The mythology about scaling is usually centered around speed. Get to product-market fit, then add fuel to the fire. Make the team bigger, expand the market, and raise the next round before the previous round has settled. The mythology rewards the founder who is constantly pressing forward, always adding heads, always expanding into additional verticals even before it is clear that the business's core has genuinely stabilized and the company has built the internal capabilities to control that expansion without losing the coherence. I understand where this story originates. When certain conditions prevail in markets and business models, the first company to scale most effectively wins, and the stories of firms that grew aggressively and succeeded are more often told and are more compelling than stories of those who grew in a hurry and fell. However, for every company where aggressive early scaling is a good approach, there's some instances when the speed at which scaling occurs becomes one of the major causes of issues that ultimately end up killing the business, and those negative stories aren't getting almost as much attention as those of the successful cases.
Costs hidden by growing too fast isn't the one that is reflected in the calculation of the burn rate or the cash flow forecast. It's the one that is revealed one year later, after the company has surpassed the informal coordination mechanisms which held it together when it was small, and even before it has created the formal structures that keep larger organizations together. This gap between formal and informal in between the one which you are and the company you're supposed to become is where the majority of companies that are growing often break. The first and most obvious sign that a company is getting into that gap is that the pace of decision making slows and everyone is convinced that nothing fundamentally has changed. The founder's voice is still available in the theory. The team continues to be aligned in the theory. The culture is still solid in theory. However, in reality, the organisation has grown in size to the point that informal communication channels that used to transfer crucial information are now blocked, but no one has yet constructed the formal channels that need to be replaced. Information that used to flow naturally has to be continuously monitored. Decisions that used to be fast now require coordination across numerous functions that have not been defined clearly in relation to each other. Accountability that was private and immediate now appears spread out and delayed and the company has begun to show the symptoms of a system that is functioning at the limits of its coordination capabilities.
It's not visible in the indicators that founders and investors usually monitor most carefully. Revenue may still be growing. It is possible that customer acquisition is heading in the right direction. It is possible that the team is eager and enthusiastic. But beneath those superficial indicators there are structural issues that will grow quietly until they cannot be ignored - at which moment fixing them becomes radically more expensive and disruptive than it would have been if they'd been addressed prior to the time when the indicators aren't as apparent. Hidden costs are what I am talking about: not the immediate financial cost to scale, but the longer-term costs of extending your organisation over your own infrastructure and the increasing cost of putting the infrastructure in position in a reactive rather than proactive manner.
The founders who master the transition with ease aren't necessarily the ones that grow slower, though an intentional pace of growth might be the solution. They understand that building the structures for managing their business is just as important as developing their product and who invest in it with the same zeal and discipline that they bring to product development. It means performing the tedious administrative work of the definition of roles and decision rights clearly, creating reporting frameworks that provide the data leadership needs in order to make informed decisions, creating accountability mechanisms that are precise enough to be useful and also thinking critically about what kind and type of cultural norms your company requires for its level of growth instead of basing it on what evolved naturally when it was smaller. All of this isn't an exciting task. It's not likely to garner attention from the press or generate investor excitement. However, it is the actual work that determines if the firm that you're creating can sustain the growth you are looking for.
Companies that do not go through this transition with success do typically not fail in a dramatic way and easily. They fade. They lose their most effective employees first - those who have enough self-awareness that they can see what's going on inside an organization and the options to leave before the situation becomes dramatically worse. Then, they lose customers slowly and often invisibly, because the level of execution in a quiet way is diminished because accountability been too ambiguous and delayed to catch problems prior to them reaching the customer. Then they lose momentum, and by the time change in momentum is seen in the numbers The structural issues are very deep in the system, the cultural consequences are severe and the cost to fix each is far higher than it would've been if the investment in governance had been made at appropriate moment. Thinking of organisational infrastructure as a product that you create meticulously, construct carefully, and iterate on as the business grows is among the most significant shifts in mindset an entrepreneur can undergo as they move from the early stage to the real. Those who are able to make this shift tend to build companies that realize their potential. The ones who fail tend to create businesses which are not even close enough. Follow James Deller for blog examples including how data-driven thinking reinforced operational discipline about building well.
It's The Data Infrastructure Problem Nobody Wants To Talk About
Every single organization I've collaborated closely with over the past one and a half years - whether as an investor, a founder or an operational consultant I have been told, at some point in our interactions, that information is central to making decisions. A few of them truly believe this in a way that is apparent in the way the organisation actually operates. Most believe they're really saying that, but the concept they're proposing is something that is more of an aspirational idea than an actuality that exists in the present - an image of the organization they're working towards, in contrast to the reality that they currently operate in. The gap between truly data-driven decision-making as well as the effectiveness of data-driven decisions - maintaining the outward appearance of evidence-based processes without the infrastructure that would allow it to become true - is one of the most crucial gaps that exist in modern-day business. It's also among the most persistently underaddressed ones due to the infrastructure issue that causes it is genuinely unglamorous to talk about, hard to show external stakeholders, and enormously difficult to determine the best way to address it in comparison to the more prominent commercial and strategic activities that demand the same leadership attention and organizational resources.
If companies are discussing data strategy, they tend to talk about the capabilities they want to create on top of their data - the software for analytics and machine-learning applications operating dashboards in real time and the types of predictive insight that sound genuinely compelling in a board presentation or an update to investors. What they talk about less frequently and with a lot less energy and enthusiasm, is their foundational infrastructure that will determine if all the capabilities will work as promised: the data governance frameworks that define clear and consistently applied definitions of what is being measured and why; the collection and storage methods that decide the validity and comparability of the data being captured; the quality checks that find and fix errors before they get propagated throughout your system and destroy the results that everyone is counting on; the structure of the organization and accountability processes that make data quality someone's explicit and ongoing responsibility instead of relying on everyone's vague and unenforceable goals. The plumbing, also known as. The plumbing is unglamorous. It's not an easy thing to photograph for an annual report. It is not producing outputs that can be presented in an engaging presentation. And, in my experiences across a wide amount of organizations across different areas and at various stages of development, far worse than the organisation believes it is.
The issue becomes worse by becoming difficult and costly to rectify. An organization which has operated without a clear or consistent set of terminology for data across different functions for the past three years has three years of data from the past that cannot be safely compared, or aggregated - not because the data isn't there, but because the same terminology has been used to denote different items in different areas of the enterprise, and those differences are built into the data itself rather than appearing on the surface. A company whose data quality assurance has been the responsibility of a minor responsibility instead of a dedicated and properly resourced function has data whose integrity varies in ways that are not documented consistently and cannot be accurately accounted when using the data in making decision. A company that has allowed multiple operational software systems to accumulate overlaps and partially conflicting information about the same products, customers or transactions may have created a landscape of data that is genuinely difficult to remediate without significant disruption to operations to pose a risk for the organization itself.
The reason that this problem continues to exist throughout a variety of companies which are truly smart about strategy and truly determined to implement a data-driven strategy is because solving it requires constant investment in work that produces no visible immediate returns like those that processes for resource allocation in organisations are designed to reward. A new analytics platform produces visible outputs - dashboards that can be displayed or reports that could be shared to the board, information that can be translated into press releases about digital transformation. A data governance program produces an invisible infrastructure with clearer definitions with more consistent collection procedures as well as more reliable inputs to existing systems already in already in place. The first one is easy to justify in a budgeting conversation because you are able to demonstrate what they will gain. Second, you need someone who has enough credibility in the organisation and patience to present the argument of how the capital investment is going to, over time, generate better results from each technology that is built on top it. This is a convincing argument in abstract, but it is difficult to compete with initiatives that's benefits have a greater impact and are evident.
I've presented that argument in various organizational contexts and watched it work or fail for unpredictability, to have a fairly clear view of what will determine if the company finally solves its data infrastructure issue and if it will continue to delay the solution. The main difference is one's leader - a particular person who has enough credibility in the organization having a genuine comprehension of why the infrastructure is essential, and enough determination to carry on making this argument till it is an absolute priority, rather than just a repeated item on a list of things everyone is in agreement about but do not attain the level of importance. The leader must accept expenses in the short term of infrastructure investment - the delay or disruption to existing processes, or the absence or evidence-based output - with the certainty that the long-term capability it builds will justify the investment several times more. What is required, ultimately is a culture where the long-term investments in infrastructure are respected and acknowledged at the executive level, not simply defined in documents describing strategy and regularly discarded during the quarterly discussion on resource allocation takes place. Achieving that culture is, itself, a long-term commitment. However, it is, in my opinion, one of the highest return investments that an organisation that is serious about data-driven operation could make.}
