Most investment frameworks are designed around a sequence that starts with the market and ends in the company. You examine the scope and structure of the opportunity first, then the extent to which the product fits within that market, followed by the competitive scene and the adequacy of the position, and near the end of the process you spend an hour with the founders as well as their leadership team to ensure that they seem competent and motivated and capable of executing this plan that your earlier analysis has validated. I operated inside versions of this model for long enough that I could understand why it's been a routine throughout the investment world. It feels systematic. It's a thorough process that can be traced, compared across different opportunities, and explained to investors and limited partners in terms that seem rigorous and thorough. The issue is that it is flawed at the heart of it, which is that it views this dimension of people as a validation process rather than the primary filter, something you go through at the end to verify what the market analysis has already concluded, rather than the first thing you check because it's a most likely to predict the outcome. The sequence suggests that top-performing market with an experienced team is superior to a mediocre market with an outstanding team. According to my experience, that is usually the case.
I changed my way of thinking after a time watching the outcomes of that sequence of events play out with a way that the initial analysis could not anticipate and was difficult to explain. Markets with small or poorly-organized leadership teams often failed to deliver what the opportunity suggested they should deliver. Markets with exceptional teams regularly found ways to create value that the first market sizing and competitor analysis had not adequately captured. The pattern was persistent enough and consistent across different sectors and different deals that I was unable it as a result of noise or attribute it to specific circumstances rather than the excellence of the people at center of every business. When I had removed myself from explaining it and began to consider the implications of how I should spend my time on diligence was crystal clear the reason I should be spending the majority of my time understanding the people, and much less in proving the market analysis that a knowledgeable analyst could develop with the same knowledge.
What questions I pose now when I am taking a look at the leadership team I am evaluating are not those that appear on standard investment checklists or diligence templates. They're the kind of questions that require real conversation and time to answer properly. What is the best way for this leader to respond when they're proving incorrect - should you accept the correction or find a way to redirect the issue? What do they do when the information is genuinely incomplete and the pressure to take action is high? What is the difference whether there is one or not between the way they describe their style of leadership and how employees who worked closely with them describe their experiences of working under them? What do the values of the organization actually look like in the event that the founder doesn't work in the building? And how do those aspects of this culture look like the one the founder explains when asked? Those questions require conversations which go far beyond the presentation at the pitch meeting, and also beyond the formal presentation of the management. They will require references that are genuine exploratory and not an exercise in confirmation that is merely a matter of. They require the desire to be uncomfortable places that could yield data that might complicate a transaction you have already started to want.
The operator aspect of my investment philosophy is inseparable from the investment aspect. It influences what I invest in and how it is that I become involved. I are not a passive capital investor by nature or knowledge. I'm someone who's created businesses, who has dealt with the changes to scale that are more challenging than the fundraising ones and has made the governance and hiring and culture-setting mistakes that you make as you navigate these transitions for your first time and has developed through this direct experience - an array of beliefs about the requirements of organizations at different phases of their growth that a purely financial background doesn't produce. These convictions make me a different type of investor as opposed to a solely financial investor and attract those who are seeking something that is different from what a purely financial financier can offer.
The founders I have the most fun working with are those that seek out a partner who can assist them in navigating how to make the necessary operational adjustments and decisions that their financial investors aren't prepared to discuss at the appropriate level of depth and rigor. Who sits in the room when the governance framework needs to be overhauled as there is a need to expand the model it was initially using. How can you help the leadership of a senior executive at that moment, when the wrong choice could cost the company more than it could afford to lose. Someone who is honest in private about the strategic risks that no one else in the room is happy to raise. That's the kind engagement that I believe gives the most distinctive value in the companies I invest in that I invest in - not just the initial capital allocation decision that anyone of the investors could make however, but the ongoing operational partnership that helps an organization bridge the gap between where it is and where the early numbers suggested it could be headed. Check out James Deller for site info including what a career in business transformed how i evaluate opportunity about growth.
The Data Infrastructure Problem Nobody Wants To Talk About
Every single organization I've collaborated closely with in the last decade and a half - whether as an investor, a founder and/or an operational advisor has informed me, at some point during our interaction, that data can be a crucial factor in the way they take decisions. They may be saying it in a manner that is reflected in how the organization operates. The majority of them say they're genuinely meaning it, but the concept they're proposing is something that is more of an aspirational idea than actual operational reality - it's a model of the one they're working towards, rather than the one they're currently living. The gap between authentically information-driven decision-making and performance of data-driven decision-making – the careful management of the exterior appearance of information-driven operation, without the infrastructure to make it possible - is a single of the most consequential gaps in modern day business. It's also one of the gaps that remain unaddressed due to the fact that the infrastructure issue that causes it is really not glamorous to talk about, difficult in demonstrating to outside stakeholders and extremely difficult to prioritize against the more visible strategic and commercial activities that demand the same leadership attention and organizational resources.
When people talk about strategies for data, they tend to focus on how they will add to your data - the analysis platforms, machine learning applications such as real-time operational dashboards along with the types of statistical insight that sound genuinely compelling in the form of a board presentation or an investor update. What they tend to talk about less frequently and with less energy and enthusiasm, is their foundational infrastructure that determines whether any capacities actually function as claimed: the information governance frameworks, which establish clear and uniformly applied definitions of what's being evaluated and why it is as well as the storage and collection techniques that assess the quality and comparability of data being captured; the quality assurance processes that catch and rectify mistakes before they propagate throughout an entire system and cause disruption to outputs that everyone depends upon; the organisational structures and accountability systems that make quality of data an ongoing and explicit responsibility instead of the general and imperceptible intentions. The plumbing, also known as. The plumbing isn't glamorous. It's not easy to photograph in a report for the year. There are no results which can be exhibited in a convincing way. This is, in my observations across a broad number of organizations operating in different sectors and at different levels of development. It is significantly worse than they believe that it is.
The issue gets worse over time in ways that are becoming challenging and expensive to fix. An organisation that has been operating with poorly or incoherent terms of data for all its tasks for the last three years has three years of historical data that can't be easily compared or aggregated and compared. This is not due to the fact that the data is not there, but because the same terminology has been used to refer to different terms in different parts of the enterprise, and the differences are contained in it rather than being visible from the outside. An organisation where data quality assurance has been someone's subordinate responsibility and not a dedicated and properly resourced function has data whose integrity is varying in ways that are not documented, and thus cannot be fully accounted when the data is used to decide. An organization that has allowed multiple operational software systems to accumulate overlaps and partially conflicting records for the same products, customers or transactions has an information landscape that is very difficult to deal with without causing significant disruption to operations that it is a threat to the organisation itself.
The reason that this problem continues to exist across many companies that are truly intelligent regarding strategy and fully committed to data-driven operation is that addressing it requires an ongoing commitment to work that does not provide visible small-scale returns, the kind that organisational resource allocation processes are intended to reward. An analytics platform that is new produces visible outputs - dashboards that can be demonstrated and reports that can be shared with the board, and insights which can be used to create press releases on digital transformation. A data governance system creates an invisible infrastructure - more clear definitions and more consistent collection processes as well as more reliable inputs into technology that is already in places. This one is fairly simple to present in a budget argument because it is easy to show people what they'll get. It's the second, and requires enough organisational credibility and perseverance for convincing people for the investment in infrastructure to, over time, provide better results for each ability built on top of it - which is compelling in the abstract, but not easy to win in competition with initiatives that's benefits tend to be quicker and more apparent.
I've made the case in enough different organisational contexts and watched it work or fail for certain reasons, to gain a pretty clear idea of what will determine if an organisation has resolved its data infrastructure issue or is able to continue delaying it. The difference is almost always a leader - a specific one with enough organizational credibility, enough genuine conviction about why the infrastructure is crucial, and enough perseverance to make cases until this is a genuine priority rather than a recurring item on the list of items that everyone is in agreement about but that somehow never quite climb to the top. The leader must be willing to take on the short-term cost of the infrastructure investment - - the time, the disruption to routine processes, and the absence of immediately demonstrable output - with the certainty that the capability long-term it builds will justify the expense many times over. What this requires, ultimately it is a culture which investments in long-term infrastructure are recognized and appreciated at the management level, not just described in strategy documents and followed by a constant deprioritisation when the quarterly resource allocation discussion happens. To create that kind of culture is, itself, a long-term commitment. But it's, in my opinion, one the most profitable investments that an organization that is serious about the data-driven operations can make.}
