The mythology surrounding scaling is basically about speed. Reach a product-market fit then put fuel on the fire. Expand the team, grow markets, raise the next round prior to the previous one has settled. The narrative rewards the founder for always pushing forward, constantly adding the number of employees, always expanding into adjacent verticals before that core company has truly settled and before the firm has developed the internal capabilities required to be able to manage the expansion without losing coherence. I understand where the mythology originates. In certain market conditions and certain business models, the first person to scale fastest wins and the stories of businesses that scaled up aggressively and made it are reported more frequently and with greater realism than reports of companies who grew aggressively and broke. For every company that aggressive earlier scaling is the optimal option, there's a few where the speed at which scaling occurs becomes the root cause of problems that eventually kill the business, and those cautionary tales do not receive all the attention that the success cases.
A hidden price of growing too quickly is not the one you see in the burn rate calculation or the cash flow forecast. It's the one that is revealed six months later, after the company has gone beyond the coordination mechanisms of informal nature which held it together when it was smaller but before it's built institutions that hold larger organisations together. This gap between informal and formal as well as between the company you used to be and the one you want to be - is where most companies scaling really fail. The first and most obvious evidence that a business is reaching that apex is when decisions slow down even though everyone claims that there is nothing fundamentally different. The founder's voice is still available in theory. The team remains united with the theories. The culture is robust in theory. But in practice, the organisation has grown to a point at which the informal communication channels which used for carrying crucial information have been clogged however, no one has yet set up the formal channels required to be replaced. Information that once flowed naturally has now to be continuously managed. The decisions that were executed quickly now require alignment across various functions that haven't been clearly defined relative to each other. What was once direct and personal has now become in the middle and awaited as the organization is beginning showing the signs of a system that is functioning at the limits of its coordination capacity.
It's not visible in the metric that founders and investors typically watch the most closely. Revenue may still be growing. It is possible that customer acquisition is going in the right direction. They may be active and efficient. But under the surface that the organisation is developing structural issues that will continue to grow slowly until they can no longer be ignored - at which point fixing them becomes dramatically more expensive and disruptive than it would have been if the issues had been addressed sooner, when the signals aren't as apparent. There is a hidden price I'm talking about that is not the financial cost of growing, but the over-the-long term cost of organisational growth that is incurred by growing beyond your existing infrastructure along with the expense of putting that infrastructure in it in a reactive way instead of proactively.
The founders who can navigate this transition successfully aren't necessarily the ones who scale more slowly, even though taking a more deliberate course of growth may be the solution. They are the ones who recognize that creating the management structure of their business is just as important as developing the product and invest in it with the same care and discipline that they bring to the development of their products. This includes doing the boring administrative work of clarifying roles and decisions clearly, designing reporting structures that actually surface the information needed by the executive to make the right decisions, developing accountability mechanisms that are sufficient to make sense and thoughtfully pondering what kind and type of cultural norms an organization requires at its level of growth instead of taking the one that took shape naturally when it was smaller. None of this work is an exciting task. None of it will generate interest from investors or press coverage. But it's the job that determines whether the organisation that you're establishing can achieve the growth you're striving for.
The companies that fail to get through this transition successfully will generally not fail very in a visible way. They slowly fade. They lose their best staff first - those who have enough self-awareness to see what's happening in the company and have the option to leave before it gets much worse. They then lose customers usually in a gradual manner, as the quality of execution gradually declines as accountability has been too ambiguous and long to be able to recognize issues before they affect the customer. They lose momentum, and by the time that decrease in momentum is apparent in the numbers because the structural problems are deeply entrenched, the cultural damage is substantial, and the cost of fixing both is a tad higher than it would've been if the investment in governance were implemented at the appropriate time. In the eyes of an organisational structure as a item - something that is designed meticulously, construct carefully, and improve upon as the business grows is one of major shifts in mindset one can make by a founder as they progress from the beginning stages to actual scale. The founders who make it tend to build businesses with the potential to succeed. The ones who do not tend to build companies that aren't quite there. Check out James Deller for blog info including why operating at scale shifted my priorities about what matters.
It's The Data Infrastructure Problem Nobody Wants To Discuss
Every business I've had the pleasure of working closely with in the last one and a half years - whether as a founder, an investor or as an operational advisor I have been told, at some point in our time together, that data is the primary factor that influences how they take decisions. Some of them genuinely mean it in a way which is apparent in the way their organization actually operates. The majority of them say they're serious, but what they are describing is an aspiration, rather than a current operational reality - the version of the company they're aiming for in contrast to the reality they currently reside in. The gap between true decisions based on data and the efficacy of decision-making driven by data - the careful management of the outward appearance of information-driven operation, without the infrastructure needed to make it feasible - is among the most important gaps in the current business. It's also one of the most persistently underaddressed ones due to the infrastructure problem that causes it isn't really glamorous to discuss, challenging to demonstrate to external stakeholders and extremely difficult to rank against the more visible strategic and commercial work that demands the same attention from leadership as well as organisational resources.
When organizations talk about data strategy, they typically tend to focus on the capabilities they wish to create on top of their existing data. They talk about systems for analytics, machine-learning applications for operational dashboards, and real-time data as well as the types of prescriptive insights that are truly compelling in the form of a board presentation or an update to investors. What they tend to talk about less frequently and with a lot less energy and enthusiasm, are the core infrastructure that is the determining factor in whether all of these capabilities work according to the specifications: the data governance frameworks that define distinct and consistent definitions of what's being evaluated and why it is as well as the storage and collection methods that decide the validity and comparability of data being captured; the quality assurance processes that catch or correct any errors before they propagate throughout systems and affect the outputs everyone relies on; the structures within the organisation and accountability mechanisms that make data quality one's ongoing and explicit responsibility instead of everyone's vague and not enforceable goal. The plumbing, or the. It is not glamorous. It's hard to take pictures of for an annual report. It doesn't produce any outputs which can be used to create an appealing presentation. It is, from my experience in a vast amount of organizations across different industries and at different stages of development, much worse than the organisation believes that it is.
The problem gets worse in ways that get harder and costlier to correct. An organization which has operated in a way that is inconsistent or not well-defined data definitions for its various functions for three or more years has three years of historical records that cannot be reliably aggregated or compared for comparison or analysis. It's not that the data isn't there, but because the same terminology has been used to describe different things across different areas within the company, and these differences are embedded into the data itself rather than appearing on the surface. The company whose data quality assurance has been someone's sole responsibility, instead of having a properly resourced and dedicated function is one whose data's reliability varies in ways that are not adequately documented and can't be properly accounted for when using the data in making decision. A company that allowed multiple operational systems to create overlapping and partially conflicting records for the same customers, products or transactions, has a data environment that is hard to clean up without operational disruption significant enough to create a risk.
This issue lingers throughout a variety of companies who are truly knowledgeable about their strategy and completely focused on data-driven operational excellence is that addressing it requires regular investment in work which does not produce visible return on investment in the form which resource allocation processes are intended to reward. A new analytics platform produces visible outputs: dashboards that can be demonstrated or reports that could be shared with the board, insights which can be used to create press releases on digital transformation. A data governance program produces invisible infrastructure: clearer underlying definitions and more consistent collection processes and more reliable inputs into technology that is already in existence. The first one is easy to justify during budget negotiations because you are able to demonstrate what they will get. The second needs someone with sufficient organisational credibility and patience to convince people this investment would eventually provide better results for each capabilities that are built on top it. This is an appealing argument in the abstract but it can be difficult to be successful in a battle with initiatives whose benefits seem to have more direct and easily visible.
I've made that argument in a variety of organizational contexts and witnessed it be successful or fail based on clear reasons to have a fairly clear view of the elements that determine whether or not an organisation is able to address its data infrastructure issue or if it continues to put off the issue. The key difference is usually a leader - a specific person with sufficient credibility within the organisation having a genuine understanding of why infrastructure matters, and enough determination to persist in making an argument until it is an absolute priority, rather than a recurring item on the list of things that everyone acknowledges are important however they don't always rise to the top. A leader must be willing to absorb the cost of the infrastructure investment – the time, the disruption to current processes, and the absence any tangible outcomes - and be confident that the capability long-term it builds will justify the expense by several times. What this requires, ultimately is a system of culture where investment in long-term infrastructure is recognized and appreciated at the upper levels of management, not simply articulated in strategy documents and not always prioritized when the quarterly allocation of resources takes place. Achieving that culture is, itself, a long-term investment. It's also, in my opinion, one those investments with the highest returns an organization that is serious about the data-driven operation can make.}
