Solving for innovation: Lessons from Google

Companies have seen massive change in the past few months, driving many to rethink the technologies and processes that keep teams effective and innovative from anywhere.

Research has shown that to cope with the changing environment, organizations with agile practices embedded in their operating models have performed better than their peers.1

Google has spent years thinking about how to maintain and grow a culture that fosters transformation, and we want to share their learnings with you.

“Google’s Guide to innovation: How to unlock strategy, resources and technology,” explores three essential concepts to help you and your teams better succeed:

- Building agility into apps and infrastructure
- Powering your business with data-driven intelligence
- Enabling workforce productivity outside the office

Learn how cloud computing has played a crucial role in enabling businesses to stay agile, and how to build a culture that supports change and innovation.

Organizations are facing unprecedented change and challenges stemming from a confluence of natural and artificial conditions. These forces are driving many to rethink the tools and technologies they use, and the places they need to be, to grow, and to innovate. 

Sustained competitive advantage cannot be achieved with technology alone 

To create a more innovative business, you must rethink how people, structures, and processes interact every day—we refer to this as organizational culture. The teams you rely on to build must have systems and processes that keep them engaged, amplify their ability to produce, and keep them consistently forward-looking. 

At Google, we’ve spent years thinking about how to maintain and improve a culture that fosters transformation and innovation. This has led to alignment around certain core principles that have informed our approach and supported Google’s culture for two decades.

Measure, make decisions, and be transparent in that process

Measurement is at the heart of everything we do at Google. We measure everything—from how our systems are running, to how productive we are, to how people are feeling. All the data we gather is extremely valuable, because it exposes problems faster than simply scratching our heads and wondering would. Once we gather that data, we still need to spend some time interpreting it, but at least we have a basis for judging how well our organizational structure is working.

A culture of measurement results in a collection of anecdotal information as well as quantitative data. Both are necessary to inform change. We perform a number of different measurements—for example, encouraging everyone to participate in an anonymous employee satisfaction survey every year. That data and that feedback loop facilitate our decisions to change how we’re doing things, as needed.

Once we’ve gathered the data and made a decision, it’s time to actually put those changes into motion. It’s important to recognize that a feedback system only works when people believe changes will be made as a result of their feedback. So the trick is to ask the questions and then actually do something with the result.

Transparency is another important part of Google culture. It’s important that we be transparent about the feedback we heard, and how we went about addressing it. Being transparent as a company increases customer trust on one hand, and employee trust on the other. It’s important that people understand why we prioritized the changes we made. That’s core to the company’s DNA.

Don’t be afraid of failure

Sometimes science learns more from failure than it does from success. Because if you ask why something didn’t work, you often learn more than you would have if it actually did work. And so, even at Google, we try a lot of things out that don't work—and we learn from them and refine our practices. And eventually, we hope, we get to the point where things that we want to work actually do work. So science is a lot like that. Google is a lot like that as well.

You have to have the willingness to allow failure. I’m not suggesting we should fail all the time—that would be a problem! I’m talking about the freedom to try things out without absolute certainty of success. This is the fundamental difference between engineering and research.

With research, you don't begin knowing the answer. With engineering, you think you know the answer, and you just have to build it. But what can happen with engineering is that you build it and then it doesn't work. These two things interact in the most wonderful ways. The engineer says, “I built it and it didn’t work.” The researcher says, “Why not?” And the engineer says, “I don’t know, can you help?” Together, they discover there’s a fundamental reason why this particular path for implementation didn’t work—and they learn from that. And then you get to develop a new design that takes this into account.

At Google, we’ll go down a number of different paths as we explore new capabilities in the system, and we often encourage people often to go down these paths, even if they might end up at a dead end. And we share, blamelessly, with others the fact that there was a dead end, so everyone learns. That's how we advance everybody's ability to carry out their work.