How Building High-Performance Teams Confirmed What I Suspected About Real Value

Why I Stopped Chasing The Next Deal And Instead To Ask Who's Running The Room
There's a type of investor behaviour that people immediately recognize, even if they have had no clue about it. It's the kind of scenario where it starts with the deck, quickly moves on to numbers, then lingers about the market size and is concluded with a discussion of exit multiples. The insiders of the company - - the ones who take the initiative to implement what is on those slides - don't even appear. Even if they appear, it is likely to be in the context of headcount projections instead of as individuals with histories, motivations, and blind spots that will determine every important decision that the company makes. I've worked long enough in that way to realize its benefits. It's extremely rigorous. It's almost like it's analytic. It's as if you're making decisions based on information rather than intuition. The problem is that it systematically excludes the single most predictive variable for determining how a company will actually be successful over the long-term and medium-term quality and character of those who run it. That exclusion is not accidental. It's the result of frameworks which were created to be repeatable, and easily documented as well as to favor those that can be quantified and compared against factors that are crucial and difficult to measure.
I learned this lesson the hard way much like the majority of investors, through watching companies with outstanding fundamentals underperform because the leadership team was not able to stand together in the face of pressure. And by watching businesses with modest basics dramatically perform because the employees within them were genuinely exceptional. After a few of these experiences I stopped assuming that figures were doing the heavy lifting in my investing decisions. They were not. The data was a weak indicator of the choices made by humans, and the decision-making quality was most of the time on who those human beings were as well as the way they performed under stress under the pressure of a missed quarter, some major departure, competitor move they had not anticipated or a board relation that was getting complicated. So I changed how I began every meeting on evaluation. Instead of starting with market size or revenue growth, I started opening with what I've now come to see as the"room question which person actually runs this organisation when the pressure is on? How can they make the right decisions when there is no data available What is their approach to others around them and what happens to the culture of that organisation when the founder does not participate in the discussion.

None of these questions are on a checklist of standard investments. They all, in the experience of me, are better accurate in predicting long-term performance than anything that does. This isn't a romantic idea of people being valuable. It's an observation of the places where value is generated and destroyed in companies which are large. There is no reason for companies to fail due to bad markets. They fail because of bad decisions taken under pressure by employees who were not prepared to make these decisions effectively or due to the impact of culture patterns that are not visible from the outside but that were slowly destroying the ability of the company to hold onto talent, maintain responsibility, and adapt to the changing environment that the original plans did not consider. Identifying those risks early - before you have committed capital or before the problems have developed, and before the culture has formed around the incorrect conduct - is really the task of an investor who cares about returns more than just deal flow. They are not easy to spot while you're spending the majority of your time scrutinizing the model.

The shift I'm discussing is easy to describe when you lay its premise clearly, however, it is an essential reorientation of what you treat as evidence. That reorientation is more difficult than it appears because it runs directly against the incentive structures in many investment practices. Speed rewards pattern matching on the surface. Competitive deal environments reward confidence over deliberation. The tradition of certain investment groups deliberately discourages what is perceived as"soft diligence," i.e. the kind that pays careful, attentiveness to human factors that can help distinguish good decisions from bad ones over meaningful period of time. I've been in enough rooms where somebody has absconded from a concern regarding leadership chemistry or management culture by saying "we can correct that post-close" to see how dangerous this notion is. You almost never can. Culture is not something that happens after closing. It is a pre-commitment reality, and if you are not paying attention to it before you write the cheque that isn't diligence. You're doing paperwork and hoping for the best.

The things I'm looking for when evaluating whether a person or a team, has evolved into a pretty specific set signals. How do leaders respond when they are demonstrably wrong in a particular area? Do they respond to the correction or ignore it? What is their approach to talking about their peers - do they regularly redirect credit and take responsibility instead of doing it in the opposite direction? What do people who have had a close relationship with them in the past tell them in the event that the conversation goes beyond the traditional reference check format and becomes more genuine and inquisitive? What happens within the company in the moments when no one is watching or the founder is on vacation and the quarterly goal cannot be reached? That's where culture exists, not in values that are printed on the walls or in the mission statement on the site but in the common decisions done by everyday people when the situation is unclear or the obvious thing and the right choice are not the same. Identifying companies in which those decisions are consistently made well and consistently successful is, to my knowledge the most reliable route for ensuring returns that last in the long run. Read James Deller for blog tips including why investing in people deepened my conviction about people about real value.



This Is The Data Infrastructure Problem Nobody Wants To Talk About
Every organization I've worked closely with in the last decade and a half - whether as an investor, founder or a consultant to operational matters I've heard from them, at some point in our working relationship, that data is the main factor in the way they take decisions. Certain of them are truly committed to it in a way that has a direct impact on how the business actually operates. Many of them believe that they mean it, but what they're really describing is an aspiration, rather than actually a present operational reality the version of the company that they're aiming to build rather than the one they live in. The gap between truly informed decision-making based on data and the efficiency in data-driven decision-making - - the careful maintenance of the exterior appearance of evidence-based processes without the infrastructure that makes it possible - is a single of the most serious gaps that exist in modern-day business. It's also among the most neglected ones in part due to the infrastructure issue that creates it is genuinely unglamorous to talk about, difficult to explain to outside stakeholders, and enormously difficult to distinguish from the more prominent commercial and strategic activities that demand the same attention of leaders and organizational resources.
When companies discuss their plans for data management, they tend to focus on their plans for the capabilities they intend to create on top of their data - the systems for analytics, machine-learning applications and the operational dashboards that are real-time along with the types of statistical insights that are truly compelling in the context of a board conference or an investor update. What they are talking about less often and with a lesser amount of energy and enthusiasm, is the foundational infrastructure that will determine if all capacities actually function in the way they're advertised: data governance frameworks that provide clear and uniformly applied definitions of what is being measured and for what purpose, the data collection and storage methods that decide the validity and comparability of data that is being collected; the quality assurance processes that detect the errors and correct them before they can spread through your system and destroy the outputs that everyone depends upon; the organisational structures and accountability processes that make data quality an ongoing and explicit obligation rather than a vague and unclear intention. The plumbing, as it were. Plumbing is not glamorous. It's not an easy thing to photograph to be used in an annual report. There are no results capable of being presented in a convincing presentation. This is, in my experience of a vast number of organizations operating in different sectors and in different stages of development, much worse than the business believes it to be.

The issue becomes more severe over time by becoming more costly and difficult to fix. An organization that has been operating without a clear or consistent set of data definitions across its different functions for three or more years has three years old data that is unable to be effectively compared or aggregated which is not because the data does not exist, but because the same terminology has become a synonym for different items in different areas of the organization, and the differences are contained in the data, rather than being apparent on the surface. An organisation whose data quality assurance has been someone's peripheral responsibility rather than a dedicated and properly resourced function has data that's reliability can be questioned because it is not systematically documented and therefore is not systematically considered when the data is used to take decisions. An organisation that has allowed multiple operational software systems to accumulate overlaps and partial conflicting records on the same products, customers or transactions can create a data environment that is real difficult to address without causing enough disruption to present a risk.

The reason this issue continues to be a problem in a lot of organisations that are genuinely intelligent in the field of strategy and totally dedicated to a data-driven approach to business is because addressing it requires constant investment in work that does not provide visible results in the short term that organisational resource allocation processes are intended to reward. The new analytics platform can produce tangible outputs, such as dashboards that can be demonstrated or reports that could be shared to the board, information that can be translated into press releases on digital transformation. A data governance system creates invisible infrastructure - cleaner underlying definitions and more consistent collection processes as well as more reliable inputs to existing systems already in already in place. The first is relatively straightforward to justify during budget negotiations because you can clearly show the people what they'll gain. Second, you need someone who has enough organizational credibility and patience to show for the investment in infrastructure to eventually produce better outcomes from every capabilities that are built on top it - which is an argument that is convincing in the abstract, but it is difficult to compete with initiatives whose benefits have a greater impact and are easily visible.

I've presented that argument in many different organizational contexts and watched it perform or fail based on well-known reasons, so that I have a pretty clear idea of the elements that determine whether or not an organization finally tackles its data infrastructure issue or continues to defer it. The main difference is the leader, a specific individual who has the organizational credibility with a deep conviction about why the infrastructure is necessary, and the perseverance to push an argument until it becomes an actual priority instead of becoming a frequent item on the list of things everyone recognizes as important however, they never rise to the top. The leader must be able to pay for all the short-term costs of the infrastructure investment – the time, the disruption to current processes, and the absence any tangible outcomes - in the knowledge of the long-term capacity created by the investment will justify its cost many times over. What's required, at the end of the day is a culture which long-term infrastructure investment is valued and rewarded at the leadership level, not just mentioned in strategic documents, but not always prioritized when the quarterly resource allocation debate takes place. Making that change is, in itself an investment over the long term. But it is, in my view, one the most profitable investments that an organization that is serious about data-driven operations can make.}