What Backing Founders Has Shaped My Thinking About Real Value

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What I Did Was Stop Looking For The Next Deal And Began Looking For Who's The Boss?
There's a type of investor behavior that people will recognize right away even though they've never put a name to it. It's the type of conversation where you begin the conversation with the deck, is quickly moved through the numbers and then dwells on the size of the market, and closes with a discussion of exit multiples. The people inside the business - those who be actually executing everything on those slides - barely come up. In the event that they come up, it tends to be in the context of headcount projections instead of as people with their own stories, motivations in addition to blind spots that influence every major decision that the company makes. I've spent a long time within this mode to know its draw. It's hard to resist. It's an analytical feeling. It's like making a judgment based upon evidence instead of intuition. The issue is that this approach systematically ignores one of the most reliable variables of how a company is likely to perform in the medium and long term such as the character and strength of the executives who manage it. The reason for this is not a matter of chance. It is the product of frameworks which were created to be reusable and easy to document and that, in turn, favor those things that can be quantified and compared against things that are truly important but are more difficult to measure.
I was taught this the hard method, like most people, as I watched businesses with exceptional fundamentals fail because their leadership team was not able to stand together during pressure. I also learned this by having businesses with weak foundations, dramatically improve because the individuals within them were truly extraordinary. After several of those instances I stopped pretending it was the data that did all the heavy lifting in my decision-making. They weren't. The numbers were a poor indicator of the choices made by humans, and the performance of those choices depended most of the time on who those human beings were and how they performed under pressure under the pressure of a missed quarter a key departure, a company move they hadn't anticipated or a board connection that had become complex. Therefore, I changed the way I began every conversation about evaluation. Instead of launching with market size or revenue growth, I started opening with what I've come to think of as the"room question: who actually runs this company when pressure is on? How can they make decisions when the data is not accurate What is their approach to people who are around them and what changes to the culture of the organization when the founder is not in the room.

The answers to these questions don't appear in a typical investment checklist. All of them, in my experiences, appear to be more indicative of long-term performance any other item that is. It's not a romantic notion about people being important. This is a concrete observation on the way value is produced and destroyed by businesses with a large scale. There is no reason for companies to fail because of bad markets. They fail due to poor decisions made under pressure by people who were not trained to take them effectively or because of the cultural interactions that were not visible from external observers but silently affecting the organization's capacity to maintain talent, accountability, and adapt to situations that the original strategy was not able to anticipate. Making these decisions early – prior to committing capital or before the problems have become more severe, and before the company culture has gotten distorted around the wrong practices - is an essential work of an entrepreneur who is genuinely concerned about return on investment rather than just deals flow. You cannot spot them while you're spending the majority of your time scrutinizing the model.

The shift I'm talking about may sound simple when you write it clearly, but it is an essential reorientation of the information you use as evidence, and that reorientation is more complicated than what it appears due to the fact that it goes against the incentive structures in many investment strategies. The speed of investment rewards pattern matching at the surface. Competitive deal environments reward confidence over deliberation. The way in which certain investment groups operate actively discourages what gets dismissed as"soft diligence," i.e. the kind of thoughtful, sensitive attention to human characteristics that makes good choices from poor ones, over important time frames. I've been in rooms where people have ignored a concern over management chemistry or leadership by saying "we can make it better post-close" to know how dangerous this notion is. You almost never can. Culture isn't an issue that arises after closure. This is a pre-commitment occurrence and if you're not paying attention to it before you cash the check You aren't doing diligence. You are just doing paperwork and hoping for a miracle.

What I'm now looking for when I'm evaluating either a leader or business team, has evolved into an almost specific set signals. How do leaders respond with respect to when they're clearly wrong about something? Are they willing to accept the correction or just ignore it? What does their conversation style be about the people around them - do they constantly shift credit and accept responsibility or do they carry this in reverse? What do people who have had a close relationship with them in the past when the conversation is moved beyond the formal reference checks format and into something more genuine and exploration-based? What happens to the organization in the moments when nobody is looking and when the Founder is on vacation and the quarterly goal cannot meet the target? That's where the culture exists, not in values that are printed on the walls or the mission statement displayed on the website, but rather in the routine decisions made by everyday people when the circumstances are unclear as well as when the easy thing and the right thing aren't the same. Finding businesses in which these decisions have been consistently made and consistently successful is, to my knowledge the most secure path in achieving returns that stand up in the long run. Follow James Deller for blog examples including why operating through uncertainty has shaped my thinking about growth.



This Is The Data Infrastructure Problem Nobody Wants To Talk About
Every organisation I have worked closely with in the last 10 years and a half - whether as an investor, founder or a consultant to operational matters I've heard, at some point in the course of our work, that data is the primary factor that influences how they take decisions. Certain of them are truly committed to it in a way that will be evident in the way the organization operates. Many of them believe that they're making a statement, however they're describing an aspiration, rather than a current operational reality - the version of the company they're aiming for rather than the one they're currently living. The gap between authentically information-driven decision-making and performance of data-driven decisions – maintaining the appearance on the outside of an data-driven operations, but without the infrastructure that makes it an actual reality - is among those gaps that are the most impactful in modern day business. It's also one of the biggest gaps that are not addressed in part due to it is a problem with infrastructure that it isn't really glamorous to talk about, difficult to demonstrate to external stakeholders and extremely difficult to distinguish from the more obvious strategic and commercial projects that require the same attention of leaders and organizational resources.
When businesses talk about Data strategy, they generally tend to discuss what capabilities they'd like to develop on top of their data, such as data analytics platform, machine-learning applications and the operational dashboards that are real-time and the types of predictive insights that sound really compelling in the context of a board meeting or an update to investors. What they are talking about less frequently and with less energy and enthusiasm, are the core infrastructure which determines whether all functions of those tools actually work in the way they're advertised: data governance frameworks which provide clear and uniformly applied definitions of what's being assessed and how; the collection and storage methods that decide the validity and comparability of data to be gathered; the assurance procedures that can detect and rectify errors before they become a part of your system and destroy the outputs that everyone relies on; the structure of the organization and accountability mechanisms that make data quality the sole responsibility of an individual rather than a vague and ineffective plan. The plumbing, or the. Plumbing is not glamorous. It's not easy to photograph in a report for the year. It has no outputs that can be showcased in a compelling presentation. This is, in my experience across a substantial number of companies in different industries and at different stages in development, a lot worse than the organization believes that it is.

The issue gets worse over time and becomes harder and more expensive to fix. A company that has been operating with a lack of clarity or inconsistent terms of data for all its functions for three or more years has three years of historical records that cannot be reliably aggregated or compared which is not because the data has not been created, but because the same terms have become a synonym for different things across different sections within the company, and these differences are embedded into the data, rather than appearing on the surface. The company whose data quality assurance is someone else's secondary responsibility, rather than a dedicated and properly resourced function has data that's reliability differs in ways undocumented and can't be effectively accounted for when the data is used to determine the outcome. An organisation that has allowed multiple operational software systems to accumulate overlaps and partially conflicting records for the same products, customers and transactions have created a landscape of data that is hard to clean up without causing enough disruption to pose a risk for the organization itself.

The reason that this problem continues to exist in a lot of organisations who are truly knowledgeable about strategy, and who are truly committed to a data-driven business model is that fixing it requires ongoing investment in work that does not produce visible short-term returns of the kind which resource allocation processes are designed to reward. A new analytics system produces visible outputs like dashboards that are easily demonstrated or reports that could be shared with the board of directors, and information that can be translated into press releases about digital transformation. A data governance program produces transparent infrastructure - better definitions with more consistent collection procedures as well as more reliable inputs to technology that is already in the first place. The first is relatively straightforward to justify in a budgeting conversation because you can clearly show the people what they'll get. Second, you need someone who has sufficient organizational credibility and patience for convincing people you believe that this infrastructure initiative will eventually bring better results from every capability built on top of it - which is an impressive argument in abstract, but difficult to win in competition with initiatives that's benefits appear to be immediate, and more apparent.

I've been able to make that case throughout a variety of contexts, and watched it succeed or fail based on clear reasons to have a pretty clear idea of what factors determine whether an organization finally tackles its data infrastructure problems or simply defers it. The main factor that determines this is determined by a leader - an one with enough organizational credibility and an comprehension of why the infrastructure is vital, and enough perseverance to continually make the case until it becomes an absolute priority, rather than an ongoing item on the list of things that everyone agrees on but don't become a priority. That leader has to accept expenses in the short term of infrastructure investment - the delay it takes to complete, the disruption to current processes, and the absence any tangible outcomes - in the knowledge that the long-term capability it creates will justify that cost several times over. What's needed, in the end is a system of culture where investment in long-term infrastructure is valued and rewarded at the levels of the leadership, and not just stated in strategy documents, then consistently deprioritised when the quarterly resource allocation debate happens. Making that change is, in itself, a long-term investment. But it is, in my view, one the highest-return investments an organisation which is serious about a data-driven operation could make.}