How DDP is Different

Every new project is a learning experience, with countless surprises along the way.  A good planning methodology should attempt to capture that learning.  DDP places learning at the center of the planning process, with an emphasis on maximizing the amount of learning per dollar spent.  In order to achieve this, it introduces several key differences from standard planning methodologies.

First, traditional planning works in the forward direction, starting from a viable model of a project's likely costs and revenues to estimate the venture's profit margin.  Discovery-driven planning works in the opposite direction, asking you to state your required profits clearly up front, before you project any costs or revenues.

It should never be enough for us to simply plan a project, estimate our likely profits, and then green-light the project.  We should know in advance what we are trying to achieve and where we want to end up.  It should never be enough to know that a project will be profitable; we should start by asking how profitable we need the project to be.  Before you even begin planning, ask: what profit margins and ROI do we require in order to even make this investment acceptable in the first place?  What level of costs are we willing to bear to reach that goal?

By forcing you to state your required profits and ROI up front, DDP ensures that you clearly define a set of unbiased hurdles to test your project against.  If your project's costs and revenues can't clear those hurdles, that gives you a good justification for abandoning the planning exercise at that point and moving on to other, more promising ventures.

A second major difference of discovery-driven planning is that it focuses explicitly on learning.  The DDP process asks us to separate our knowledge -- the things we can state with absolute certainty -- from our assumptions.  For example, if we put in a spreadsheet that Apple will take 30 percent royalties from a product on the App Store two months from now, that's a well-known fact and we unlikely to change in the next two months, so it counts as knowledge.  On the other hand, factors such as project costs, future sales, development time, customer retention, and user demand for our product or any of our in-app purchases are all highly uncertain until they actually happen, and we can only estimate them within a certain range.  They count as assumptions.

All of our assumptions are ranges of possible values, not point estimates.  Although the initial stages of discovery-driven planning ask us to pick the most likely values for our assumptions, the later stages of planning require us to specify a full range of possible values for each.  As product development proceeds, we should be able to gradually reduce the range of uncertainty around all of our different variables.  The goal is to transmute our assumptions into knowledge until there are as few unknowns as possible by the end of the project -- or at least, until we can be confident that any variation inside the range of all of the unknown variables will not sink the project below our profitability hurdles.

By using ranges, we are acknowledging the unknowns, and then working to determine which assumptions are the most important using a sensitivity analysis.

DDP introduces the concept of the assumption-to-knowledge ratio -- the ratio of all of the collected uncertainty around our project against the things we can state with certainty.  DDP focuses on reducing the assumption-to-knowledge ratio throughout the project lifecycle as quickly and cheaply as possible, asking us to prioritize those efforts that will offer us the greatest reductions to the assumption-to-knowledge ratio at the lowest cost.

This allows us to build any number of sanity checks into the plan itself, so that if our assumptions turn out to be wrong at any point and the project no longer appears likely to meet our hurdles, we can either abandon it or attempt to adjust course with minimal financial loss.  We never want to be 90 percent of the way into a project before we realize we face intractable technology obstacles, that our external development partner has delivered unusable game assets, or that our expected customer base does not exist.

DDP asks us to attempt to nail down this uncertainty as early and as cheaply as possible, prioritizing those efforts that will reduce the assumption-to-knowledge ratio the most quickly at the lowest cost.

Finally, DDP also places a strong emphasis on redirection.   When we find that our plan no longer meets our initial financial hurdles, we're encouraged to attempt to modify the plan by scaling it up or down, finding ways to cut costs, trying different revenue models, or otherwise adjusting it in a way that will get it past our financial hurdles.

As a result of all of these factors, the DDP planning process grows beyond an exercise to simply estimate project scope and viability and becomes a tool to actively identify and manage risk.  It is an exercise in disciplined reasoning that forces us to clearly articulate what must be accomplished and how we plan to accomplish it, separate the knowns from the unknowns, and plan in a way that will expose the unknowns to the harsh light of reality as quickly and cheaply as possible.