Why Developing People at Scale Reinforced Operational Discipline About Results

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What Has A Football Dressing Room I Learned About Building An High-Performance Technology Team
I grew up playing professionals in a way that afforded me access to areas that the majority of people have only learned about. Training grounds. Dressing rooms. The conversations that happen between players and coaching staff after a game, when the media and cameras are gone, and the official version of what transpired has already been recorded. Although I wasn't a participant myself - my entry into that world came through those around the game rather than the actual game itself. But I was on the right side of it and for long enough, to learn something valuable about the ways that high-performance environments work by removing the mythology that surrounds them. The one thing I understood most immediately was this: teams that consistently surpassed their resources and requirements were not the ones having the most individual strengths on paper. They're the teams that found out how to create an environment where its members truly wanted to be a part of each others - not to earn financial gain, nor for individual acknowledgment, but simply because the collective had meaning and a sense of community that made personal sacrifice feel important rather than only a necessary obligation.
This statement is evident as you explain it clearly. Teams are of course more effective with people who trust one another and are able to believe in the same mission. However, the practical implications of this fact are not as obvious and can be where a lot of companies - businesses in the field of technology and football alike - often get into trouble. Establishing a culture in which people really want to be a part of each other is not something you can mandate from the top down or create as a policy or articulate in a set of corporate values and expect to see it manifest. It has to be earned over time, by consistency in the behaviour of leaders - particularly in the moments that don't get a lot of attention - and by the careful oversight of the numerous small decisions that collectively inform everyone involved in the company the values that are actually valued, what is actually tolerated and what happens when the stated values and the personally or commercially feasible option do not agree. In the top football environments I have been in, these tiny decisions were handled with exceptional care by the best coaches. What they did when an older player made an error that could have been avoided during training. Which disciplinary criteria applied to the twenty-year veteran is in fact similar to standards applied to eighteen-year-old on the edge of the team. How the team responded when someone was struggling with the aftermath of a personal crisis outside the field. None of those choices appear in a team's results on any given Saturday. All of them throughout the year, determine what the team's performance falls above but falls short of the ceiling.

When I founded 1Touch and subsequently built different organizations, one factors I was the least determined about was to recreate - within a technology workplace context - that same quality of atmosphere I'd witnessed in the most prestigious football venues I had been a close associate of. In a way, but not literally because startups in the field of technology are not a football team, and the analogy breaks down quickly when you overdo it. But on the basis of operational principles, the lessons were implemented with astonishing fidelity. The first principle was that standards have been applied consistently regardless of rank or unassailability. The most comfortable dressing rooms I had been in were those that had a professional and behavioural standards expected of the youngest player in the squad were in fact the same as those to be expected of the top-earning, most experienced player. Not because the organisation could not have afforded the luxury of making exceptions, however simply because the entire dressing room was constantly watching to see whether exceptions would be made. And the response to that question showed them everything they needed know about whether the stated values of the company were genuinely true or merely cosmetic.

The second lesson dealt with how organisations deal with failure and the distinction between accountability and punishment. In the environments where participants developed rapidly were not necessarily those in which mistakes were punished the most severely or in the most public manner. They were the ones in which mistakes were examined with the utmost honesty while discussing what had gone wrong was focused and constructive, rather than general or distributing blame. The lesson learned was shared throughout the group rather than held against the person who made the error. Accountability is the ability to be clear about who was at fault, the reason it went wrong and the changes that occurred that resulted from it. Punishment involves distributing blame in an approach that causes people to be anxious and cautious, and more concerned with their safety than in achieving their goals. The first builds the capacity of an organisation. The second builds a culture where individuals manage their exposure rather than committing fully to the cause, and this distinction manifests in technology firms, and has the identical results it has with football players.

The final point is most difficult for me to comprehend. longest to express clearly, but which I think to be the most important of all The most successful environments I observed were those in which the growth that a person had was considered equally as the growth of the athlete. The most effective coaches weren't only educating players on how to play football. They were also teaching them how to think under pressure, how to communicate clearly in high-risk situations, how to bounce back from setbacks with out being discouraged, and to be the person that a team with a high performance has its players be. It was an investment in the complete growth of an individual rather than just in the technical skills that the organization immediately needed, was not charity. They were the single most efficient way to improve performance over the long term for the clubs. It is, as I see it, the most efficient long-term performance plan that is available to all organizations that are serious about building something genuinely lasting, rather than simply impressive in the short run. See the James Deller for more info including what operating at scale changed my approach about growth.



It's The Data Infrastructure Problem Nobody Wants To Discuss
Every organization I've worked closely with during the last decade and a quarter - whether as an investor, founder or as an operational advisor I've been told, at some point during our interaction, that data can be a crucial factor in how they make decisions. Certain of them are truly committed to it in a way that is apparent in the way their organization actually operates. Many of them believe that they're really saying that, but the concept they're proposing is something that is more of an aspirational idea than real-time operational reality, an idea of the kind of organization they're creating and not the one that they currently exist in. The gap between authentically information-driven decision-making and performance of data-driven decision-making - the careful management of the external appearance of evidence-based operations without the infrastructure that could make it actual - is among the most serious gaps that exist in modern-day business. It's also among the gaps that remain unaddressed due to it is a problem with infrastructure that it to be incredibly unattractive to discuss, challenging to demonstrate to external stakeholders, and enormously difficult to prioritise against the more obvious strategic and commercial work that competes for the same attention from leaders as well as organisational resources.
When organisations talk about data strategy, they typically tend to talk about what capabilities they'd like to develop on top of their data: the analytical platforms, machine-learning applications, the real-time operational dashboards or the kinds that offer predictive information that sounds truly compelling in the context of a board meeting or an update to investors. What they talk about considerably less frequently and with a lot less energy and enthusiasm, is their foundational infrastructure that determines if all of those capabilities are actually working according to the specifications: the data governance frameworks that establish clear and consistently applied definitions of what's being analyzed and why, the data collection and storage processes that evaluate the reliability and comparability of the data being recorded; the quality control processes that identify and rectify errors before they spread across your system and destroy the data outputs which everyone is relying on; and the organisational structures and accountability processes that make data quality an ongoing and explicit obligation rather than everyone's vague unenforceable goals. The plumbing, or the. It is not glamorous. It's difficult to photograph to be used in an annual report. It is not producing outputs that can be showcased in a convincing presentation. And it is, in my observations across a broad variety of companies operating in diverse industries and at different levels of development, significantly more difficult than the company believes it to be.

The issue increases over time as it becomes complicated and costly to reverse. A company which has operated with poorly or incoherent data definitions for its various functions for three years has three years of historical data that are unable to be safely compared, or aggregated - not because the data isn't there, but because the same term has been used for different things across different areas of the company, and the distinctions are embedded within the data itself, instead of being visible on the surface. A business whose quality assurance has been the responsibility of a peripheral responsibility rather than a specialized and properly resourced task has data that's integrity has a range of variations that are not documented, and thus can't be properly accounted for when the data is used when making decisions. An organization that has allowed multiple operational systems to accumulate redundant and slightly conflicting accounts of the same products, customers and transactions have an unresolved data landscape that is truly difficult to rectify without major disruptions to operations that it is a threat to the organisation itself.

The reason this issue continues to be a problem across a wide range of organizations that are really smart about strategy and truly determined to implement a data-driven strategy is because addressing it requires long-term commitment to work that doesn't yield tangible immediate returns like those that allocation of resources processes are intended to reward. The new analytics platform can produce tangible outputs, such as dashboards that can be shown or reports that could be shared with the board, insights that can be translated into press releases about digital transformation. Data governance software creates transparent infrastructure - better definitions with more consistent collection procedures and more reliable inputs into the systems that are already in existence. The first is relatively easy to justify in a budgeting conversation as you can explain what they'll receive. Second, you need someone who has enough credibility in the organisation and patience to show of how the capital investment is going to, over time, result in better outcomes for every ability built on top of it - which is a compelling argument in the abstract, but can be difficult to compete with initiatives that have benefits that appear to be immediate, and more obvious.

I've made that argument in numerous different contexts within the organisation and watched it perform or fail for well-known reasons, so that I have the most precise understanding about what makes an organisation is able to address its infrastructure issues with regards to data or simply defers it. The difference is almost always the leader, a specific person who has sufficient organizational credibility and an knowledge of the reasons why infrastructure matters, and enough determination to persist in making claims until they is a genuine priority rather than being a regular item on the list of things everyone agrees are important yet never make it to the top. That leader has to be willing to bear this short-term cost associated with infrastructure investment: the amount of time for the project, the disruption to existing processes, or the absence of immediately demonstrable output - in the confidence that the capability it creates will justify that expense many times over. What's needed, in the end is a culture where investment in long-term infrastructure investments are recognized and appreciated at the executive level, not simply described in strategy documents and is then systematically relegated to the back burner when the quarterly allocation of resources occurs. Achieving that culture is, itself considered a long-term investment. But it's, in my opinion, one of the most rewarding investments an organization who is serious about a data-driven operations can make.}