-to-market world, there’s a cost of coming second. This is where Cost Of Delay as a prioritization scheme comes into play. And it’s not just about making money off new products. There’s a cost of maintenance projects that are needed not to lose existing customers, for example. We need to prioritize all kinds of projects that help the bottom line in some way, and putting a cost on each can help us make the right decision. Cost Of Delay is covered extensively in Don Reinertsen’s book, and it’s not just theory: Shipping company Maersk is using COD to manage their entire IT business, and if one of the biggest companies on Earth can do that, you can too probably.
What’s the opposite of using data to make decision? Estimations. I’ve written and presented about #NoEstimates, and the biggest drawback of cost estimations, is that they provide a false sense of confidence when making decisions. Instead, we’ve made the estimation process into an art form, where many people are creating numbers that everyone knows are fake, and are treated as such. As #NoEstimaters say, there are alternatives, or at least complementary ways to make better decisions that take other parameters into account to corroborate the truthiness of the estimates.
Making the right decisions can build or break businesses. Shouldn’t we explore better ways to do that?