Should product management decisions be data-driven or more so data-informed? Should Product Managers lead with intuition and use data to back up their assumptions?

There is definitely a fine line here. Every decision cannot be data driven and will likely be informed to some extent BUT part of the excitment of product management is leaning on that intuition. In some cases, you will have data to back up your assumptions and in others you will not. Every situation is going to be different and you'll have to become an educated risk taker. Leaning on previous experiences or patterns you've seen somewhere else.

Data should be used to inform product management decisions rather than drive them. A product manager should lead with intuition and use data to support their assumptions. Data should be used to inform decisions, but it should not be the only factor in making decisions. It is important for product managers to use their experience to make informed decisions, and then to use data to validate those decisions. 

Data can provide insight into:

• customer needs
• market trends
• and product performance 

It can also be useful in identifying opportunities and risks. Data alone, however, cannot provide the full picture. Data needs to be interpreted by product managers using their experience and expertise in order to make decisions that are in the company's and product's best interest. 

These decisions can be validated and supported with data. For example, your key customers' needs cannot be understood by data alone, you need empathy, context, interpretation, and intuition to make a decision. 

In your role as a product manager, you should be deeply engaged in the product, market, business, and customer pains/experiences which should help drive the intuition you feel for the product strategy.

Classic PM answer - it depends. 

The longer you are at the organization, the better intuition you will develop.

The same applies to the product maturity, e.g. 0-1 vs. 1-N. New product to market requires PMs to look at trends and have a better sense of the customer needs. Well-established product decisions align more with data e.g. Google's conclusion that ms of latency = impact on $ revenue. 

Product management decisions should ideally be data-informed rather than purely data-driven. Being data-informed means leveraging data to guide and support decision-making, while also acknowledging the context, the subtleties of customer behavior, and the broader market trends that quantitative data might not fully capture.

Data is invaluable for validating hypotheses, understanding user behavior, and measuring performance against goals. However, it's equally important to recognize the limitations of data and the potential for it to lead to suboptimal decisions if relied upon in isolation. Product Managers should indeed lead with an informed intuition—a blend of experience, understanding of the customer, and strategic vision. This intuition is then refined and validated through the use of data. By using data to back up assumptions, Product Managers can challenge their biases and ensure that their strategic direction is grounded in reality.

Ultimately, the most successful Product Managers are those who can adeptly interpret data, understand its implications, and also know when to question its suggestions. They balance the art of intuition with the science of data, using each to enhance the other. This holistic approach allows for innovation and creativity, driving product decisions that resonate with customers and succeed in the market.

While intuition serves as a valuable starting point for Product Managers, particularly those well-versed in their field, market dynamics, and customer preferences, relying solely on intuition can lead to biases and blind spots. Product Managers may become overly attached to their ideas, making it challenging to pivot or abandon unsuccessful pursuits.

Data serves as a crucial tool in validating assumptions and guiding decision-making. However, it's essential to recognize that data can be manipulated to support preconceived notions. Therefore, a balanced approach is imperative, emphasizing being data-informed rather than strictly data-driven.

In instances where pioneering new categories or introducing innovative solutions, relying solely on existing data may not suffice. In such scenarios, taking calculated risks becomes necessary, accompanied by continuous stakeholder engagement and customer feedback to ensure alignment with genuine needs.

Establishing metrics and data collection mechanisms from the outset is paramount. This facilitates ongoing evaluation of product performance and user engagement, enabling timely adjustments and preventing prolonged investment in misaligned initiatives.

The answer to this question falls in the category of "it depends" :) Let me give a few examples where different approaches would help drive the decision making.

Example 1: You are trying to build a new solution/product that has no precedent within the company

In this example, you would need to use data to make the business case, however you would also need to make some assumptions around adoption, relevance to customer verticals and likely competitive analysis to guide the narrative. This is an example where you are not using any historical baseline data from within the company, but using sales inputs and maybe market analysis to help drive decision making

Example 2: You are trying to launch a new generation of product which would be successor to an existing product as an incremental upgrade (think iPhone 10 vs iPhone 11 etc.)

In this case, you likely already have baseline data to make decisions around demand forecast, pricing strategy, global revenue split etc. This is an example where most of your decisions would be data driven to the extent possible. Ofcourse, this could also be the case for software feature enhancements or products that reply on user interactions, dwell times etc. to make help drive decisions.

One golden rule with regards to using data is - don't get caught in the analysis paralysis cycle. Time is of essence while driving business, especially in a competitive market. Hence, take calculated risks and try to use data to back those up as much as possible or guide decisions wherever possible.

The ongoing debate between data-driven decision-making and being data-informed continues to captivate the product management community. From my personal experience, data has always served as a powerful tool in navigating the intricacies of decision-making, particularly in overcoming the challenges of indecisiveness. This holds true regardless of whether the product is targeted towards B2B or B2C markets. However, it is important to acknowledge that relying exclusively on data may not always result in the most optimal decision-making outcomes.

Let me provide an example to illustrate this point: In a previous role where I was responsible for a leading enterprise search product, we introduced an advanced query feature that quickly gained traction and showed promising usage. The feature appeared to be popular, and the application efficiently handled a flurry of complex search requests. However, upon engaging with users, we discovered that many struggled with the complexity of the queries and desired the ability to modify or refine them. Without a seamless way to edit and relaunch searches, users ended up launching multiple versions of the same query repeatedly. This issue wouldn't have been apparent through data analysis alone; it required direct user interaction to uncover. Failing to identify this early on would have led to celebrating premature success and exacerbating user frustration down the line.

On the other hand, relying solely on intuition with limited data is not advisable, especially when entering a new domain or industry. Without adequate experiential or foundational knowledge to guide intuition, incorporating data—both qualitative and quantitative—can foster a better understanding of users and the product over time. This understanding serves as a prerequisite for cultivating intuition. This approach allows you to validate your decisions against your intuition and even question the accuracy of the data if your instincts suggest inconsistencies. Ultimately, the crux lies in striking a delicate balance—embracing the tangible wisdom offered by data while cultivating the experiential acumen necessary for intuition to flourish.