Why Startups Should Be Data-Driven
Why Startups Should Be Data-Driven

Why Startups Should Be Data-Driven

Oliver Taylor
Oliver Taylor

February 23, 2024

Data is a double-edged sword in the arsenal of a startup. Wielding it wisely offers benefits, but, as with many weapons, mishandling it is dangerous. In this post, I’ll explain why this sword is worthy of battle and how it’s best swung with the intuition of its wielder.

Global data volume is increasing exponentially – currently, there are 44 zettabytes of data in the digital world. This is the equivalent of the global population writing and storing ~5 million average-length books each. Data volume among startups is increasing, too. But sadly much of that data is under-utilised. This is mainly because early-stage companies are heavily resource-constrained and struggle to extract signals from noise.

What does data-driven actually mean?

Before I discuss practical benefits, let’s outline why you should leverage your data at a high level… From a young age, we’re taught a worldview of cemented, foundational principles. But Clayton Christensen taught us that, at least in business, these principles are malleable and subject to change. Preferences, industries, and the world change. Hence, there is no truth in business, rather there is knowledge, which helps you predict the outcomes of your decisions. If you can better predict the outcomes of your decisions, then you can better run your business. So, if data gives knowledge, you should strive to leverage it.

But what does it actually mean to be data-driven and how does it differ from being data-informed? These terms are often used interchangeably, yet they have different meanings. Being data-driven is to make decisions based solely on data, even when other factors like your experience and intuition contradict it. Two main assumptions underlie this approach. First, data provides a complete understanding of a situation or problem. Second, reality lacks nuance.

In contrast, being data-informed is to blend human intuition with data to make aware- and experience-based decisions. In this approach, data is vital, but it’s one of several inputs considered in decision-making. Other inputs include personal experience, strategy, and culture, alongside data. The notion is that data augments, not replaces. This allows for the nuances of reality to seep into decision-making.

There are benefits to opting for a more data-driven approach. First, there’s the obvious absence of human bias. By relying solely on data, decisions are based more on objectivity rather than subjective opinions. I say ‘more’ as the objectivity of data is a myth – the same data set can support multiple perspectives. Second, less responsibility is placed on leadership; the onus is placed on data rather than their guidance. Also, there is greater company-wide transparency over why certain decisions were made.

But the main problem with data-driven methods is that they disregard the big picture and force blind trust in data. They forget reality is nuanced and the real world embodies a spectrum of grey areas: it’s not binary. Plus, many companies are limited by the volume, quality, and relevance of their data. This means it can be challenging to rely entirely on data to provide answers to complex, multi-faceted problems. Arguably, being data-informed is the best of both worlds. But, so what?

Benefits of the sword

Back to the sword. Here are some practical reasons why it’s worthy of battle. They ultimately stem from the improved predictive power I mentioned above.

Tighter feedback loops. With shorter experiment cycles, you can conduct more experiments and learn from them faster. Quick validation or refutation of hypotheses in startups is gold. If feedback loops are shorter, there is less friction involved in experimenting, so your employees will be naturally more inclined to test. Hence, you can better cultivate a culture of testing and experimentation.

Lower decision overturn rate. Reversing decisions can waste resources that startups can’t afford. Decreasing your decision overturn rate will bank you time, cash, and morale. These saved resources can be directed towards critical business areas to fuel growth.

Better prioritisation. Every decision about where to allocate resources involves an opportunity cost, which is the cost of foregoing the next best alternative. When you decide to double down on a growth opportunity identified through data, you inherently decide not to allocate those resources elsewhere. Being data-informed helps ensure shrewd trade-offs are made by choosing opportunities that offer the highest ROI. This, of course, assumes your data capabilities genuinely help you spot these opportunities.

Uncover issues and opportunities early. Spot issues promptly, before they germinate and compound into colossal Redwoods. Similarly, if you can discover growth opportunities early, you can double down on them. Again, these allow you to save precious, scarce resources which can be spent on better solving the core problem at hand. It also helps unlock a culture of proactivity over reactivity.

As you’ve probably noted, these lower-level benefits build towards an overarching one: increased speed. This sounds appealing at face value, but what are the consequences of getting from point A to B faster? Well, the financier says, “Obviously, you’re more likely to survive. But, as you need fewer resources to find a scalable, repeatable business model, you may need less funding and can decrease dilution.” The strategist says, “If you lack a defensive moat, speed is a critical force that helps you destroy your competition. You can capitalise on first-mover advantage, capture up-tapped markets, and erect entry barriers.” The customers say, “Pace increases proximity to us. Agile companies respond quicker to our feedback and iterate faster on their products to better meet our needs.” Finally, your investor says, “You make Bolt look like he’s running backwards. We’d love to pro-rate.”

Intangible benefits exist too. One problem that plagues many startup leaders is that they feel like they’re flying blind. Introducing more data into day-to-day operations can increase confidence in decision-making. It builds trust and credibility – both internally and externally. When employees and investors see that decisions are supported by data, decision-makers are likely to be perceived as more credible and reliable. Plus, data-informed decisions are more defensible and transparent, which can build trust among team members and external stakeholders. Lastly, if employees are data-informed themselves, they can see their work’s impact better. This can enhance employee engagement and foster a sense of value among your team.

Why most companies aren’t data-driven

There’s a widespread sense that operators should employ their data. And many have serious intentions but hit speed bumps along the way.

The first bump: they lack resources to build tools that help to effectively analyse their siloed data. It takes hiring a new data person and several months to build a solid analytics infrastructure. This involves setting up event tracking, storage, and processing in a data warehouse. Then you need a bunch of mechanisms to ensure data availability and integrity. Many early-stage companies simply can’t afford this.

The second bump: even if they can, they struggle to extract meaningful insights from their data. As Cedric Chin posits elegantly, the problem is wiggly chart lines. Ultimately, it’s hard to make conclusions from real-world operational data due to routine variation – the random-looking chart wiggles that are a natural part of business. Because of this variation, “[a] large change is not necessarily worth investigating, and a small change is not necessarily benign”. Chin explains that this results in operators not knowing when they’ve successfully improved and wasting time chasing noise. Ultimately, they lack the knowledge to better predict the outcomes of their decisions.

A route to change

These speed bumps are the overarching problems we look to solve at Calliper. We believe everyone should be able to leverage data in their decision-making, no matter their technical skills. They should be able to reap the benefits of being data-informed. Simply put, the product is a plug-and-play business intelligence solution for SaaS operators. It connects to your SaaS data sources like Stripe, Hubspot, and Mixpanel out of the box with no coding. Setup takes minutes. Calliper is pre-built around the most important metrics for each department, so you can focus on metrics that help you grow. Unlike other BI tools, it provides a feed of automated insights based on your data that take into account routine variation. If you’re interested, check it out here.

P.S. Have a read of this article on leadership metrics and dashboards 💞

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