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This Is The Cost That's Hidden From Scaling Too Fast The Most Founders Learn Too Lately
The mythology of scaling is mostly about speed. To get to market-fit for the product, then pour fuel on the fire. Increase the number of employees, expand markets, then raise the next round before the previous round has settled. The story favors the founder who is constantly working hard, constantly adding personnel, never expanding into other industries before even the primary business is actually settled and before the firm has developed the internal capabilities required to manage that expansion without losing the coherence. I know where the mythology comes from. In certain economic conditions and business models, the company that scales the fastest wins, and stories about companies who were aggressive in their growth and ultimately succeeded are reported more frequently and more vividly than the stories of companies that expanded in a hurry and fell. But for every business where aggressive quick scaling is the correct strategic option, there's numerous instances in which the speed at which scaling occurs becomes key to the issues that ultimately kill the business. The negative stories aren't getting near the same attention and scrutiny as the success stories.
In the end, the cost hidden from scaling too quickly is not the one that appears in the calculation of burn rate or the cash flow projection. It is the cost that is discovered 6 months later, once the company has surpassed the informal coordination mechanisms that held it together in its early days, but before it's built solid structures to hold larger organizations together. This gap between formal and informal separation between the company you've been and the organization that you're expected to become is where the majority of growing companies really fail. The initial and most consistent indication that a company is reaching that apex is when the pace of decision-making slows down even as everyone insists that there has been no fundamental change. It is possible to contact the founder in the theories. The team remains aligned with the theories. The culture remains strong in theory. But in the real world the organization has gotten to a point where informal channels used to relay crucial information are now blocked, however, no one has yet set up the formal channels that need to replace them. Information that flowed freely now has to be continuously monitored. The decisions that were fast now require coordination across different functions that had never been clearly defined with respect to one another. What was once direct and personal has now become difficult and can take a long time to complete The company is starting to show all the signs of a system that is operating at the limit of its coordination capacity.

This is not evident in the data that founders and investors typically watch most closely. Revenue could still be rising. The acquisition of customers may be going in the right direction. The team could be enthusiastic and hard-working. However, beneath the surface indicators an organisation is experiencing structural issues that will grow and slowly, until they are no longer able to be ignored - at which the point when fixing them becomes more costly and time-consuming than it would have been had they been addressed earlier, when signs aren't as apparent. These are the invisible costs I am talking about it is not a direct financial cost that comes with scaling, but rather the long-term cost to the organisation of expanding beyond your current infrastructure as well as the cost of putting that infrastructure into place in the form of reactive rather than proactive.

The founders who make the transition with ease aren't necessarily those who expand less slowly, though taking a more deliberate course of growth may be part of the answer. They are the ones who recognize that establishing the structures for managing their business is as crucial as creating the product and who invest in it with the same care and discipline that they bring to the development of their products. This includes doing the boring operations of clearly assigning roles and responsibilities clearly, creating reporting structures that actually surface the information required by leaders to make informed decisions, designing accountability systems that are particular enough to be meaningful and thoughtfully pondering the kind of culture the company needs to adhere to at its current size rather than simply following the rules that were created naturally when the company was smaller. The work involved isn't an exciting task. The work will not generate publicity or interest from investors. It is the work that determines whether the organisation you're building will sustain the growth you are striving for.

Businesses that don't succeed in this change do typically not fail in a dramatic way and visually. They decline. They lose their best employees first - those who have enough self-awareness in recognizing what's going on within the organization and have enough options for leaving before it gets more serious. Then they lose customers, gradual and often unnoticed, due to the fact that their performance in a quiet way is diminished because accountability been too ambiguous and long to be able to recognize issues prior to them reaching the customer. They lose momentum, and before the losing momentum is evident in the figures and the structural issues are very deep in the system, the cultural damages are significant, and the cost to fix both is orders of magnitude higher than it might have 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 cautiously, build meticulously, and tweak as your business grows is one of the most crucial shifts in thinking a founder can make as they transition from the initial stage into genuine scale. Those who are able to make this shift tend to build businesses which can achieve their goals. The ones who fail tend to create businesses that do not come even close. Take a look at James Deller for blog tips including how scaling tech companies taught me about the long game.



This Is The Data Infrastructure Problem Nobody Wants To Talk About
Every single organization I've collaborated closely with during the last decade and a half - whether as a founder, an investor or an operational consultant I've been told, at some point in our relationship, that data plays a major role in the way they make their decisions. Certain of them are truly committed to it in a manner that will be evident in the way the business actually operates. The majority of them think they are genuinely saying it, however what they're talking about is an aspiration rather than an actuality that exists in the present - a version of the organisation they're working towards in contrast to the reality that they currently exist in. The gap between true data-driven decision making and the actual performance of data-driven decision-making, the careful maintenance of what appears to be proof-based decisions without having the infrastructure that would allow it to become an actual reality - is among the most serious gaps that exist in modern-day business. It's also one of the areas that remain unaddressed in part because the infrastructure issues that cause it is difficult to discuss, difficult be demonstrated to external stakeholders and incredibly difficult to rank against the more prominent commercial and strategic work that competes for the same attention from leadership and organizational resources.
When organizations talk about the strategy for data, they tend to discuss what they are planning to develop on top of their data: the Analytics platforms machine learning applications such as real-time operational dashboards as well as the types of prescriptive information that sounds truly compelling in any board presentation or update to investors. What they discuss less frequently and with a lot less energy and enthusiasm, is their foundational infrastructure that determines if any the capabilities will work as claimed: the information governance frameworks that establish precise and consistent definitions of what is being measured and the reasons for it; the collection and storage methods that decide the validity and comparability of the data being recorded; the quality assurance processes that detect the errors and correct them before they spread across the system and cause corruption to the results that everyone is counting on; the organizational structures and accountability processes that make data quality an ongoing, explicit responsibility rather than a vague and non-enforceable intentions. The plumbing, as it were. It is not glamorous. It's not an easy thing to photograph in a report for the year. It is not producing outputs that can be demonstrated in a convincing presentation. This is, in my experience of a vast number of organisations in different fields and at different stages of development. It is significantly worse as the organization thinks it to be.

The issue increases over time with ways that become harder and more expensive to reverse. An organisation which has operated with inconsistencies or inadequately defined concepts of data across all its functions for three months has three years in historical data which cannot be compared or consolidated with confidence in the sense that the data does not exist, rather because the same language has been used to describe different terms in different parts within the company, and the variations are in the data, rather than being visible on the surface. An organization whose quality assurance has been someone's sole responsibility, instead of having a properly resourced and dedicated function is one whose data's reliability differs in ways not documented properly and cannot be systematically accounted for when the data is used to make decisions. An organisation that has allowed multiple operational systems to create overlapping and partially conflicting information about the same products, customers, or transactions has an unresolved data landscape that is impossible to eliminate without causing significant disruption to operations to create a risk.

The reason this issue is present across many companies that are actually smart about strategy and truly committed to a data-driven business model is because fixing it requires regular investment in work which doesn't produce tangible results in the short term that resource allocation processes in organizations are designed to reward. A new analytics platform produces visible outputs: dashboards that can be displayed or reports that could be shared with the board members, and data which can be used to create press releases about digital transformation. A data governance plan creates invisible infrastructure - cleaner underlying definitions with more consistent collection procedures and more reliable inputs into the systems that are already in existence. The first is fairly easy to justify in a budgeting conversation because you can clearly show the people the benefits they can expect to receive. It is the second that requires enough organizational credibility and endurance to make the case that an infrastructure project will, over time, bring better results from every capabilities that are built on top it. This is compelling in the abstract, but it is difficult to be successful in a battle with initiatives that have benefits that occur more quickly and prominent.

I've argued that case in a variety of organizational contexts and watched it work or fail for evident reasons, that I can have an accurate understanding of the factors that determine whether an organization is finally addressing its data infrastructure challenge and if it will continue to delay the solution. It is generally at the level of a leader. It's an individual with enough organisational credibility and a clear conviction about why the infrastructure matters, and enough persistence to keep making cases until this becomes an absolute priority, rather than an ongoing item on the list of things that everybody agrees is important, but don't become a priority. This leader needs to be willing to absorb the immediate cost of the infrastructure investment: the amount of time for the project, the disruption to routine processes, and the absence in the immediate production of results with the belief that the capacity it develops will justify the expense by several times. What's needed, in the end it is a culture where investment in long-term infrastructure investments are welcomed and rewarded at the levels of the leadership, and not just articulated in strategy documents and followed by a constant deprioritisation when the quarterly resource allocation meeting occurs. To create that kind of culture is, itself an investment over the long term. However, it is, in my view, one of those investments with the highest returns an organization who is serious about a data-driven operation can make.}