Why some projects are harder than others
Over my career, I've had the experience of working on projects that have gone wonderfully well and I've worked on projects that just ran into the sand and went nowhere. I've come to recognize the red flashing warning signs for a certain type of project that's pathologically bad: they tend to be projects involving wicked problems or have the characteristics of wicked problems. Interestingly, I've come across more wicked problems in data science than elsewhere.
Wicked problems
The term 'wicked problem' comes from the planning and policy world [Rittel and Webber] and refers to problems that are difficult or impossible to fix inside the current social, political, and economic system. A good example is solving poverty; there are many, many stakeholders, each with fiercely different views, and no clear measure of success (how is poverty measured, is the goal reduction or eliminations, etc.). Poverty is also linked to other factors too, like level of education, health, housing, etc. If you were a politician, do you think you could solve poverty?
(Properties of wicked problems. Image source: Wikimedia Commons, License: Creative Commons, Author: Christian Sarkar)
In the five decades since Rittel and Weber first discussed wicked problems, researchers have identified some of their key characteristics:
- Wicked problems are not fully understood until after the creation of a solution.
- Wicked problems have no stopping rule, there's nothing to tell you that you've reached an optimal solution.
- Solutions to wicked problems are not right or wrong: they are better or worse, or good-enough or not-good-enough.
- Every wicked problem is new: you can't apply prior learning to it.
- Wicked problems have no alternative solutions to choose from.
Rittel and Weber's seminal paper points out a key feature of these types of problems: they're not amenable to traditional project management using a phased approach (usually something like "gather data", "synthesize data", "create plan", "execute on plan", etc.). This is crucial to understanding why projects solving wicked problems go wrong.
Wicked problems in software
If you think wicked problems sound a lot like some software development projects, you're not alone. In 1990, DeGrace and Stahl published "Wicked problems, righteous solutions" which laid out the comparison and compared the utility of different software development methodologies to solve wicked problems. To state the obvious, the killers for software project predictability are understanding the problem and applying prior learning.
Readers who know agile software development methods are probably jumping up right now and saying 'that's why agile was developed!' and they're partly right. Agile is a huge improvement on the waterfall approach, but it's not a complete solution. Even with agile, wicked problems can be extremely hard to solve. I've had the experience of working on a project where we found a new critical requirement right towards the end, and no amount of agile would have changed that.
Wicked problems in data science
Data science has its own wicked problems, which I'll put into two buckets.
The first is the ethical implications of technology. Facial recognition obviously has profound implications for society, but there are well-known issues of racial bias in other data science-based systems too (see for example, Obermeyer). Resolving these issues isn't only a data science problem, in fact, I would say it can't only be a data science problem. This makes these projects wicked in the original sense of the term.
The other bucket is operational. Although some data science problems are well-defined, many are not. In several projects, I've had the experience of finding out something new and fundamental late in the project. To understand the problem, you have to solve it. For example, you may be tasked with reducing the RMSE for a model below some threshold, but as your model becomes more sophisticated, you might find irreducible randomness or as your understanding of the problem increases by solving it, you may find there are key missing features.
Here are some signposts for wicked problems in data science:
- Any algorithm involved in offering goods or services to the public. Racial, gender or other biases may become big issues and these risks are rarely outlined in the project documentation - in fact, they may only be discovered very, very late into the project. Even worse, there's often no resource allocation to manage them.
- No one in your organization has attempted to solve a problem like this before and none of the people on the project have prior experience working on similar projects.
- The underlying problem is not fully understood and/or not fully studied.
- No clear numerical targets for project quality. Good targets might be thresholds for false error rates, RMSE, F1 scores, and so on.
What's to be done?
Outline the risks and manage them
It's always good practice to have requirements specifications and similar documents. These project documents should lay out project risks and steps to counter them. For example, facial recognition projects might include sections on bias or ethics and the steps necessary to counter them. Managing these risks takes effort, which includes effort spent on looking for risks and estimating their impact.
Expect the unexpected
If wicked problems can't be fully understood until they're solved, this is a huge project risk. If a new requirement is found late in the project, it can add substantial project time. Project plans should allow for finding something new late into the project, in fact, if we're solving a wicked problem, we should expect to find something new late in the project.
Set expectations
All of the stakeholders (technical and non-technical) should know the risks before the project begins and should know the consequences of finding something late in the project. Everyone needs to understand this is a wicked project with all the attendant risks.
Communications
Stakeholders need to know about new issues and project progress. They must understand the project risks.
Overall
If a lot of this sounds like good project management, that's because it is. Data science projects are often riskier than other projects and require more robust project management. A good understanding of the dynamics of wicked problems is a great start.
