“Exploding quantities of data have the potential to fuel a new era of fact-based innovation in corporations, backing up new ideas with solid evidence. Buoyed by hopes of better satisfying customers, streamlining operations, and clarifying strategy, firms have for the past decade amassed data, invested in technologies, and paid handsomely for analytical talent. Yet for many companies a strong, data-driven culture remains elusive, and data are rarely the universal basis for decision making.
Why is it so hard? Our work in a range of industries indicates that the biggest obstacles to creating data-based businesses aren’t technical; they’re cultural.”
––Harvard Business Review, 10 Steps to Creating a Data-Driven Culture
Data-driven culture has become a buzzword for publications from MIT to Forbes that offer counsel on what it means and how to achieve it in your company––and with good reason. According to The New Decision Makers: Equipping Frontline Workers for Success, published by Harvard Business Review, “Nearly 90% of organizations say success depends on data-driven decisions made by frontline employees.”
But for all its hype and promise, data-driven culture comes with another familiar cliche: It’s easier said than done. That’s why Fuzy went to a group of seasoned data science experts who have truly walked the walk when it comes to data-driven culture to get to the heart of what it takes to infuse company culture with data-based decision-making.
Ethan Handel, Tom Wilbur, and Christine Luo were steeped in data-driven culture at Indeed, an early pioneer in data science known for its data-centricity. When we asked them what the key readiness factors were for implementing product science within a company, they unanimously agreed that culture is key.
These are the three key cultural traits these data professionals believe are critical in laying a foundation for success with product science.
#1 Leadership prioritization
Our data experts and Forbes agree that executive sponsorship is a must for building data-driven culture. “It can impact how your people form decisions, what expertise you look for in new hires, what information your teams share with each other, and even how your overall organization operates. Strong executive sponsorship is almost always needed to overcome the organizational inertia that may resist and derail a transformation effort to become more data-driven.” Without it, any efforts to launch a product science initiative at your company are likely to fail.
According to Ethan Handel, a senior product manager and data scientist, how the use of data is discussed, incentivized, and rewarded from the top down plays a key role shaping culture. “A lot of it comes down to consistency by leadership in how we talk about our business and how we assess merit and outcomes. At Indeed, there's a ‘show me the data’ culture, and employees are far more likely to believe an argument when they see the supporting evidence.”
Senior product leader Christine Luo calls this phenomenon ‘The Great Equalizer.’ “Everybody says, yes, we're data-driven, but when you actually look underneath the hood, what you see is that people like data if it confirms their point of view. What I like about data is that it’s a great equalizer in the group. The most senior person and the most junior person are staring at the same chart. Data gives you an authority that you may not otherwise have yourself…What I would say for somebody who's trying to push for more data in their decision processes, especially at early stage companies, is that data will help you make more right decisions than wrong. Wouldn't you want to give yourself the greatest chance of succeeding?”
#2 Humility and curiosity
Senior product leader and data expert Tom Wilbur says that another major component of product science readiness is “to be humble before the data.” At Indeed, there’s an important mantra, “Strong opinions, weakly held,” says Tom. “It’s the idea that you have a strong point of view about the right business approach based on what you know––but when what you know changes, when you learn more, when you see different data, you're able to shift. There’s no personal identity tied to a particular solution. I think companies need to have the core expectation that data gives us insights that can be smarter than us (or at least we plus insights is certainly smarter than we alone).”
Hand in hand with humility comes curiosity, says Ethan Handel. “Indeed's culture has always started from a place of curiosity with rigor. We should be empowered to identify questions we care about and go measure them when we don't have measurements. It's just understood when we talk about impact that quantitative data is the gold standard. Within our products, the first question is always, ‘How do we know that?’ We don’t just want to know that something improved; we want to know how and why. However, there is a certain point when you can take this too far. I like to say that data is better than opinions, but opinions are better than inaction. You can’t be overly dogmatic about having a data point for every little thing. Sometimes you just don’t know and have to try something in order to get the data you’re looking for.”
#3 Transparency and autonomy over gatekeeping
While transparency and autonomy may seem like abstract cultural ideals, these factors actually materially affect how data is handled and structured within companies. Is it siloed off by department or visible to all teams? This can impact everything from collaboration to speed to market, and business planning and strategy. As Tom puts it, “How do you deal with the fact that organizations have giant volumes of data that are siloed, that are disconnected, that are managed like the Key to Kingdom, held sacred and locked away? A big part of becoming a data-driven company is getting that data to be usable and used.
Ethan illustrates exactly how transparency and autonomy in action can lead to better outcomes. “Indeed invested in data infrastructure from the early days––20 years ago. Nearly all that product data is accessible to literally everyone at the company. When you're not building barriers, you're building transparency. We hold the assumption that everyone should be looking at data. But you have to start with transparency and that transparency begets collaboration. What you get from that is people sitting at lunch together talking about queries and getting an insight that you wouldn't have figured out alone. Everyone's excited to go poke around and answer the question in a better way and challenge the assumptions.”
Cultivating a Data-Driven Culture: The Path to Product Science Success
Building a data-driven culture within companies is essential for leveraging the vast quantities of data available for fact-based innovation. While technical challenges can be overcome, the main obstacles lie in cultural factors. Leadership prioritization, humility and curiosity, and transparency and autonomy over data gatekeeping are the key traits necessary to lay a strong foundation for success with product science. By fostering these cultural traits, companies can empower their teams to make data-driven decisions, leading to better outcomes and increased success in the long run.