What Growing Up Around the Game Changed My Approach About Results

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What's The Hidden Cost To Scaling Too Fast: What Founders Typically Learn Too Late
The mythology about scaling is often centered on speed. Make sure that the product is market-ready, then add fuel to the fire. Make the team bigger, expand your market, then raise the next round prior to the previous one has properly settled. The story favors the founder who is always in the process of expanding, adding heads, always expanding into adjacent verticals before when the main business of his truly stabilised, and before the company has built the internal capabilities needed to control that expansion without losing the semblance of. I am aware of where this notion comes from. In certain economic conditions and business models, the first one to scale the fastest wins, as are the stories about firms that scaled up aggressively and made it are more often told and in greater detail than tales of companies that grew aggressively and broke. In every instance where aggressive early scaling is a good approach, there's a dozen where the speed at which scaling occurs becomes the root cause of problems that eventually destroy companies, and those cautionary tales don't receive much of the same attention as those that have been successful.
The hidden cost of scaling too quickly is not the one that is revealed in the calculation of burn rate or the cash flow forecast. It is what is visible at the end of six months, when an organization has advanced past the coordination mechanisms of informal nature that held it together as it was a small one, and even before it has created more formal mechanisms that keep larger organizations together. This gap - between formal and informal as well as between the company which you are and the company you're supposed to become is the place where many companies growing are able to fail. The first and most obvious evidence that a business is getting into that gap is that decision-making starts to slow down when everyone says that nothing fundamentally changed. The founder is still accessible in the theories. The team continues to be aligned in the theory. The culture remains solid in theory. However, in actual practice the organization has gotten to the point where informal channels for communication used to carry the crucial information have been clogged and no one is yet able to build the formal channels needed to be replaced. Information that was once flowing naturally is now constantly monitored. Decisions that used to be quick now require coordination across various functions that never have been clearly defined as compared to each other. Accountability that used to be personal and immediate has become dispersed and delayed The organization is beginning to display the signs of a system functioning at the limits of its coordination capacity.

None of this is visible in the data that the founders and investors are expected to watch most attentively. It is possible that revenue will continue to grow. It is possible that customer acquisition is growing in the right direction. The team may remain energetic and hardworking. However, underneath the surface indicators it is becoming apparent that the business has structural issues that can only grow slowly until they can no longer 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 in the past, when the warning signs were more subtle than obvious. The hidden costs I am talking about that is not the financial cost to scale, but the longer-term costs of extending your organisation beyond your existing infrastructure along with the expense to put that infrastructure in it in a reactive way instead of proactively.

The founders who make the transition with ease aren't necessarily those who expand more slowly, even though an intentional pace of growth might be the answer. They recognise that building the corporate governance structure is just as important in the same way as creating the product and invest in this with the same level of commitment and commitment to the development of their products. It means performing the tedious operational task of the definition of roles and decision rights clearly, building reporting structures that actually surface the information management needs to make sound decisions, designing accountability systems that are particular enough to be meaningful as well as thinking about what kind and type of cultural norms your company requires for its scale, instead of being reliant on the ones that developed naturally when it was smaller. All of this isn't fun. It's not going to generate interest from investors or press coverage. But it's the job that determines if your company you're building will achieve the growth you're in pursuit of.

Companies that do not achieve this feat do generally not fail very clearly. They are fading. They lose their best employees first - the ones who have enough awareness of the state of affairs within the organisation and enough options to quit before things get substantially worse. They also lose customers usually in a gradual manner, as the quality of execution in a quiet way is diminished because accountability become too scattered and delayed to spot problems before they reach the customer. As they lose momentum until the loss of momentum becomes apparent in the numbers The structural issues are deeply rooted. The cultural damage is substantial, and the cost of fixing each is far higher than it would've been if the governance investment had been made at the right time. It is important to view organisational infrastructure as an product that you create mindfully, construct carefully and continue to refine as the business grows is among the most crucial shifts in thinking an entrepreneur can undergo as they transition from the initial stage to the real. The founders who master this tend to build companies which are able to fulfill their potential. However, those who fail tend to build companies that don't even come close. Follow James Deller for site tips including how advising growing businesses changed my approach about what matters.



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 a founder, an investor and/or an operational advisor I have 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 way which is reflected in how the business actually operates. A majority believe they're genuinely meaning it, but what they're discussing is the aspiration of an actual reality that is the version of the company that they're aiming to build and not the one they currently reside in. The gap between authentically data-driven decision-making as well as the effectiveness of data-driven decision-making, maintaining the external appearance of evidence-based decision-making without the infrastructure needed to make it real - is one those gaps that are the most impactful in modern day business. It's also among the biggest gaps that are not addressed partly because the infrastructure issue that causes it isn't glamorous to discuss, challenging to explain to outside stakeholders and extremely difficult to classify against the more prominent commercial and strategic work that is competing for the same leadership attention and organizational resources.
When businesses talk about plans for data management, they tend to talk about the capabilities they wish to build on top of their data - the analytics platforms, the machine-learning applications and the operational dashboards that are real-time that provide the kind of predictive insights that sound really compelling in presentations for boards or in an investor update. The thing they discuss less frequently and with far less energy and passion, is the base infrastructure that will determine if all of those capabilities actually function as claimed: the information governance frameworks that set specific and consistent definitions of what's being measured and why to measure it; the storage and collection methods used to determine the authenticity and comparability of the information in the process of being collected; quality assurance processes that identify and fix errors prior to they are propagated through the system and corrupt the outputs that everyone relies upon; the organization's structures and accountability systems that make quality of data someone's explicit and ongoing responsibility as opposed to everyone's vague unclear intention. The plumbing, in other words. The plumbing isn't glamorous. It's hard to photograph to be used in an annual report. It has no outputs that can be demonstrated in an impressive presentation. In my experience of a vast number of companies in different fields and at different stages of development, significantly worse than what the organization believes it to be.

The issue continues to grow over time and becomes difficult and costly to correct. An organization that has been operating with a lack of clarity or inconsistent terminology for data across different roles for three consecutive years has three years of historical data that are unable to be accurately compared or aggregated - not because the data doesn't exist, however because the same terminology has been used to refer to various terms in different parts of the organization. Moreover, the distinctions are embedded within the data itself instead being apparent on the surface. A business whose quality assurance is a sole responsibility, instead of a dedicated and properly resourced function is one whose data's reliability differs in ways that are not documented in a systematic manner and cannot be systematically accounted for when using the data in making decision. An organisation that has allowed multiple operational processes to accumulate overlapping or partly conflicting records of the same customers, products or transactions may have an unresolved data landscape that is very difficult to deal with without causing significant disruption to operations that it is a threat to the organisation itself.

The reason this problem persists in so many organizations that are genuinely intelligent about strategy and genuinely committed to data-driven operation is because it's a constant investment in work that produces no visible short-term returns of the kind that resource allocation processes in organizations are intended to reward. Analytics platforms that are new produce tangible outputs - dashboards, which can be demonstrated and reports that can be shared with the board, insights that can be translated into press releases about digital transformation. Data governance programmes create invisible infrastructure that is more efficient - clearer definitions and more consistent collection methods as well as more reliable inputs into technology that is already in established. The first one is easy to explain in a budget debate because you are able to demonstrate what they'll gain. The second one requires sufficient organisational credibility and a willingness to convince people of how the capital investment is going to eventually provide better results for each new capability that is added to it. It's an impressive argument in abstract but a difficult one to compete with initiatives that's benefits will be more tangible and more obvious.

I've argued that case in multiple organizational contexts, and watched it succeed or fail due to obvious reasons, to be able to get a pretty clear idea as to what decides whether an organisation is able to address its data infrastructure problems or continues delaying it. It is generally an individual leader - an leader with the credibility of an organisation and a clear appreciation of the reason that infrastructure is necessary, and the determination to continue making that argument to the extent it becomes an actual priority instead of simply a part of the list of things everyone is in agreement about however, they never become a priority. That leader has to be willing to take on some of the costs of an infrastructure project - the cost, the time, the disruption to existing processes, and the lack of tangible outputs - in the confidence that the long-term capability this investment creates will justify it's investment several times more. What this requires, ultimately is a framework which long-term infrastructure investment is considered and valued at the top level, not only stated in strategy documents, regularly discarded during the quarterly resource allocation discussions takes place. Making that change is, in itself an investment for the future. However, it is, in my opinion, one of the highest-return investments an organisation which is serious about a data-driven operation could make.}