Piyanka Jain
Securing funding for startups has never been a walk in the park, and the current economic volatility has made it even more demanding. According to PitchBook’s 2023 report, capital demand surpasses supply by a daunting 50.5% for early-stage and 67.1% for growth-stage ventures. Startups cannot rely on impressing with just metrics to attract investors. Investors want more; they want to understand the “why” behind a startup’s success, delving into the long-term growth trajectory.
In my capacity as the CEO of Aryng, a data science consulting firm that helps startups and growing enterprises drive success with their data, I recently engaged in an insightful conversation with Cathy Tanimura, VP of Analytics and Data Science at Summit Partners. We discussed the critical role of building and nurturing a robust data culture.
In a time when investors are exercising heightened caution, a strong data culture proves invaluable. When you have a strong data culture, investors are able to gain insight into the “reasons” behind success. They understand how your data team solves problems, optimizes fund allocation, and identifies revenue-driving insights.
A data-focused approach signals a company’s ability to run its business efficiently. As Cathy aptly put it, leaders should “know the drivers of your business, what makes your customers tick, what’s important to your growth, what’s helping or hindering you from growing and then work backward into the data that you need to have the insights about those drivers.”
Cathy went on to add how data culture can help align metrics with goals, manage risks, streamline due diligence, optimize data retrieval, and ensure control and transparency.
Building this culture empowers founders to compellingly articulate their journey to potential investors. It signals a commitment to data-driven decisions over gut instincts, a trait that gives confidence to cautious investors.
As I absorbed Cathy’s perspective, I felt inspired to share my experience-driven strategies on how startups and growing companies can build a robust and adaptive data culture that can help them kickstart this initiative.
Empower data-driven leadership
While the strategies I’m about to share aren’t necessarily a step-by-step sequence, one thing is clear: Data culture starts at the very top.
Leadership is the engine that drives a strong data culture in an organization. The core of establishing a thriving data culture in these settings hinges on leaders making decisions grounded in data-driven insights.
In my experience, many startups kick off with decision-making based on hunches. However, as they evolve and grow, depending solely on hunches becomes a limitation. It’s crucial to differentiate between hunches and structured hypotheses. Hunches often rely on intuition without concrete data, whereas hypotheses involve crafting specific statements based on existing data and logic. This transition can set them on a path of rigorous experimentation and data analysis, resulting in more informed, data-driven decisions.
I strongly recommend executives adopt this hypothesis-driven approach. It deepens their understanding of complex cause-and-effect dynamics within their operations, cultivating a culture of data-driven excellence.
Make OKRs the north star for orienting efforts
Let’s talk about objectives and key results (OKRs) — a solid framework that guides businesses in making data-driven decisions. It’s all about accountability, paving the way for a results-focused culture rooted in clear-cut hypotheses. Every decision that leaders make should carry a precise expectation of its business impact, fitting into broader company goals.
I also suggest embracing net-zero budgeting. This strategy urges executives to dig deep into expenses, ensuring resources are allocated efficiently. By implementing OKRs and net-zero budgeting systematically, any startup can navigate data-driven decision-making while optimizing resource utilization, ultimately driving growth and success.
Hire a head of data and centralize your data function
To build a strong data culture, startups need to see their data team as a revenue center rather than a cost center. This means giving the head of data a seat at the table, reporting directly to the CEO or a CXO with significant P&L experience.
By having an internal champion who actively drives the data agenda, a growing enterprise can ensure that good data science is always an active part of their growth strategy. Investors agree that data teams are an investment, not a cost to be deferred.
Furthermore, having a head of data and a data team will centralize data function. Instead of having multiple data processes scattered across departments, having a head of data brings them into a more coordinated system. Whether it’s creating the data strategy that will drive decision-making, maintaining data integrity and security, providing insights through intelligent analytics, or managing a scalable data infrastructure, the head of data is indispensable for any startup’s data culture.
Orient data efforts to top business use cases
Unlike larger corporations, startups and growth companies don’t have the luxury of unlimited resources to drive multiple initiatives. Instead, they must zero in on the key performance indicators (KPIs) that matter most for business success.
My proposed solution is the KPI DuPont exercise, a visual method that helps businesses better understand their KPIs and the metrics that drive them. The process is most effective when the entire executive leadership team (ELT) is involved, and with support from the head of data, it typically takes four to six weeks to complete. Once the KPI DuPont is finished, the leadership can now identify the initiatives (use cases) that have the most impact on driver metrics and, consequently, on their KPIs. This alignment between use cases and business outcomes ensures that the results are directly linked to the top driver metrics from the KPI DuPont.
The KPI DuPont exercise creates a visual tree that links each KPI to its immediate driver metrics, allowing for up to five to 10 levels of metrics. This structure often mirrors the actual organization’s hierarchy. For example, if a business is divided into geographic regions and further segmented into distinct product lines, the KPI DuPont will accurately reflect this hierarchical structure.
This exercise achieves more than just aligning the leadership team’s approach to measuring success; it reveals significant opportunities for swiftly improving KPIs. For instance, if the KPI is revenue growth and a metric situated four levels down pertains to customer retention, which currently stands at, say, 60% below industry standards. Then enhancing it by 5% could result in a substantial 3% increase in revenue growth, which can be equivalent to a significant incremental revenue (please note that these figures are for example purposes only). These opportunities, unveiled during the exercise, can be assessed, and the top three to five can be prioritized, leading to a substantial boost in revenue growth.
By ensuring that their initiatives align with the most impactful KPIs, startup and growth companies can also easily demonstrate to investors how their endeavors directly contribute to the bottom line.
Invest in a single source of truth and KPI dashboards
As startups experience rapid growth, it comes as a mixed blessing, bringing a bag of opportunities and a bundle of challenges. On the one hand, it opens doors to enhanced insights, increased efficiency, and better decision-making. On the other hand, it ushers in complexities such as data volume, organization, and accessibility.
Maintaining a clear focus becomes critical as businesses expand. Every startup at some point in its journey grapples with a common challenge: data inconsistencies, with vital metrics like subscriber counts and revenue often refusing to play nicely together. It’s important to remember that there’s no such thing as “perfect data.” So, what’s the solution? Identifying the top 300 to 500 crucial metrics for the business is the way to go. But it’s not enough just to identify them; it’s about ensuring everyone in the organization has access to accurate versions of these metrics, and that’s where the single source of truth (SSOT) driven by KPI dashboards comes into play.
Another essential component in this strategy is the use of KPI dashboards, which offer a visual representation of KPI DuPont, making quick and informed decision-making possible. For instance, you can see revenue growth at 15% and all the relevant driver metrics. The key is to make these dashboards user friendly and easily accessible so that everyone on the team gets into the habit of using them.
While KPI dashboards serve the CXOs, supporting metrics are necessary for detailed business insights, extending down to front-line roles. These 300 to 500 metrics should also be consolidated into an SSOT, allowing access to data, information, and insights from a single repository. This avoids data silos and keeps everyone on the same page when it comes to KPIs.
In addition, KPI dashboards, supported by SSOT, simplify due diligence processes, enabling accurate presentations of the business’s status to potential investors.
How a data culture overhaul transformed a payment orchestration startup
To illustrate how these strategies come to life in a real-world scenario, let me share a case study featuring a payment orchestration startup.
This startup was grappling with issues related to conversion rates, pricing, and cost optimization. Furthermore, they faced data inconsistencies, which eroded trust in their data and hindered their progress.
To address these challenges, we started by building a KPI DuPont with their leadership team, delving down to level 6 to identify key metrics and their drivers. We then aligned their OKRs with the relevant metrics identified in the KPI DuPont.
This eye-opening process shed light on the varying perspectives of the company’s leaders regarding what’s essential to drive the business forward. This exercise served as the foundation for a unified vision and guidelines, represented by the KPI DuPont and, eventually, the KPI dashboard.
At the same time, we identified top business use cases with the potential to significantly impact drivers of key KPIs. In parallel, we assessed the tech infrastructure to identify gaps in the SSOT and technical skills. Using these business use cases as our north star, we charted a roadmap to drive tangible business results.
Our key initiatives included:
- Implementing a democratized SSOT with over 300 key metrics.
- Building KPI dashboards to monitor performance.
- Empowering analytics capabilities to gain valuable insights.
Undergoing this transformation in just nine months, the startup achieved an $8.4 million revenue uplift and a $1.7 million reduction in losses. The CTO hailed this process as a “game-changer,” underscoring the monetary value attached to nurturing a data culture. It empowered teams to solve problems, optimize fund allocation, and identify revenue-driving insights, and this is the kind of data-driven approach that investors want to see.
Data culture isn’t just a trendy buzzword; it’s a strategic necessity that forms the bedrock of smart decision-making, rooted in facts and the results of experimentation. It provides startups and growth companies with the confidence they need for a successful journey.
The strategies I shared are integral to this process. Developing a robust data culture doesn’t happen overnight; it requires sustained effort and commitment, particularly from leadership. When implemented effectively, a strong data culture isn’t some abstract notion — it’s quantifiable and directly influences profitability.
It’s crucial for startups and growth companies to shift their perspective on data. Unlike larger enterprises, they operate under different constraints. They need to be resourceful and discerning to extend their runway to the next funding round. While fundraising is a top priority for startups, nurturing a robust data culture can significantly contribute to their overall success.
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