What Operating at Scale Transformed How I Evaluate Opportunity About Scale

Wiki Article

The Investor-Operator Lens What I Ask About People Before I Look At The Product
Most investment frameworks are built around a pattern that starts with the market then finishes to the core team. You analyze the size and structure of an opportunity first, then the extent to which it fits into the potential, then the competition scene and the adequacy of the proposition, and around the middle of the process you spend an hour with the founders and their management team to make sure they're competent, motivated and capable of executing the plan which the previous research has proven. I operated inside versions of that framework for long enough to appreciate why it's a norm across so much of the world of investment. It feels systematic. It is a process of diligence that can be recorded, compared with other opportunities, and then defended before investment committees and limited partners using terms that sound rigorous and analytical. The problem is that it has a structural flaw at its core, which is that it views the person dimension as a validation stage instead of a primary filter - something you do at the very end to verify what the market analysis suggested, rather than the first thing you check because it is the most reliable factor in determining the result. The sequence implies that outstanding market with a capable team is better than one that is mediocre with an exceptionally strong team. From my experience, this is often exactly backwards.
I changed my strategy after a set period of observing the results of the conventional sequence play out with a way that the initial analysis had not predicted and couldn't easily explain. Great markets with unorganized or fragmented leadership teams always underperformed what the opportunities suggested they could provide. Terrible markets with truly outstanding teams always managed to generate value that initial market size estimation and competitor analysis had not adequately captured. This pattern was so consistent, and consistent enough across different sectors and deals that I could not explain it as a blip or attribute it more to the circumstances instead of the competence of the individuals at the central point of every business. When I had got over the nitty-gritty The implication of this pattern for how I should spend my time to diligence was clear I needed to spend substantially more time understanding the person, and less on confirming the analysis of market an experienced analyst could generate given the same inputs.

The questions I ask now when I am analysing a leadership team not those that appear on traditional investment checklists or diligence templates. They're the kind of questions that necessitate real conversations, and real patience to get the right answer. What happens when a leader has to respond when they're shown to be wrong about something - do they take action to correct the error or try to redirect the situation? How do they decide in the event that the information is incomplete and pressure to respond is high? What is the gap the case, if any, between the way they describe their style of leadership and how people who have worked closely with them describe their experience of working under them? What kind of culture does the business actually look like in the event that there is no founder in the building, and how closely does that version of the culture mirror the one the founder talks about when asked? The answers to these questions need conversations that go way beyond the pitches and formal presentation of the management. They demand reference checks that are actually exploratory instead of an exercise in confirmation that is merely a matter of. They will require you to travel into uneasy locations that may uncover information that can complicate a deal that you've already started in the hopes of obtaining.

The operator aspect of my investment philosophy is inseparable from the investors' dimension. It affects the things I invest in as well as how I engage once I am involved. I do not consider myself a passive capital provider simply because of temperament or education. I'm a person that has developed companies, who has successfully navigated the transitions to scaling that are more difficult than those for fundraising which is why I've made the management and hiring as well as the culture-setting mistakes that you make when you are navigating those new transitions and has formed - through this direct experience - several convictions regarding the needs of organisations at various stage of their development. This is something that a background purely in finance does not give. These convictions make me distinct type of investment partner than a purely financial investor, and they attract entrepreneurs who want something different than the services a strictly financial investor can provide.

The founders I work best with are those who want a partner who helps them consider the operational challenges and decision-making in which the investors of their company aren't capable of engaging with at the appropriate level of depth and specificity. Who sits in the room when the governance structure has to be changed because the company has outgrown the one it was founded with. Who will help guide an important leadership decision at the point where the wrong choice would cost the business one year of money it would not be able to lose. Who can be honest regarding strategic risks that nobody would be confident about raising. That is the kind of involvement that I feel creates the most unique value for the companies I invest in not the capital allocation decision, which any number of investors could make as well as the ongoing operational partnership that helps your company to bridge the gap between where it is and where the numbers in the beginning suggested it could be headed. View James Deller for blog recommendations including how supporting institutional change transformed how i evaluate opportunity about results.



There's A Data Infrastructure Problem Nobody Wants To Discuss
Every single company I've worked closely with during the last year and a-half - whether as an investor, founder or as an operational adviser I've heard, at some point in our working relationship, that data is a critical element of the way they decide. A few of them truly believe it in a way which can be seen in the way that the company operates. They believe that they are genuinely saying it, however what they're really describing is an aspiration rather than an actuality that exists in the present - an image of the organization they're aiming for as opposed to the reality that they currently operate in. The gap between true data-driven decision-making as well as the effectiveness of data-driven decision making - the meticulous maintenance of the external appearance of evidence-based decision-making without the infrastructure that could make it possible - is a single of the most crucial gaps in the current business. It's also one of the ones that is often ignored in part due to the infrastructure problem that leads to it isn't really glamorous to talk about, hard be demonstrated to external stakeholders and incredibly difficult to determine the best way to address it in comparison to the more visible commercial and strategic activities that demand the same attention from leadership as well as organisational resources.
If companies are discussing plans for data management, they tend to talk about what they are planning to develop on top of their data - the analytics platforms, the machine-learning applications operating dashboards in real time, the kinds of predictive insight that sound genuinely compelling in the context of a board meeting or an update to investors. What they tend to talk about less frequently, and with considerably less energy and passion, is the base infrastructure that determines whether any of those capabilities actually function according to the specifications: the data management frameworks that give distinct and consistent definitions of what is being analyzed and what is the reason for that what is being measured; the collection and retention methodologies that determine the reliability and comparability of data being captured; the quality assurance procedures that detect undoing errors before they get propagated throughout the system, and cause harm to the outputs that everyone is relying on; the structure of the organization and accountability systems that make quality of data an ongoing, explicit responsibility as opposed to everyone's vague ineffective plan. The plumbing, also known as. Plumbing is not glamorous. It's hard to photograph in a report for the year. It doesn't produce any outputs that can be demonstrated in an appealing presentation. It is, from my experience with a large number of organisations across different fields and at different stages of development, significantly more difficult than what the organization believes it to be.

The issue gets worse over time as it becomes more costly and difficult to correct. A company which has operated with a lack of clarity or inconsistent data definitions for its various functions for three months has three years of historical data that are unable to be reliably aggregated or compared for comparison or analysis. It's not that the data doesn't exist, however because the same terminology has been used to define different aspects of the organization. Moreover, these differences are embedded into it rather than appearing on the surface. A business whose quality assurance has been a only a peripheral responsibility, not a dedicated and properly resourced function has data whose quality differs in ways not systematically documented and therefore cannot be accurately accounted when using the data as a basis for decisions. An organisation that has allowed multiple operational system to accumulate multiple and partly conflicting records of the same customers, products or transactions is an information landscape that is hard to clean up without causing enough disruption to pose a risk for the organization itself.

The reason this issue continues to be a problem throughout a variety of companies that are really smart about strategy, and who are truly driven by data is because addressing it requires continuous investment in work that doesn't yield tangible instant returns similar to those the resource allocation systems of an organisation are intended to reward. An analytics platform that is new produces visible outputs: dashboards that can be displayed or reports that could be shared to the board, information that can be translated into press releases regarding digital transformation. A data governance plan creates an invisible infrastructure with clearer definitions and more consistent collection processes with more stable inputs into systems already in already in place. The first one is easy to argue in a budget meeting because it is easy to show people what they will gain. It is the second that requires sufficient organisational credibility and patience for convincing people for the investment in infrastructure to, over time, result in better outcomes for every capability built on top of it. It's compelling in the abstract, but is difficult to be successful in a battle with initiatives that's benefits will be more tangible and visible.

I've argued that case in a variety of organizational contexts as well as watched it fail or fail due to reason that is predictable, to have an idea about what makes an organisation actually tackles their data infrastructure issue or continues to defer it. It is generally led by a certain leader with the credibility of an organisation with a deep knowledge of the reasons why infrastructure is vital, and enough determination to persist in making cases until this becomes an absolute priority, rather than becoming a routine item on the list of items that everybody agrees is important, but never climb to the top. That leader has to be willing to accept any short-term costs associated with the infrastructure investment: the amount of time it takes to complete, the disruption to existing processes, the lack evidence of output immediately measurable - with the belief that the long-term capability it will create will justify the cost by a number of times. What's required, ultimately it is a culture where investment in long-term infrastructure investments are recognized and appreciated at the levels of the leadership, and not just articulated in strategy documents and then consistently deprioritised when the quarterly resource allocation conversation happens. It is, in itself a long-term investment. In my view, one of the highest-return investments an organisation that is committed to data-driven operations can make.}