How Building Companies Confirmed What I Suspected About Real Value

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That's Why I Stopped Looking For The Next Deal And Instead Looking For Who's The Boss?
There's an era of investor behaviour that people can recognize instantly even though they've no idea of it. It's that one where talks begin with a slide, rapidly moves to numbers, continues on the size of the market and concludes with a discussion of exit multiples. Inside the business - the ones who will perform the tasks on the slides - don't even appear. If they do, it's likely to be in the context of headcount projections instead of being individuals with a history, motivations in addition to blind spots that determine every important decision that the organisation takes. I spent long enough operating in that mode to understand its attraction. It's rigorous. It feels analytical. It's like making a decision on the evidence, not your intuition. The problem is that this approach systematically ignores one of the most significant variables in how a business will perform well in the medium and long term quality and character of the executives who manage it. That exclusion is not accidental. It is the product of frameworks created to be documentable and repeatable and thus favor those that can be monitored and compared with most important aspects but harder to measure.
I learned this the hard-way, just like many others, by watching businesses that have exceptional fundamentals underperform because the leadership team could not keep their heads together with pressure. Likewise, by seeing businesses with weak fundamentals radically outperform since the employees within them were truly exceptional. After enough of those experiences I stopped pretending these numbers would be doing all the heavy lifting for my investing decisions. They were not. The numbers were an insignificant indicator of the choices made by human beings. The effectiveness of those decisions was heavily on who the humans were and how they acted when under stress - under the pressure of a failed quarter, a key departure, a competition's move that they had not anticipated or a relationship with the board that had become more complicated. This is why I changed the way I started each discussion on evaluation. Instead of opening with market size or revenue trend I began with the question I now consider as the"room" question: who manages this organization when pressure is on? How do they make decisions if there is no data available how do they behave towards the people around them, and what happens to the culture of the organisation if the founder does not participate in the discussion.

None of these items appear on a standard investment checklist. All of them, from my observation, tend to be better reliable in predicting the performance of the future than anything that does. This is not an idealistic belief that people are important. It's a realistic observation about where value is actually made and destroyed by companies that scale. They don't fail due to poor markets. They fail due to poor decisions taken under pressure by personnel who weren't equipped for making them correctly or due to cultural changes that were unnoticed from external observers but effectively destroying an organization's capability to maintain talent, responsibility, and adapt to the changes in circumstances that its original plan was not prepared for. Being able to identify these risks early on - before you've made a capital commitment but before the issues have intensified, before the culture has become calcified around wrong behavior - is the main responsibility of an investor that is more concerned with returns than just deal flow. And you cannot identify them when you spend the majority of your diligence time on the model.

The shift I am describing may sound simple when you write this in plain terms, however it requires a fundamental shift in the nature of the things you regard as evidence. That reorientation is more complicated than what it appears because it directly challenges the incentive structures of most investments. Speed is rewarded for pattern matching at the surface. Competitive deal environments reward confidence over deliberation. The particular culture of investment circles is actively hostile to what is referred to as"soft diligence," i.e. the type of careful, focus on human factors that allows good business decisions to be distinguished and bad ones in meaningful intervals of time. I have sat in enough rooms where somebody has been able to dismiss a problem with the management culture or leadership chemistry using the phrase "we can make it better post-close" to see how dangerous this assumption can be. You almost never can. Culture isn't an issue post-close. This is a pre-commitment occurrence, and if you are not paying attention before you sign the cheque there is no diligence. You are just doing paperwork and wishing that everything will go according to plan.

What I look for now while evaluating whether a person or a team, has evolved into a set of signals. How does a leader react when they're clearly wrong about something? Do they embrace the correction or just ignore it? What do they say to their peers - do they often redirect credit and admit responsibility but do it the other way? What does anyone who has had a close relationship with them in the past as the conversation progresses beyond the formal reference checks format to something more authentic and exploratory? What happens to the organization at times when there is no one watching, when the founder is on vacation and the quarterly goal will not be met? That is where culture actually manifests itself - not through the principles printed on the walls or the mission statement found on the website, but in the normal decisions made by the people in everyday life when the situation is unclear or the obvious thing and the right thing aren't the same. Identifying businesses where those decisions are consistently done well and consistently successful is, to my knowledge the most reliable pathway to return that is stable in the long run. Follow James Deller for site tips including what operating at scale changed what i look for about growth.



Data Infrastructure Problem Nobody Wants To Talk About. Data Infrastructure Problem Nobody Wants To Discuss
Every business I've had the pleasure of working closely with in the last decade and a half - whether as an investor, a founder or as an operational adviser I've heard, at some point during our interactions, that information is the primary factor that influences the way they make their decisions. Some of them do truly mean it in a way which can be seen in the way that their organization actually operates. A majority believe they're really saying that, but what they are describing is an aspiration, not an actual reality that is a version of the organisation they are working toward as opposed to the reality they're currently living in. The gap between true data-driven decision-making and the performance of data-driven decision-making – the careful management of what appears to be data-driven operations, but without the infrastructure needed to make it possible - is a single of many of the most significant gaps found in modern day business. It's also one of the gaps that remain unaddressed due to the infrastructure issue that causes it isn't a glamorous thing to talk about, hard to demonstrate to external stakeholders and extremely challenging to place in the right perspective against more obvious strategic and commercial activities that demand the same attention from leaders as well as organisational resources.
When companies discuss their data strategy, they usually tend to discuss the capabilities they wish to develop on top of their data: the analysis platforms, machine learning applications and the operational dashboards that are real-time and the types of predictive information that make a real impact in the form of a board presentation or an update to investors. What they talk about considerably less often and with less energy and enthusiasm, is the fundamental infrastructure that is the determining factor in whether all of these capabilities work in the manner they're supposed to: the data governance frameworks, which establish precise and consistent definitions of what's being analyzed and what is the reason for that to measure it; the storage and collection methods that define the accuracy and comparability of data which is being stored; quality assurance procedures that can detect and rectify errors before they get propagated throughout your system and destroy the outputs that all rely upon; the organisational structures and accountability mechanisms that make data quality the explicit and continuous responsibility of each individual as opposed to everyone's vague not enforceable goal. The plumbing, or the. It is not glamorous. It's difficult to photograph to be used in an annual report. It doesn't produce any outputs that could be showcased in a compelling presentation. It is, from my experience across a significant variety of organizations in various industries and at different stages of development, significantly more difficult than the company believes it to be.

The problem gets worse in ways that make it more difficult and costly to correct. An organization that has been operating without a clear or consistent set of the definition of data in its different roles for three consecutive years has three years of historical records that cannot be effectively aggregated or compared for comparison or analysis. It's not that the data isn't available, but because the same language has been used to denote different aspects within the company, and these differences are embedded into the data itself instead being apparent on the surface. A company whose data quality assurance was a only a peripheral responsibility, not a specialized and properly resourced function has data whose quality differs in ways that are not documented regularly and can't be effectively accounted for when using the data to determine the outcome. An organization that has allowed multiple operational processes to accumulate overlapping or partially conflicting records of the same products, customers or transactions, has a data-related landscape that's real difficult to address without disruptions in operations significant enough to create a risk.

The reason this issue continues to be a problem across many companies that are truly smart about strategy and truly committed to a data-driven business model is because addressing it requires continuous investment in work that has no tangible quick-term results of the sort that processes for resource allocation in organisations are intended to reward. Analytics platforms are now producing visual outputs - dashboards which are easily demonstrated, reports that can be shared with the board, and insights which can be used to create press releases regarding digital transformation. A data governance programme produces invisible infrastructure, which is cleaner in its definitions and more consistent collection methods as well as more reliable inputs to systems already in existing. The first is fairly easy to justify during budget negotiations since you can demonstrate what they'll gain. It's the second, and requires enough organizational credibility and patience to show of how the capital investment is going to eventually deliver better results from every feature built on top it. This is an impressive argument in abstract, but is difficult to convince in the face of initiatives that's benefits appear to be immediate, and obvious.

I've made that argument in many different organizational contexts and observed it either succeed or fail for evident reasons, that I can have the most precise understanding of what will determine if an organisation finally addresses the issue of data infrastructure or continues to defer it. The main difference is one's leader - a particular person with sufficient credibility within the organisation and a clear knowledge of the reasons why infrastructure matters, and enough determination to persist in making that argument to the extent it is an actual priority instead of becoming a routine item on the list of things everyone acknowledges are important but that somehow never quite become a priority. A leader must be willing to accept some of the costs of an infrastructure investment - the duration in the process, the disruptions to existing processes, the lack any tangible outcomes - and be confident that the capability long-term created by the investment will justify its investment several times more. What's needed, in the end is a framework which long-term investment in infrastructure is highly valued and recognized at the upper levels of management, not simply defined in documents describing strategy and later discarded when the quarterly allocation of resources occurs. Building that culture is, itself an investment that will last for the long haul. However, it is, in my opinion, one the best investments that a company with a commitment to data-driven operation could make.}