How Thinking Like an Operator Taught Me About Scale

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The Investor-Operator Lens How I Ask About People Before I Look At The Product
The majority of investment frameworks are constructed around a pattern that starts with the market, and then ends by assessing the group. You assess the size and structure of the potential first, then the degree to which the product is compatible with that opportunity, then the competitive landscape and the viability of the position, and toward the conclusion this process, you'll take an hour with the founders and their management team to ensure they seem competent and motivated and able of carrying out what the earlier analysis has validated. I worked within versions of this framework for long enough to see why it has become standard practice throughout the investment industry. It's systematic. It produces a diligence process that can be tracked, compared to other opportunities, as well as defended in front of the investment committees or limited partners in terms that feel rigorous and analytical. The issue is that it has a structural flaw at its heart, which is that it views this dimension of people as a validation process instead of a primary filter - something you do at the very end to confirm what your market analysis has already indicated instead of one you consider first because it is the most influential factor in the end result. The method suggests that a fantastic market with an excellent team is more effective than an average market that has an extraordinary team. According to my experience, that is often exactly backwards.
My approach changed after a specific period where I witnessed the results that standard sequence play out in ways that the downstream analysis hadn't anticipated and couldn't easily explain. Markets that had leaders who were weak or poorly organized typically did not perform as the opportunity advised them to deliver. Mediocre markets with genuinely exceptional teams always managed to create value that the initial market size estimation and analyses of competition hadn't captured. It was a pattern that was persistent and consistent across different sectors as well as different types of deals which I was unable to explain it as a blip or attribute it to specific circumstances and not to the skills of the people at the center of every business. Once I quit arguing about it, the implication for the way I allocate my time to diligence was clear The point was that I ought to be focusing considerable time understanding the person, and less on confirming the market analysis that a competent analyst can develop with the same knowledge.

The questions I have to ask when analysing a leadership team not the types of questions you find in the typical investment checklists and diligence templates. They are questions that require real dialogue and time to properly answer. How does this leader actually respond when they're proven incorrect about something? Do they make amends or come up with a solution to redirect it? What decisions do they make when the information is genuinely incomplete and the pressure to take action is intense? What is the difference there is between how they describe their leadership style as well as how people who worked closely with them describe the experience of working under them? What does the overall culture of the organization actually look like on days when there is no founder in the building? And, how closely does that aspect of the culture match up to the one the founder is describing when asked? This kind of question requires conversations which go far beyond the pitch meeting and the formal management presentation. They are a requirement for reference checks that are genuinely exploratory rather than routine exercises for confirmation. They need the will to travel into uneasy locations that may uncover facts that may complicate the deal that you've already started with.

The operator dimension of my investment philosophy is inseparable from the investors' dimension. It shapes both what I invest in and the way I get involved. I am not a passive source by birth or knowledge. I'm someone who's created businesses, who has faced the transitions of scaling which are more challenging than fundraising ones which is why I've made the hire and governance mistakes that you make when navigating those transitions for the first time, and who has cultivated - based on that personal experience - several convictions regarding what organisations need at different stages of their growth and that a strictly financial background does not give. These convictions are what make me distinct type of investment partner as opposed to a solely financial investor, and they attract founders looking for something different than the type of financial investment that only a purely financial one can offer.

The founders I do my best work with are the ones that seek out a partner who can help them think through the operational transitions and decisions in which the investors of their company are not capable of dealing with in the right amount of depth and specificity. Are you able to be in the room when the governance structure has to be revamped because an organization has grown beyond the one it started with. Who can assist in navigating the senior decision-making process at time when a bad choice could cost the business twelve months it cannot afford to lose. Who is honest regarding strategic risks that nobody else in the room is willing to discuss. That's the kind of participation that I think creates the greatest value for the businesses I back Not the first capital allocation decision that any investor can make however, but the ongoing operational partnership that helps your company to bridge the gap between where it stands and where the early numbers suggested it could be headed. Check out James Deller for website tips including how building in stealth transformed how i evaluate opportunity about growth.



There's A Data Infrastructure Problem Nobody Wants To Discuss
Each company I've been closely with over the past decade and a quarter - whether as an investor, a founder, or an operational advisor has told me at some point in our interaction, that data can be a crucial factor in making decisions. Some of them genuinely mean it in a manner that manifests in how the company actually runs. A majority of them believe they're really saying that, but they're describing more of an aspiration than being a reality in operation - the version of the company they're striving to achieve instead of the one they live in. The gap between true data-driven decision-making as well as the effectiveness of data-driven decisions - the careful management of what appears to be evidence-based processes without the infrastructure that would make it tangible - is one the most important gaps within modern business. It's also among the areas that remain unaddressed in part due to the infrastructure issue behind it to be incredibly unattractive to talk about, difficult to demonstrate to external stakeholders and extremely difficult to rank against the more prominent strategic and commercial jobs that vie for the same attention of leaders and organizational resources.
When organisations talk about strategies for data, they tend to discuss how they will create on top of their data, such as analysis platforms, machine learning applications, the real-time operational dashboards, the kinds of predictive insights that sound really compelling in presentations for boards or in an update to investors. What they tend to talk about less often, and with considerably less energy and energy, is the infrastructure that determines if all of these capabilities work in the way they're advertised: data governance frameworks that establish explicit and consistently interpreted definitions of what is being analyzed and why what is being measured; the collection and retention methods that establish the accuracy and comparability of data in the process of being collected; quality assurance processes that identify and correct errors before they spread across data systems and corrupt outputs everyone relies on; the organisational structures and accountability processes that make data quality someone's explicit and ongoing responsibility instead of relying on everyone's vague and imperceptible intentions. The plumbing, also known as. The plumbing isn't glamorous. It's not an easy thing to photograph to be used in an annual report. It is not producing outputs that can be showcased in a compelling presentation. And, in my experience across a significant variety of companies operating in diverse sectors and at different stages in their development, considerably worse that what the organization perceives it to be.

The issue increases over time in ways that become progressively harder and costlier to rectify. An organisation which has operated without a clear or consistent set of data definitions for its various roles for three consecutive years has three years in historical data which cannot be reliably aggregated or compared it is not because the data does not exist, rather because the same term has been used to refer to various things in different areas of the enterprise, and those differences are built into the data itself, instead of appearing on the surface. A company whose data quality assurance was a personal responsibility, rather than having a properly resourced and dedicated function has data whose integrity differs in ways that are not documented regularly and is not systematically considered when the data is used to take decisions. An organisation that has allowed multiple operational systems to create overlapping and partial conflicting records on the same products, customers and transactions have a data landscape that's hard to clean up without operational disruption significant enough to pose a risk for the organization itself.

The reason why this problem is recurrent across a wide range of organizations which are truly smart about strategy and truly dedicated to a data-driven approach to business is because solving it requires constant investment in work that isn't producing visible immediate returns like those that resource allocation processes in organizations are designed to reward. A new analytics platform provides tangible outputs - dashboards, which are easily demonstrated as well as reports that are shared with the board and also insights that can be translated into press releases regarding digital transformation. A data governance plan creates invisible infrastructure: clearer underlying definitions that are more consistent with the collection process with more stable inputs into the systems that are already in place. The first is relatively easy to present in a budget argument because it is easy to show people what they'll get. The second one requires sufficient organisational credibility and patience to prove that the infrastructure investment will, over time, bring better results from every capabilities that are built on top it. It's a convincing argument in abstract but a difficult one to win in competition with initiatives that have benefits that seem to have more direct and more obvious.

I've made that argument across a range of different organisational settings and witnessed it be successful or fail for well-known reasons, so that I have an understanding of the factors that determine whether an organization finally tackles its data infrastructure problem or is able to continue delaying it. It is generally at the level of a leader. It's an one with enough organizational credibility and a clear conviction about why the infrastructure is critical, as well as the determination to continue making the case until it is an actual priority instead of becoming a routine item on the list of things that everyone recognizes as important but that somehow never quite reach the heights of. The leader must be willing to absorb all the short-term costs of the infrastructure investment - the time it takes to complete, the disruption to processes that are already in place, the absence evidence of output immediately measurable - in the confidence that the long-term capability it builds will justify the investment several times more. What is required, ultimately it is a culture which long-term investment in infrastructure is respected and acknowledged at the executive level, not simply identified in strategy documents regularly discarded during the quarterly resource allocation process happens. To create that kind of culture is, itself an investment in the long run. However, it is, in my opinion, one of the most profitable investments that an organization that is serious about the data-driven operation could make.}