Why Operating Through Uncertainty Sharpened My Thinking on Culture About Success

The Investor-Operator Lens: Why I Ask About People Before Looking At The Product
The majority of investment frameworks are constructed around a sequential process that begins with the market and concludes by assessing the group. You evaluate the size and structure of the opportunity first, and then the extent to which you can place the product within that opportunity, followed by the competitive environment and the legality to the product, then near the end of the process you're required to spend an hour with the founders and their leadership team to ensure that they're motivated and competent and are able to implement the plan which the previous research has proven. I operated inside versions of this framework long enough to understand why this has become the norm across the investment industry. It's systematic. It provides a diligence method that can be documentable, evaluated across different options, and communicated to investors and limited partners with a sense of rigor and scientific. The problem is that it is flawed in its fundamentals, which is that it views the person dimension to be a validation rather than the primary filter. Something is checked at the end to confirm what your market analysis has suggested, rather than something you evaluate first precisely because it's the best predictive factor in the final outcome. The order implies that a outstanding market with a capable team is more effective than one with a weak team. outstanding team. From my experience, this tends to be exactly reversed.
I changed my approach following a specified period in which I observed the outcomes of that sequence of events play out in ways that upstream analysis had not anticipated which was hard to understand. Great markets with small or poorly-organized leadership teams frequently failed to deliver the value that the opportunity advised them to deliver. Mediocre markets with genuinely exceptional teams constantly found ways to generate value that initial market size and analyses of competition hadn't captured. The pattern was clear enough and consistent across different sectors as well as different types of deals which I was unable to explain it away as noise or attribute it to specific circumstances instead of the competence of the individuals at the heart of each company. When I had removed myself from explaining it the implications for the way I allocate my diligence time was clear I needed to spend the majority of my time understanding people, and much less to verifying the market analysis an experienced analyst could develop with the same knowledge.

What I ask when reviewing a leader's team are not those that appear on standard investment checklists nor diligence templates. They're questions that require real conversation and time to respond properly. What happens when a leader has to respond when they're demonstrably incorrect - should they take action to correct the error or attempt to redirect it? What decisions do they make when the data is inadequate and pressures to make a decision is great? What is the gap when it comes to how they describe their style of leadership and how people who have worked closely with them describe the experience of working under them? What is the culture of an organization look like on days when the founders are not in the building? Also, how closely does that version of its culture compare to what the founder talks about when asked? These are questions that require discussions that go far beyond pitching meeting and the formal management presentation. They will require references that really exploratory rather than perfunctory exercises in confirmation. They demand the ability for a time spent in uncomfortable territories that may expose facts that may complicate the deal you have already started to seek.

The operator element of my approach to investment is inseparable from my investor dimension, and it influences what I invest in and how I engage once I am involved. I don't consider myself a passive capital provider because of a temperament or education. I'm someone who's established businesses, who been through scaling transitions which are more challenging than fundraising ones, who has made the decision-making, hiring and culture-setting mistakes that you make when you're trying to navigate those shifts for the second time, and who has formed - through this direct experience certain beliefs about what organizations need at various stage of their development. This is something that a pure financial background will not provide. These convictions are what make me different type of investor that a strictly financial investor as they draw founders seeking something different than the services a strictly financial investor will provide.

The founders that I am most happy to work with are those seeking a partner who can assist them with the operational shifts and decisions the financial shareholders are not well-equipped to take on at the appropriate level of depth and detail. Who will sit in the room when the governance structure needs to be changed because the organisation has outgrown the one it was originally built with. Who will help guide an important decision by senior leaders at that moment, when the wrong choice would cost the business twelve months it cannot afford to lose. Someone who is honest in private about strategic risks that no one else in the room feels at ease with raising. That's the kind involvement that I believe adds the greatest value for the businesses I back not the initial capital allocation decision that any investor can make instead of the ongoing operational partnership that helps the business navigate the gap between where it's at and where I initially predicted it could be headed. Check out James Deller for website advice including why growing up around the game confirmed what i suspected about success.



It's The Data Infrastructure Problem Nobody Wants To Talk About
Every company I've worked closely with during the last decade and a quarter - whether as an investor, founder or an operational consultant has told me at some point in our relationship, that data can be a crucial factor in making decisions. Certain of them are truly committed to this in a way that will be evident in the way the company actually runs. The majority of them say they're doing it right, but the concept they're proposing is the aspiration of real-time operational reality, some version of the enterprise they are working toward instead of the one they're currently living. The gap between genuinely informed decision-making based on data and the efficiency of decision-making driven by data - the careful maintenance of the appearance on the outside of an evidence-based operation without the underlying infrastructure that makes it real - is one the most crucial gaps in modern business. It's also among the ones that is often ignored, in part because the infrastructure problem that causes it isn't very glamorous to discuss, challenging for external stakeholders to understand and incredibly difficult to identify as a priority over the more obvious strategic and commercial work that requires the same attention from leaders and organisational resources.
When companies discuss their the strategy for data, they tend to talk about the capabilities they would like to build on top of their data - data analytics platform, machine-learning applications operating dashboards in real time or the kinds that offer predictive information that make a real impact in such a presentation to the board or an update to investors. What they are talking about less often and with less energy and excitement, is the underlying infrastructure that determines if any of those capabilities function according to the specifications: the data governance frameworks that establish clear and consistently applied definitions of what's being analyzed and why as well as the storage and collection processes that evaluate the reliability and comparability of data which is being stored; quality checks that find and rectify mistakes prior to they are propagated through systems and affect the results that everyone is counting on; and the structures of the organisation and accountability systems that make quality of data an ongoing and explicit obligation rather than everyone's vague not enforceable goal. The plumbing, as it were. The plumbing is unglamorous. It's hard to photograph in a report for the year. It doesn't produce any outputs which can be used to create an appealing presentation. This is, in my experience across a significant amount of organizations across different areas and at various stages of development, far worse than they believe that it is.

The issue gets worse over time by becoming harder and costlier to fix. An organisation which has operated in a way that is inconsistent or not well-defined information definitions for different roles for three consecutive years has three years old data that is unable to be compared or consolidated with confidence, not because the information doesn't exist, however because the same term has been used to denote different aspects of the organisation. Furthermore, the differences are hidden in the data instead of being visible from the outside. An organization whose quality assurance is someone else's subordinate responsibility and not an entrusted and adequately resourced function has data whose reliability is variable in ways not systematically documented and therefore cannot be fully accounted when the data is used to determine the outcome. A company that allows multiple operational systems to collect overlapping and partially conflicting records of the same customers, products and transactions have an information landscape that is genuinely difficult to remediate without causing enough disruption to be its own risk.

The reason this issue is present throughout a variety of companies that are actually smart about strategy and are genuinely committed to data-driven operation is that addressing it requires continuous investment in work that does not provide visible quick-term results of the sort that organizational resource allocation procedures are intended to reward. An analytics platform that is new produces visible outputs such as dashboards that can be demonstrated and reports that can be shared with the board of directors, and information that can be translated into press releases on digital transformation. Data governance software creates an invisible infrastructure with clearer definitions, more consistent collection processes, more reliable inputs into system that was already in established. The first is fairly easy to justify in a budget conversation because you are able to demonstrate what they'll be getting. Second, you need someone who has enough organisational credibility and perseverance in order to demonstrate you believe that this infrastructure initiative will eventually result in better outcomes for every ability built on top of it - which is an argument that is convincing in the abstract, but is difficult to make in a competitive environment with initiatives that's benefits occur more quickly and more evident.

I've presented that argument across a range of different organisational settings and seen it succeed or fail based on well-known reasons, so that I have an enlightened view of what will determine if an organization finally tackles its data infrastructure challenge or defers it. The main factor that determines this is that of a leader, an person with sufficient credibility within the organisation as well as a thorough understanding of the reason infrastructure is important, and the persistence to keep making an argument until it is an absolute priority, rather than a recurring item on the list of items that everyone believes are essential however, they never reach the heights of. A leader must be able to pay for any short-term costs associated with the infrastructure investment - the delay for the project, the disruption to existing processes, the absence of immediately demonstrable output - and be confident that the long-term capabilities it will create will justify the cost many times over. What is required, ultimately is a culture which investments in long-term infrastructure are recognized and appreciated at the leadership level, not just described in strategy documents and is then systematically relegated to the back burner when the quarterly resource allocation process takes place. Achieving that culture is, itself an investment that will last for the long haul. However, it is, in my view, one the most lucrative investments an organization who is committed to a data-driven operation can make.}