If you’ve been reading this blog for awhile, you’ve probably noticed that accepting uncertainty is a a recurring theme when it comes to Agile and agility. While it’s never stated outright as a “value” in either the Agile Manifesto or the Twelve Principles of Agile, the concept itself underlies many of the points made in those documents. In my opinion, it’s the primary cultural distinction between organizations that still cling to the old, outdated “waterfall” approaches. Waterfall creates a false sense of security by defining everything possible up-front. Agile accepts that we don’t always know everything, and that new information will not only be discovered, but might alter the path. Here are a few specific reasons why accepting uncertainty is essential for teams to be successful.
There are a great many different corporate cultures to be found in the world, but one consistency among far too many of them is decision-making processes that rely more on gut-level instinct and whomever yells the loudest rather than on hard data. For some companies, this has served the CEO well — a small, nimble startup can’t always waste time doing detailed validation or data-gathering in a “stop moving forward and you’ll die” environment. In other companies, it’s become the de facto standard due to strong personalities who may prefer authoritarian leadership styles over more democratic and empowering styles. Regardless of the reason, though — companies like this eventually wind up struggling because they make the wrong choice one time too many, based on the leaderships “market instinct”. And it’s our job as Product Managers to shepherd these companies into a more modern-day, data- and hypothesis-driven approach. Here are three major reasons why data-driven management is far more effective than management by gut or personality.
One of the more common challenges that growing companies face is balancing the needs and goals of the company with the needs and goals of its employees. And, unfortunately, all too often decisions are made with a business perspective that don’t take into account the potential effects on the personnel side of the equation. The simple fact is, people will do what we incent them to do and what we reward them for far more often than they will do what we want them to do, if there is any misalignment between the two. This applies across the business — from high-level executives to entry-level employees, and even out to our products — how we position, package, and price our products can often drastically affect how people will perceive and use the product.
While people always seem to nod their heads when you tell them this, it’s rather insane to realize just how often we create competition between these two things. Here are some things to consider when you’re trying to figure out how to get people to do what you want, or why they’re not doing what you expected.
One of the primary things that Product Managers are constantly working on is change — changing the way people view our customers, changing the way our customers view our product, changing the culture of our company to be more agile, changing peoples’ minds about what’s important and what’s not…the list goes on and on. And, not surprisingly, nearly every Product Manager eventually comes to the realization that change is hard. I mean really, really hard. And sometimes it seems like even the smallest changes are the hardest to get people to commit to and deliver on a regular basis. So why is change so hard? Here are a few of the common reasons…
One of the ongoing challenges that we face as Product Managers is that we’re primarily charged with predicting customer and user behavior. We’re constantly asked to come up with new ideas, new features, and new designs that we “know” will delight our users, or at the very least satisfy them. But the fact is, predicting human behavior is incredibly difficult — there are many thousands of people who have spent hundreds of years trying to figure out why people do what they do (they’re called psychologists, sociologists, and anthropologists), and we’re still making educated guesses at best. So, what are some of the challenges that we face?