Data-Driven, Data-Informed, or Data-Inspired?

A Simple Framework for Making Decisions with Data

“Whenever you see a successful business, someone once made a courageous decision.” - Peter Drucker

The phrase ‘data-driven’ has turned out to be quite contentious!

Hey there and welcome back to the Product Playbook. This post discusses making decisions with data and aims to help you understand when and why to use each approach. Yalla beena.

As a product manager, having data is invaluable for steering your product roadmap and making decisions. However, not all decisions require the same level of scrutiny. The role of data can range from being the sole driver to simply inspiring. In this post, I explore three approaches to leveraging data: data-driven, data-informed, and data-inspired decision-making. By understanding when to apply each method, you can avoid analysis paralysis and reduce stress around decision-making.

Three Ways to Use Data

To kick things off, let's define the three data decision-making methodologies.

Data-Driven Decisions: This is when you let the data call the shots. You look at the numbers, see what they’re telling you, and follow their lead.

Data-Informed Decisions: Here, data is part of the mix, but not the only ingredient. You also consider your own experience, what your users are saying, and what your gut is telling you.

Data-Inspired Decisions: In this scenario, you use data as a source of inspiration. You explore different data points, see what ideas emerge, and use those to guide your decisions.

You’ll probably find yourself using a bit of each approach, depending on the situation and the depth, breadth, and type of data you have.

Choosing Your Approach

The choice of decision-making approach often hinges on the nature of the problem you're tackling. Well-defined, structured problems with clear metrics and benchmarks lend themselves well to data-driven decisions.

In contrast, unstructured or ambiguous problems may benefit more from a data-informed or data-inspired approach. Pivoting your product's strategic direction, exploring new market opportunities, or addressing complex user needs often require a more holistic perspective that combines data with other inputs.

When you’re faced with a decision, think about the problem you’re tackling. Is it a well-defined issue with clear metrics? That’s a sign to go data-driven. You’ve got solid numbers to back up your move, like optimizing conversion rates or reducing load times.

But what if you’re looking at the big picture, like pivoting your product going into a new business vertical? That’s where data-informed shines. You’re not just looking at the numbers; you’re also weighing in your expertise and the market pulse.

And then there are times when the data just isn’t there, or it’s too early to tell. This is usually the case when you’re embarking on true innovation. In these cases, data-inspired decisions can lead the way. You’re guided by early signals and your intuition to make a pioneering move.

Some Heuristics

Here are a few heuristics to help you choose how much influence you will give data in your decision-making process:

• Operational or Optimization Tasks: Go data-driven. It’s perfect for routine decisions where you can apply tried-and-true frameworks.

• Strategic Moves: Choose data-informed. You need room to experiment and a keen eye on key performance indicators (KPIs).

• Decisions in the Dark: Lean on data-inspired. When data is scarce, let your instincts and early feedback guide you.

In addition, consider your team’s experience and how often you need to make these calls. Data-driven methods are great for helping newer team members make smaller decisions. But for the big stuff, you’ll want to be data-informed, with enough data and time to test the waters.

Setting Metrics that Matter

Regardless of the approach you choose, you need to select the right metrics. Take the time to identify the metrics that truly matter for your product's success. Align your metrics with your overall business objectives and user experience goals to ensure you're measuring what truly counts.

Practical Examples

Let’s put this into practice with some examples for each approach:

Data-Driven Decision: Analysts at a popular video streaming service noticed a worrying trend - viewer engagement was dropping for certain types of content. They discovered that many users were abandoning shows after just a few episodes due to slow pacing or lack of memorable characters early on. Armed with these insights, they implemented new algorithms to prioritize shows with gripping opening acts, leading to viewers sticking with shows longer and thus, reduced churn.

Data-Informed Decision: A popular music streaming service has long used data to drive its music recommendations. However, when exploring the idea of venturing into podcasts, they couldn't rely on data alone. While user listening data provided some guidance, they also had to consider competitive podcast landscape among other inputs. Ultimately, they launched a limited podcast offering to beta users, combining data-driven personalization with qualitative inputs to shape their long-term podcast strategy.

Data-Inspired Decision: For years, a popular e-commerce product has set the standard for e-commerce personalization. But even they couldn't fully anticipate the transformative potential of voice assistants. With limited initial data on voice shopping behavior, they leaned into their pioneering spirit. Inspired by early user interactions and an intuitive grasp of voice's potential, they doubled down on their voice assistant’s development. This data-inspired bet paved the way for voice's widespread adoption and the company became a leader in the space.

Going Back to Your Why

We all want rules to help us do things better, whether that is being more productive or making decisions. Remember to refer to your product vision, strategy, and goals when making decisions. Your ‘Why”.

So whatever method you use for data as an input to your decision-making process, your decisions need to sync with your ‘Why’.

And that’s it for this post! Does it make sense? What could I have explained better? I would love to hear from you.