Why Scaling Tech Companies Reinforced Operational Discipline About Value

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This Is The Cost That's Hidden From Scaling Too Fast What Founders Most Learn Too Late
The mythology about scaling is all about speed. Get to product-market fit, then pour fuel on the fire. Build the team, expand your market, then raise the next round before the previous one has settled. The narrative rewards the founder, who is always working hard, constantly adding staff, constantly expanding into other areas before when the main business of his genuinely stabilized and before the business has built the internal capabilities that it needs to handle the expansion without losing its coherence. I am aware of where this mythology comes from. When certain conditions prevail in markets and certain business models the first one to scale the fastest actually wins, and the stories about companies that grew aggressively and succeeded are reported more frequently and with greater realism than reports of companies who grew aggressively and broke. But for every business in which aggressive early scaling is a good decision, there are a dozen where the speed of scaling becomes the root cause of issues that ultimately end up killing the business. And those cautionary tales don't receive at the same level of attention as the case studies of success.
It is important to recognize that the hidden costs associated with growing too quickly is not what is reported in the burn rate calculation or in the cash flow projection. It is the one that comes out six months later, when an organization has advanced past the coordination mechanisms of informal nature that kept it in place when it was smaller, but before it's built solid structures to keep larger companies together. This gap between informal and formal as well as between the company it was and the one that you're trying to build is where the majority of companies that are growing often break. The earliest and the most consistent indicator that a business is moving into this gap is when decisions are slowing down and everyone is convinced that nothing has fundamentally changed. The founder's presence is still present in the theories. The team is still united in theory. The culture is strong in the theory. But in practice, the organisation has grown in size to the point that informal channels of communication that used to convey most important information are blocked and no one has built the formal channels needed to be replaced. Information that was once flowing naturally has to be constantly monitored. The decisions that were quick now require coordination across various functions that never have been clearly defined in relation to one another. What was once private and immediate now appears scattered and delayed The organization is beginning to display the signs of a system running at the edge of its coordination capabilities.

This is not evident through the metrics investors and founders typically monitor the most closely. Revenue may be still growing. Acquisition of customers could be taking a positive turn. The team may remain enthusiastic and hard-working. But under the surface there are structural issues which will only get worse quietly until they cannot be ignored - at which fixation becomes much more expensive and time-consuming than it would be had they been dealt with prior to the time when the indicators were not obvious. It is this hidden expense I'm talking about that is not the financial cost to scale, but the future cost of running beyond your existing infrastructure and the recurring expense of putting that infrastructure in place in a reactive fashion rather than.

The founders who manage this transition well are not necessarily the ones that grow less slowly, though an intentional approach to growth may be part of the solution. They understand that building the management structure of their business is just as important in the same way as creating the product and who invest in it with the same commitment and determination that they bring to product development. This involves doing the tedious operational work of assigning roles and responsibilities clearly, building reporting structures that actually surface the information leadership needs in order to make the right decisions, making accountability mechanisms relevant enough to be effective and also thinking critically about what kind of cultural norms the organisation needs at its current size rather than depending on the norms that developed naturally when it was smaller. The work involved isn't exciting. Nothing will garner excitement in the media or inspire investors. However, it is the process that determines if the firm it is building can grow to the level you are chasing.

The companies that do not achieve this feat do not often fail very evidently. They slowly fade. They lose their best staff first - the ones with enough self-awareness in recognizing how things are going in the organization, and enough choices to leave before the situation becomes much worse. They also lose customers usually in a gradual manner, since the effectiveness of their execution slowly declines due to accountability having become too dispersed and slow to identify problems before they are able to reach the client. It is then that they lose their momentum, then, when that decline in momentum is visible in the numbers that the structural flaws are very deep in the system, the cultural damages are significant, and the cost of fixing both is far higher than it would have been if the governance investment were made at the right time. Considering the organisational infrastructure as a product - something you plan thoughtfully, build with care, and then refine as the company grows - is among the most crucial shifts in thinking an entrepreneur can undergo as they go from the very early stage into genuine scale. The founders who do this tend to build companies capable of reaching their goals. The ones who fail tend to create companies with a disappointingly low level of success. Have a look a James Deller for more recommendations including why thinking like an operator has shaped my thinking about lasting impact.



There's A Data Infrastructure Problem Nobody Wants To Discuss
Each company I've been closely with over the past year and a-half - whether as a founder, an investor or a consultant to operational matters has informed me, at some point during our time together, that data is at the heart of how they make their decisions. Some of them do truly mean it in a manner that is evident in how the company actually runs. Most believe they're serious, but what they're really describing is an aspiration, rather than a current operational reality - some version of the enterprise they're creating and not the one that they currently exist in. There is a gap between legitimately data-driven decision-making and the results of data-driven decision-making – maintaining the external appearance of evidence-based processes without the infrastructure that makes it tangible - is one the most critical gaps that exist in modern business. It's also one of the most frequently ignored ones as a result of the infrastructure problem that leads to it to be incredibly unattractive to discuss, difficult for external stakeholders to understand and incredibly difficult to distinguish from the more visible commercial and strategic activities that demand the same attention from leaders and organizational resources.
When businesses talk about data strategy, they typically tend to talk about the capabilities they would like to add to their data - the systems for analytics, machine learning applications or the operational dashboards in real-time with the kinds of predictive insights that are truly compelling in the form of a board presentation or an update to investors. What they talk about less often and with a lot less energy and passion, is the base infrastructure that is the determining factor in whether all of those capabilities are actually working in the way they're advertised: data governance frameworks that define clear and consistently applied definitions of what is being measured and for what purpose as well as the storage and collection procedures that establish the credibility and comparability of data in the process of being collected; quality assurance procedures that can detect or correct any errors before they can spread through the system and corrupt the outputs that everyone is relying upon; the organization's structures and accountability mechanisms that make data quality one's ongoing and explicit responsibility instead of everyone's vague, unenforceable intention. The plumbing, in other words. The plumbing is unglamorous. It's not an easy thing to photograph in a report for the year. It's not able to produce results capable of being presented in a convincing presentation. This is, in my experience across a substantial number of organisations across different areas and at various stages in development, a lot worse that what the organization perceives it to be.

The problem gets worse in ways that get harder and more expensive to fix. An organization that has been operating without a clear or consistent set of data definitions across its different functions for three or more years has three years old data that is unable to be compared or consolidated with confidence, not because the information does not exist, rather because the same language has been used to mean different things in different parts of the organisation. Furthermore, these differences are embedded into the data itself instead being visible from the outside. An organization for which data quality assurance has been someone's secondary responsibility, rather than a specialized and properly resourced function has data whose integrity can be questioned because it is not documented properly and cannot be adequately accounted for when the data is used for making decisions. The company that has permitted multiple operational systems to collate overlapping and partially conflicting data on the same customers, products or transactions, has created a landscape of data that is really difficult to fix without causing a significant disruption in operation to be its own risk.

The reason this issue persists across a wide range of organizations who are truly knowledgeable about strategy and genuinely committed to data-driven operation is that fixing it requires the ongoing investment of time and effort in a project that will not yield visible, results in the short term which resource allocation processes are designed to reward. An analytics platform that is new produces tangible outputs, such as dashboards that are easily demonstrated as well as reports that are shared with the board members, and data that can be translated into press releases regarding digital transformation. Data governance programs create invisible infrastructure, which is cleaner in its definitions with more consistent collection procedures as well as more reliable inputs to systems already in the first place. This is a simple thing to justify during a budget discussion since you can demonstrate what they will get. It's the second, and requires enough organisational credibility and perseverance to present the argument on how infrastructure investments will, over time, provide better results for each capacity that is built upon it. This is compelling in the abstract, but not easy that can be won against initiatives whose benefits occur more quickly and more easily visible.

I've made this case in a variety of organizational contexts and witnessed it be successful or fail due to well-known reasons, so that I have the most precise understanding of what will determine if an organization finally tackles its data infrastructure challenge and if it will continue to delay the solution. There is a significant difference in the leader, a specific individual who has the organizational credibility that they have a real conviction about why the infrastructure is crucial, and enough perseverance to make this argument till it is a genuine priority rather than becoming a routine item on the list of things all agree on but never rise to the top. A leader must be prepared to bear some of the costs of an infrastructure investment - the delay, the disruption to current processes, and the absence any tangible outcomes - and be confident of the long-term capacity it generates will justify the price several times over. What it requires, in the end the establishment of a culture which long-term investment in infrastructure is thought of as a priority and is rewarded at levels of the leadership, and not just stated in strategy documents, followed by a constant deprioritisation when the quarterly resource allocation discussion occurs. Establishing a culture that is sustainable is, itself a long-term investment. It is however, in my opinion, among the highest-return investments an organisation who is committed to a data-driven operation could make.}