The pitfalls of being data-driven without context

Data is king – or so we’re told. In today’s hyper-connected world, where every click, like, and share is meticulously tracked and analyzed, being data-driven has become the gold standard for businesses. It has also become the go-to line that PMs and strategists use when describing their work style. After all, what could be more reliable than hard numbers?

But what if I told you this fixation on data, without understanding the stories behind these numbers, is more of a cop-out than a strategy?

Many SaaS companies, despite being data-driven, end up failing because they overlook critical qualitative aspects of their business. They put too much faith in their numbers and ignore the human side of their operations.

An example of this is seen in the common failure of SaaS start-ups to meet the right product-market fit. Around 42% of SaaS start-ups cease operations because they fail to build a product that fits market needs, often due to a lack of qualitative insights into what their customers actually value.

The data trap: A false sense of security

Data in itself is not bad. However, its interpretation can be misleading without proper context. For instance, consider a product feature that is used less frequently according to your analytics. The immediate data-driven decision might be to scrap the feature, considering it unsuccessful.

But what if the real issue was that users were unaware of it, or found it hard to use due to poor design? Without qualitative insights, such as user interviews or surveys, the true value of the feature could be completely overlooked.

Data does not, and should not, exist in a silo. A single metric, no matter how impressive or compelling it might seem, can never provide the full picture. It’s akin to looking at a single piece of a jigsaw puzzle and trying to understand the entire image. To truly gain insights from data, we need to look at a series of metrics in tandem, forming a funnel that provides a comprehensive view of our situation.

Imagine trying to evaluate your health based on a single metric from a blood test. If your cholesterol level is perfect, does that mean you’re the epitome of health? Not necessarily. To gain a comprehensive understanding of your health, doctors look at a variety of metrics such as blood pressure, sugar levels, heart rate, and many more. Even then, these quantitative metrics are supplemented with qualitative information like your lifestyle, diet, and stress levels.

Similarly, in the business world, you need a combination of metrics to draw meaningful insights. For instance, high traffic on your website is a positive sign, but without knowing the conversion rate or the bounce rate, you can’t truly assess the effectiveness of your site.

Are visitors actually engaging with your content, or are they leaving within seconds of arrival? Are they going through the purchase journey, or are they dropping off at a particular step? A singular focus on one metric could lead to a distorted perception of success and may result in misguided decisions.

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Vanity numbers and illusions

Numbers don’t lie, right? Wrong. Data can be manipulated or used selectively to support pre-existing beliefs or ideas. This is especially dangerous when data-driven decisions are used to avoid accountability. “The data made us do it” becomes an easy excuse for controversial decisions, conveniently ignoring other factors at play.

For some people, there is a temptation to dazzle stakeholders, particularly investors, with impressive-sounding metrics. These so-called “vanity metrics” often look good on paper but don’t necessarily reflect the true health or potential of a business. They might show off high numbers, but they don’t contribute to understanding the business’s value proposition or strategic direction.

For instance, a company might boast about having millions of downloads for its app. However, if the majority of those users don’t actively use the app, or if they uninstall it soon after downloading, that high download number becomes meaningless. It’s a shiny facade that, once scratched, reveals a less impressive reality.

Using vanity metrics to impress or mislead investors is not just unethical, but it’s also short-sighted. Yes, you might win over some people in the short term, but what happens when they start expecting results based on those metrics? When the reality can’t match the illusion you’ve created, it can lead to lost trust, damaged relationships, and potential legal issues.

If we truly want to be data-driven, we need to be transparent, honest, and comprehensive in our use of data. We need to translate the entire product into actionable input and output KPIs (Key Performance Indicators).

These KPIs should provide insights into all aspects of the business, from product usage and customer satisfaction to financial performance and competitive positioning. They should enable us to understand our weaknesses, leverage our strengths, and make informed decisions that drive sustainable growth.

Remember, being data-driven isn’t about presenting the prettiest picture; it’s about understanding the true state of things, warts and all. Only then can we identify the best path forward, creating real value for customers, employees, and investors alike.

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The power of qualitative data

So, should we abandon measuring metrics and KPIs altogether? Absolutely not. The key is to combine quantitative data with qualitative insights. Qualitative data, like user interviews, surveys, and direct feedback, provides the context that numbers often lack. It brings the human element back into the equation, helping us understand not just the “what,” but the “why” behind the data.

A great example of this is how the streaming service Spotify continually evolves its algorithm. Rather than relying purely on play counts and skips, they actively seek user feedback and use this qualitative data to improve their recommendations, creating a more personalized experience for their listeners.

No amount of data or advanced analytics can fully replace the invaluable insights gained from one-on-one conversations with customers. As a matter of fact, some of the most critical business decisions are informed by these direct interactions. It’s only through these conversations that we can understand our customers’ experiences, needs, pain points, and their desires.

Many organizations have fallen into the trap of believing that customer behavior can be fully predicted or understood through data alone. However, data can only tell us what is happening, not why it’s happening.

Numbers give us facts, but not context or emotion. They can’t explain why a customer suddenly stopped using a product or why they prefer one feature over another. They can’t tell us about the small frustrations that don’t lead to a customer service call but do lead to diminished satisfaction over time.

On the other hand, a simple conversation with a customer can reveal insights no amount of quantitative data could. Speaking directly to customers allows us to ask why, to dig deeper, and to learn the motivations and emotions behind their actions. It allows us to empathize with their experiences and to see our products and services from their perspective.

Moreover, these interactions can lead to moments of “customer delight” – those unexpected positive experiences that can turn a satisfied customer into a loyal advocate for your brand. Whether it’s resolving a problem swiftly and effectively, going above and beyond to meet a customer’s needs, or simply showing genuine appreciation for their business, these moments can’t be quantified, but they can be incredibly powerful.


Rather than being data-driven, we should aim to be data-informed and value-driven.

Being data-driven is not inherently wrong, but it becomes a problem when data is seen as the ultimate truth without considering the nuances and complexities of real-life situations. Rather than being data-driven, we should aim to be data-informed and value-driven, where data is one of many tools in our decision-making toolkit, complemented by qualitative insights, domain expertise, and an understanding of the broader context.

So the next time you hear about the wonders of being data-driven, remember that data is only as good as the insights derived from it. And these insights can only be truly valuable when they’re informed by a deep understanding of the human experiences, needs, and contexts that lie beyond the numbers.

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