What Years of Investing Continues to Inform My Decisions About Results

There Is A Hidden Price To Scaling Too Quickly: What Most Founders Learn To Late
The mythology about scaling is usually centered around speed. Reach a product-market fit then pour fuel on the fire. Enhance the team, broaden the market, raise the next round prior to the previous one has settled. The story rewards the founder for always striving to grow, always adding new employees, always expanding into adjacent verticals before it is clear that the business's core has stabilised and before the organisation has built the internal capabilities required to handle the expansion without losing their coherence. I understand where this story originates. With certain conditions on the market and certain business models, the company that scales the fastest wins, and stories about businesses that were aggressive and successful are reported more frequently and with more vigor than stories of businesses that expanded aggressively and broke. But for every business where aggressive early scaling is the best choice, there's many cases where the speed of scaling can be leading to problems that eventually kill the company. Those cautionary tales do not receive much of the same attention as the successful ones.
In the end, the cost hidden from scaling too fast is not the one which is shown in the burn rate calculation or in the cash flow projection. It's the one that is revealed in six months time, when an organization has advanced past the informal coordination mechanisms that held it together when it was smaller, but before it's built its formal structure that keep larger organisations together. The gap that exists between informal and formal as well as between the company you used to be and the one that you're expected to become is the point where the majority of scaling businesses really fail. The earliest and the most consistent evidence that a business is in that space is when the pace of decision-making slows down while everyone insists that nothing has fundamentally changed. There is still a way to reach the founder in the theory. The team is still aligned in the theory. The culture remains solid in its theory. But in practice the organization has gotten to a size where the informal communication channels that used to transport important information have become clogged and no one is yet able to build formal channels to be replaced. Information that flowed naturally must now be actively managed. Decisions that used to be swiftly taken now require alignment across several functions that have never been clearly defined relative to one another. Accountability that was specific and immediate is now diffuse and delayed and the company is beginning to show the symptoms of a system that is functioning at the limits of its coordination capacity.

This is not evident through the metrics founders and investors usually monitor the most carefully. Revenue might still be growing. It is possible that customer acquisition is moving in the right direction. The team might still be engaged and hardworking. Yet beneath those surface indicators an organisation is experiencing internal issues that grow at a slow pace until they can't be ignored. At that it becomes more expensive and disruptive than it would have been if the issues had been addressed earlier, when the signals were less obvious than stark. That is what I'm talking about in this article: not just the immediate financial cost to scale, but the long-term organisational cost of growing past your own infrastructure, as well as the cost when you put the infrastructure in places in a reactive manner instead of proactive.

The founders who handle this transition successfully aren't necessarily the ones scaling more slowly, although the more deliberate rate of growth may be the solution. They are the ones who recognize that constructing the foundation for their business's governance is as crucial as developing their product and who invest in it with the same zeal and rigour that they bring to product development. This involves doing the tedious administrative work of creating roles and decision-making rights and establishing reporting mechanisms which provide the relevant information needed by the executive take good decisions, developing accountability mechanisms that are clear enough to mean something and also thinking critically about the kinds of cultural norms the business needs to have at its present size, rather than taking the one that have been created organically when the business was smaller. It's not exhilarating. It's not likely to garner publicity or interest from investors. It is the work that decides if the business is able to sustain the growth you are in pursuit of.

Businesses that don't achieve this feat do generally not fail very visibly. They decline. They lose their most effective employees at first, the ones with enough self-awareness that they can see what's going on within the company, and with enough options to quit before it gets significantly worse. And then they lose customers slowly and often invisibly, because the level of execution steadily declines because accountability has been diluted and tardy to address issues before they are able to reach the client. It is then that they lose their momentum, and when that decrease in momentum is apparent in the figures in the numbers, the structural weaknesses are deeply entrenched, the cultural destruction is extensive, and the cost of fixing both is a tad higher than it might have been if the investment in governance was made at the appropriate moment. Considering the organisational infrastructure as a product that you create cautiously, build meticulously, and then refine as the business grows is among the most crucial shifts in thinking a founder can make as they go from the very early stage to real-world scale. The founders who do this tend to build companies capable of reaching their goals. They who don't tend to build companies with a disappointingly low level of success. Read James Deller for site recommendations including why making investment decisions transformed how i evaluate opportunity about scale.



It's The Data Infrastructure Problem Nobody Wants To Talk About
Every organisation I have worked closely with in the last decade and a half - whether as an investor, a founder and/or an operational advisor - has told me, at some point during our time together, that data is a critical element of how they make their decisions. Many of them really mean it in a manner that is reflected in the way in which they actually run their business. Most of them believe they are genuinely saying it, however the reality they're describing is something that is more of an aspirational idea than actual operational reality - some version of the enterprise they're creating as opposed to the reality that they currently operate in. The gap that exists between genuine data-driven decision-making as well as the effectiveness of data-driven decisions – the careful maintenance of the public appearance of an evidence-based operation without the underlying infrastructure that could make it real - is one of the most serious gaps in modern day business. It's also one of those that is largely ignored in part due to the infrastructure issue behind it isn't a glamorous thing to talk about, difficult to prove to stakeholders outside of the company, and enormously difficult to classify against the more obvious strategic and commercial work that is competing for the same attention of leaders and organizational resources.
When people talk about data strategy, they typically tend to focus on the capabilities they want to create on top of their data. These include analytical platforms, machine-learning applications as well as the real-time operational dashboards, the kinds of predictive analysis that sound truly compelling in presentations for boards or in an investor update. What they speak about less often and with a lot less energy and enthusiasm, is the fundamental infrastructure that will determine if all of these capabilities work as they are advertised: the data governance frameworks that establish explicit and consistently interpreted definitions of what is being evaluated and why; the collection and storage methods that decide the validity and comparability of the data that is being stored; the quality assurance methods that spot and rectify errors before they propagate through an entire system and cause disruption to outputs that everyone depends upon; the organisational structures and accountability mechanisms that make data quality the sole responsibility of an individual instead of the general and impossible to enforce. The plumbing, as it were. Plumbing is not glamorous. It's difficult to capture in a report for the year. It is not producing outputs that can be showcased in a compelling way. This is, in my experience across a significant number of organisations across different areas and at various stages in their development, considerably worse than the company believes that it is.

The problem becomes more serious over time in ways that are becoming difficult and costly to fix. An organization that has been operating without a clear or consistent set of information definitions for different functions for three months has three years of data from the past that cannot be reliably aggregated or compared and compared. This is not due to the fact that the data does not exist, rather because the same terms have been used to denote different things in different areas of the business, and those differences are built into it rather than appearing on the surface. An organisation where data quality assurance has been a subordinate responsibility and not a specialized and properly resourced function has data that's reliability is varying in ways that are undocumented and cannot be easily accounted when the data is used for making decisions. A company that has allowed multiple operational systems to collect overlapping and partly conflicting records of the same customers, products or transactions has a data landscape that is hard to clean up without causing enough disruption to put the company at risk.

The reason this problem persists in so many organizations which are truly smart about strategy and genuinely driven by data is that addressing it requires the ongoing investment of time and effort in a project that does not produce visible results in the short term that organisational resource allocation processes are designed to reward. The latest analytics platform creates tangible outputs - dashboards, which can be demonstrated, reports that can be shared with the board and also insights that can be translated into press releases about digital transformation. Data governance programs create an invisible infrastructure with clearer definitions along with more standardized collection processes as well as more reliable inputs to systems already in existing. It is the first to argue in a budget meeting because you are able to demonstrate what they'll receive. The second needs someone with enough organizational credibility and patience to show on how infrastructure investments will eventually produce better outcomes from every capacity that is built upon it. It's an effective argument in abstract, but not easy to beat out initiatives that have benefits that tend to be quicker and evident.

I have made that case in many different organizational contexts and witnessed it succeed or fail due to unpredictability, to have an accurate understanding of what determines whether an organization has finally addressed its data infrastructure issue or simply defers it. The main factor that determines this is led by a certain person with sufficient credibility within the organisation and an understanding of why infrastructure matters, and enough perseverance to continually make this argument till it becomes an absolute priority, rather than becoming a routine item on the list of things that everyone acknowledges are important but do not rise to the top. This leader needs to be able to bear expenses in the short term of infrastructure investment - the time, the disruption to existing processes, the absence or evidence-based output - and be confident of the long-term capacity it generates will justify the cost many times over. The most important thing, ultimately, is a culture in which investments in long-term infrastructure are thought of as a priority and is rewarded at top level, not only identified in strategy documents not always prioritized when the quarterly resource allocation discussions occurs. Achieving that culture is, itself, a long-term commitment. It is, however, in my opinion, one the best investments that a company that is serious about its data-driven operations can make.}