PDF Summary:Upstream, by Dan Heath

Do you ever feel like there are endless fires to put out—in your personal life, at work, and in society as a whole? What if you could work at the root of the problem instead, preventing crises from happening altogether? In Upstream, business writer Dan Heath explains a strategy for focusing energy on proactive, long-term solutions: upstream problem-solving.

Heath concedes that the concept of working on the root causes of problems is not always the easiest route, but it does offer the potential for improving flawed systems and reducing harm on a broader scale. He describes guidelines for implementing upstream action that you can apply to many different contexts ranging from combating climate change to improving your business strategy. Heath also identifies some of the common roadblocks to avoid as you tackle complex problems through upstream intervention. Throughout this guide, we’ll also include techniques from other experts on how to solve problems before they happen.

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(Shortform note: In Scarcity, Sendhil Mullainathan and Eldar Shafir contend that in addition to preventing people from making good long-term decisions about things like time and money, scarcity can also exacerbate problems by causing us to neglect other parts of our life that we care about. For example, if you’re worried about the limited time you have to finish all of your work, the scarcity mindset might prevent you from being fully present and engaged with your loved ones. Therefore, the scarcity hinders upstream solutions to the original problem and potentially causes new problems in other areas.)

Communities Normalize Widespread Problems

Lastly, Heath explains that people favor reactive solutions because when a problem becomes pervasive, people naturally adapt to the situation and normalize it. For example, in the previous scenario, if your house seems like it’s always a mess no matter how often you tell your housemate to clean, you might eventually accept the messiness as an inevitable state of things. You might use reactive strategies like jumping over laundry piles instead of trying to prevent the mess.

Heath writes that people forget that they have the agency to avoid many problems. Furthermore, even if a problem impacts people broadly, there’s often no single actor with clear and sole ownership over resolving widespread societal crises such as health epidemics.

(Shortform note: In Emotional Intelligence 2.0, Travis Bradberry and Jean Greaves recommend a concrete strategy to combat this tendency to avoid or normalize problems. They suggest a self-management technique of carving out 15 minutes per day, away from your regular work, that you can dedicate to problem-solving. On the other hand, in The Success Principles, Jack Canfield points out that it’s important to only commit to things you can deliver on, suggesting that you should be selective about the problems you take on. This aspect of being realistic about your capacity might be particularly important when working with other people who will feel let down if you don’t follow through.)

Advice for Upstream Intervention

Given these cognitive biases toward reactive thinking, how can people intentionally foster an upstream mindset? In this section, we’ll describe Heath’s recommendations for implementing upstream solutions in any context. We’ve consolidated his advice into three key points: Gather people with a shared interest, reform systems by identifying small tweaks that can produce significant change, and use data to guide experimentation.

Gather People With a Shared Interest

The first important step for upstream intervention is gathering a team of people who can tackle the problem from many angles—a process Heath calls “surrounding the problem.” Heath argues that diverse people with a shared interest can make a big difference when they align their efforts toward a common goal. He claims that if there’s nobody with explicit responsibility over a problem, it’s up to individuals to take on the challenge and recruit others who are motivated and empowered to help.

(Shortform note: Gathering a team to take upstream action is likely to benefit from different forms of diversity: variation in their professional skills or expertise as well as cognitive diversity. Cognitive diversity represents the different perspectives and ways of processing information that people have, and business research shows that more cognitively diverse teams perform better at problem-solving.)

To illustrate this advice, let’s examine the earlier example of a garbage-polluted stream. Countless people could work together to eliminate the littering problem long term: individual community members who want access to clean public spaces, conservation groups that want to maintain healthy ecosystems, schoolteachers who want to instill good values in their students, local officials who want to respond to their constituents’ concerns, and manufacturers who want to appeal to consumers’ eco-friendly values. Each of these groups has different motivating factors, but they could all benefit from taking upstream action to prevent stream pollution.

(Shortform note: One potential challenge to gathering the right team for upstream action is motivation. In The Oz Principle, Roger Conners, Tom Smith, and Craig Hickman assert that even if you feel inclined to let someone else solve a problem, you should want to take responsibility for a problem because of the benefits it offers. The authors claim that participating in the problem-solving process helps you avert a crisis, makes people think highly of you because of the important role of problem-solvers in society, and enhances your personal development through creative work and active learning.)

Identify Small Changes With Significant Impacts

Heath’s next piece of advice builds on the group of people who are invested in reform: Change systems rather than reacting to byproducts of bad systems. He recommends achieving this by targeting opportunities to make minor changes that have a disproportionately large impact on the problem (what Heath calls “leverage points”).

(Shortform note: In Thinking in Systems, Donella Meadows defines leverage points as places to intervene in a system. Within this definition, there are low leverage points—aspects of a system where changes will have a minimal impact—and high leverage points, where changes to a system have a big impact. One of Meadows’s examples of a high leverage point is changing the rules of a system (things like laws, contracts, and incentives) as well as changing who gets to set the rules.)

In one of Heath’s examples, the travel booking company Expedia eliminated nearly 20 million customer support calls every year simply by changing its booking system to ensure that customers received a copy of their travel itinerary immediately after making the reservation. This ensured travelers had all the information they needed without having to request it via phone. By zeroing in on this upstream leverage point, the company drastically reduced the time and money spent on this simple customer request.

(Shortform note: One strategy that may have identified this leverage point sooner at Expedia is actively soliciting feedback from employees about what’s working and what isn’t. Feedback from the customer support employees might have clarified early on that most of the calls were requests for an itinerary. Creating a trusting environment for people to provide feedback might give business managers an opportunity to spot upstream problem areas quickly.)

Use Data to Experiment Continuously

Heath’s third component of upstream intervention is using data to guide and tweak experimental solutions. As discussed in an earlier section, the benefits of upstream problem-solving are not always immediately obvious. Heath writes that in some areas—like preventive health care or changing a relationship dynamic—it could take years to see a significant change in outcomes. As a result, it’s important to be patient with the process and identify relevant metrics that you can continuously monitor to ensure that you’re headed in the right direction.

Heath contends that ample data allows you to not only identify early warning signs of a problem—to avoid being forced to react rather than prevent—but also allows you to track progress toward a goal and improve your strategy. For example, in the polluted stream scenario, relevant data might include the volume of trash on the banks of a particular stretch, the population of wildlife in the stream, or the number of families visiting the area (assuming that trash discourages both wildlife and humans from enjoying the stream).

An increase in the volume of non-biodegradable materials purchased locally might serve as an early warning sign that the problem will get worse, assuming that the prevalence of these materials contributes to the littering crisis. Heath suggests that trends in these areas could indicate success for the various stakeholders or indicate a need to change course. He emphasizes that you don’t have to predict the best solution from the beginning, you just have to respond accordingly to what the data shows.

Advice for Using Data Effectively

In the context of personal development, research suggests that humans are generally bad at self-reporting progress toward a goal. This is because we tend to remember things as we expected or hoped them to be rather than making an objectively accurate assessment. In The Bullet Journal Method, Ryder Carroll recommends creating customized tracking sections in a monthly overview section of your journal to continuously monitor your progress toward goals. He contends that this strategy increases self-awareness and provides insight into which efforts are successful and which ones require modification.

In The 12 Week Year, Brian P. Moran recommends tracking progress with two key indicators: lag and lead indicators. Lag indicators are the end results of your actions—the long-term outcomes you hope to achieve by taking upstream action. The lead indicators are the short-term actions taken to achieve that goal.

For example, in the polluted stream scenario, the lag indicators would include the volume of trash in the stream and the lead indicators might include educational flyers posted about littering or the number of trash cans installed. Together, the two types of data help you understand both progress toward your larger goal as well as how effective your strategy is.

Challenges of Upstream Work

Now that we’ve covered the three key aspects of upstream problem-solving, we’ll describe some of the challenges that Heath says to look out for as you implement these strategies. These potential roadblocks include metrics that falsely indicate success (what Heath calls “ghost victories”), unintended consequences that exacerbate the initial problem, and a mismatch between the people who contribute to solutions and those who benefit from them. In this section, we’ll describe these common challenges in more detail.

Data-Related Problems

Heath contends that there are multiple ways for data to fall short when it comes to accurately indicating the success of upstream efforts. Therefore, it’s important to constantly reevaluate the metrics you’re using to measure progress.

The first potential problem with data occurs when an overall trend driven by external factors gives the illusion that your specific efforts are driving change. For example, a manufacturing company might try to combat low customer satisfaction by beefing up quality control measures during the manufacturing process for products (an upstream solution). Customer satisfaction metrics might falsely indicate that these efforts are working even if the change was due to a personnel change in the customer support department.

Heath writes that the second data problem arises when the metrics for tracking upstream intervention don’t align with the overall goal. For example, in the customer satisfaction example, metrics related to quality control, like reducing the number of manufacturing errors, might not significantly contribute to customer satisfaction. A more accurate measure of success could be the number of negative reviews on the business’s retail website.

Lastly, Heath explains that a metric for short-term efforts can morph into the end goal rather than a means to a larger change. For example, if workers at a business get bonuses when there are fewer negative reviews for a product, reducing those numbers could become the top priority for the employees rather than improving the product and customer satisfaction. This could lead to people falsifying data or making it more difficult for customers to leave reviews, which is counterproductive to the long-term goal.

Heath suggests that one potential solution to this challenge is to pair two complementary metrics together to ensure that you’re moving toward the initial goal. For example, you might supplement the metric of negative product reviews with surveys on how individual consumers’ opinions changed over time.

A Business-Oriented Approach to Heath’s Data Problems

The challenges that Heath discusses in this section are all related to the overarching problem of how to accurately measure and evaluate success toward a goal—a problem that business managers frequently wrestle with.

In Built to Last, Jim Collins and Jerry I. Porras write that it’s important for a company’s policies and processes to reinforce the core values of the company to avoid any misalignment. They recommend staying vigilant and noticing when things like reward systems or organizational structure are slowing down progress toward the core goal.

In Playing to Win, Alan G. Lafley and Roger Martin suggest a concrete strategy for businesses to successfully measure progress toward achieving a goal or solving a problem. They recommend giving each individual department within a company its own smaller goals to ensure that the strategy toward a larger goal is well-rounded.

By using multiple metrics across different departments, the company can reduce the likelihood that one of the data problems Heath describes will derail the problem-solving effort.

For example, if one department thinks it has achieved its goal, but it was actually a larger, external trend that caused a shift in its target metric, company-wide efforts toward the larger goal will continue across the other departments. Likewise, if one metric is misaligned with the larger goal or the metric itself becomes the priority, other departments’ metrics would compensate for it.

Lafley and Martin’s recommendation for compartmentalized goals could also be adapted to Heath’s framework and applied in other contexts by giving each member or sub-group of the problem-solving team its own target metric for addressing root causes.

Unintended Negative Consequences

Heath warns that in addition to challenges related to data, there can also be unintended negative consequences of upstream intervention. For example, at the hypothetical company with low customer satisfaction, managers might decide that they need to redesign their products to make them easier to use. However, the end result might leave customers even more dissatisfied than before because the new product design doesn’t look as aesthetically pleasing as it used to.

Heath’s solution to this challenge is staying vigilant to unintended outcomes and adjusting your strategy when the need arises.

(Shortform note: On the flip side of this possibility that your upstream problem-solving will have unintended negative consequences, it’s also possible that your actions will have positive outcomes that you didn’t expect. For example, in User Friendly, Cliff Kuang and Robert Fabricant explain that when engineers design a new product to be user-friendly for disabled people, it’s likely to also benefit the general population. One of Kuang and Fabricant’s examples is the Oxo vegetable peeler: It was designed for people with arthritis who have trouble gripping narrow, metal peelers, but people in general found it more comfortable to use, and it inspired a widely popular design component in many kitchen utensils.)

Mismatch in Contributors and Beneficiaries of Upstream Change

The last challenge of upstream work that Heath discusses is a mismatch between the people who are empowered to do the upstream intervention and those who benefit from those solutions. Heath writes that upstream intervention often requires costly or time-consuming initiatives, and it might not always seem worth it for people to make the effort or investment.

For example, consider a business scenario where improving customer satisfaction requires labor-intensive efforts by lower-ranking employees. However, those employees won’t benefit from the increase in customer satisfaction and a subsequent increase in the company’s profitability—they might get the same wages while the customers and shareholders receive the benefits. This could lead to a lack of motivation and failure to achieve the long-term goal.

(Shortform note: A couple of potential strategies for motivating someone to help with upstream interventions include explaining to the person why they would be uniquely qualified to support the effort and giving the person the autonomy to decide how they’ll work.)

Heath explains that to combat this challenge, it’s important to ensure that everyone involved in the upstream efforts has a vested interest in the overall goal, whether it’s financial, moral, or ideological. This also applies when an organization or business has to provide financial investment for upstream work and the monetary return on their investment is not guaranteed. For example, it may not improve a company’s profitability to pay for their employees to take sabbatical leave, but the executives might still decide that it’s worth making this upstream investment because they value the long-term well-being of the employees.

(Shortform note: This mismatch problem that Heath identifies might also be addressed by obtaining funding for upstream work from public institutions that are tasked with ensuring people’s well-being rather than making a profit. This could include government funding for social programs or funding from non-profit organizations that are focused on community issues. In these cases, the organizations are more likely to provide financial investment for the long-term goal of social benefits.)