The mythology around scaling is often centered on speed. Once you have a good fit for your product, then put fuel on the fire. Build the team, expand your market, then raise the next round before the previous round has settled. The mythology rewards the founder who is constantly pushing forward, constantly adding the number of employees, always expanding into other verticals before even the primary business is truly stabilised and before the organisation has built the internal capabilities needed to be able to manage the expansion without losing its coherence. I can see where this mythology comes from. Under certain conditions in the market and business models, the company that scales fastest does genuinely win, and the stories of firms who grew rapidly and achieved success are reported more frequently and more vividly than the stories about companies that scaled fast and failed. However, for every enterprise where aggressive rapid scaling is the most effective option, there's several cases where the speed of scaling becomes one of the major causes of issues that ultimately kill the company, and those negative stories aren't getting nearly the same attention as those that have been successful.
It is important to recognize that the hidden costs associated with scaling too fast isn't the one that appears in the calculation of the burn rate or the cash flow forecast. It's the one that comes out 6 months later, once the company has gone beyond the coordination mechanisms of informal nature that held it together as it was a small one, but before it's built its formal structure that hold larger organizations together. That gap - between informal and formal separation between the company you've been and the organization you want to be - is where the majority of growing companies are able to fail. The most evident indicator that a business is reaching that apex is when it slows down its decision-making even though everyone claims it hasn't changed in the fundamentals. The founder's presence is still present in the theory. The team continues to be aligned in theory. The culture is strong in the theory. However, in actual practice the organization has gotten to a point where informal channels used to transfer essential information are blocked and there is no way to create the formal channels that need to be replaced. Information that was flowing effortlessly now must be continuously managed. Decisions that used to be swiftly taken now require alignment across multiple functions that have never been clearly defined in relation to each other. Reliableness that was immediate and personal is now difficult and can take a long time to complete, and the organisation has begun to show the signs of a system that is functioning at the limits of its coordination capacity.
The absence of any evidence is evident in the metrics that investors and founders usually follow the most closely. Revenue might still be growing. It is possible that customer acquisition is going in the right direction. The team may be active and efficient. But underneath those surface indicators the company is exhibiting structural issues that will grow in a quiet manner until they are unable to be ignored - at which the point when fixing them becomes more costly and time-consuming than it would have been if they'd been addressed earlier, when the signs were less obvious than stark. Hidden costs are what I'm talking about it is not a direct financial cost of scale, but the long-term organisational cost of growing past your own infrastructure, and the increasing cost when you put the infrastructure in places in a reactive manner instead of proactive.
The founders who can navigate this transition successfully aren't necessarily those who expand slower, though a more deliberate pace of growth is sometimes part of the solution. They are the ones who know that creating the right management structure of their business is as crucial in the same way as creating the product and invest in it with the same enthusiasm and dedication to the development of their products. It means performing the tedious administrative work of defining roles and decision rights clearly, establishing reporting structures that reveal the data executives require to make good decisions, making accountability mechanisms sufficiently specific to be meaningful and thoughtfully pondering what kinds of norms that the organization needs at its size and not taking the one that took shape naturally when it was smaller. None of this work is stimulating. There is no way to create excitement in the media or inspire investors. But it is the work to determine whether the company that you're establishing can keep the growth you're striving for.
Businesses that don't complete this process successfully do not often fail very easily. They decline. They lose their most effective employees first - the ones who have sufficient self-awareness to be aware of how things are going in the organisation and enough options to leave before it gets substantially worse. And then they lose customers usually in a gradual manner, as the efficiency of execution is deteriorating because accountability has been diluted and tardy to address issues prior to them reaching the customer. In the end, they are losing momentum at the point that change in momentum is seen in the numbers and the structural issues are deeply rooted. The cultural effects are extensive, and the cost to fix both is much higher than it might have been if the investment in governance had been made at appropriate time. In the eyes of an organisational structure as a product, something you develop thoughtfully, build with care, and tweak as your business grows is among the most significant shifts in mindset that a founder could make as they move from the early stages to actual scale. The founders who do this tend to create companies with the potential to succeed. Those who fail tend to create businesses which are not even close enough. View James Deller for blog advice including how running organisations continues to inform my decisions about people.
There's A Data Infrastructure Problem Nobody Wants To Discuss
Every single company I've worked closely with during the last decade and a quarter - whether as an investor, founder or an operational consultant I've been told, at some point in our collaboration, that data plays a major role in the way they make decisions. Some of them have truly believed this in a way that is apparent in the way the organization actually functions. Most believe they are genuinely saying it, however what they're discussing is only an aspirational notion rather than the current reality of operations - it's a model of the one they're working towards, and not the one they currently reside in. The gap between real-time decisions based on data and the efficacy of data-driven decisions – the meticulous maintenance of the public appearance of an evidence-based operation without the underlying infrastructure to make it tangible - is one the most crucial gaps in the modern world of business. It's also among the gaps that remain unaddressed in part due to the infrastructure-related issue that creates it is difficult to discuss, challenging to show external stakeholders and incredibly difficult to prioritize against the more visible strategic and commercial work that competes for the same leadership attention and organizational resources.
When organizations discuss data strategy, they tend to discuss the capabilities they would like to create on top of their data - analytical platforms, machine-learning applications such as real-time operational dashboards and the types of predictive insight that sound genuinely compelling in such a presentation to the board or an update to investors. What they discuss less often and with significantly less energy and enthusiasm, is the fundamental infrastructure that is the determining factor in whether all capacities actually function as claimed: the information governance frameworks that define explicit and consistently interpreted definitions of what is being measured and the reasons for it collecting and storing methodologies that determine the reliability and comparability of the information being recorded; the quality assurance methods that spot and rectify errors before they propagate through your system and destroy the outputs that everyone depends on; and the organisational structures and accountability mechanisms that make data quality the explicit and continuous responsibility of each individual instead of relying on everyone's vague and impossible to enforce. The plumbing, also known as. Plumbing is not glamorous. It's difficult to photograph for an annual report. The outputs it produces are not ones that can be showcased in a convincing way. And, in my experience across a significant number of organisations in different sectors and at different stages of development, significantly worse than the organisation believes it is.
The issue becomes more severe over time and becomes complicated and costly to rectify. An organisation that has been operating with poorly or incoherent terminology for data across different functions for the past three years has three years of data from the past that cannot be effectively compared or aggregated in the sense that the data does not exist, rather because the same term has been used to denote different things in different parts of the organisation. Furthermore, those differences are built into the data rather than being apparent on the surface. A business whose quality assurance was a primary responsibility, and not a dedicated and properly resourced function is one whose data's reliability fluctuates in ways that are undocumented and cannot be easily accounted when the data is used to determine the outcome. A business that has allowed multiple operational processes to accumulate overlapping or partly conflicting records of the same products, customers or transactions has an unresolved data landscape that is hard to clean up without disruptions in operations significant enough to pose a risk for the organization itself.
The reason this issue is present throughout a variety of companies that are genuinely intelligent about strategy, and who are truly focused on data-driven operational excellence is because addressing it requires the ongoing investment of time and effort in a project that does not produce visible results in the short term that allocation of resources processes are designed to reward. A new analytics platform provides visual outputs: dashboards and reports that can be demonstrated as well as reports that are shared with the board and also insights that can be translated into press releases on digital transformation. Data governance programs create invisible infrastructure - cleaner underlying definitions along with more standardized collection processes that are more reliable in integrating into systems that were already in use. This is a simple thing to argue in a budget meeting because you are able to demonstrate the results they can expect. The second requires enough organizational credibility and endurance to prove it will eventually produce better outcomes from every facility built on top of it. It's an appealing argument in the abstract, but hard to win in competition with initiatives that's benefits can be seen immediately and obvious.
I've presented this argument in multiple organizational contexts, and watched it succeed or fail for clear reasons to have the most precise understanding as to what decides whether the company finally solves its data infrastructure challenge or continues delaying it. It is generally an individual leader - an person who has enough credibility in the organization and a clear appreciation of the reason that infrastructure is essential, and enough persistence to keep making this argument till it becomes a genuine priority rather than becoming a routine item on the list of things that all agree on yet never attain the level of importance. A leader must accept all the short-term costs of the infrastructure investment - - the time or disruption to current processes, and the absence of immediate tangible results - knowing that the ability it will create will justify the cost several times over. The most important thing, ultimately is a system of culture where investment in long-term infrastructure investments are appreciated and celebrated at the management level, not just mentioned in strategic documents, but not always prioritized when the quarterly discussion on resource allocation happens. The creation of that culture is, in itself considered a long-term investment. In my opinion, one the best returns that an enterprise which is serious about a data-driven operation can make.}
