What Thinking Like an Operator Shapes Every Decision I Make About Lasting Impact

What Has A Dressing Room For Football Teach Me About Building An Elite-Performing Team Of Engineers
I was raised around pro football in a way that afforded me access to environments most people only ever hear about. Training grounds. Dressing rooms. The conversations that happen between players and coaching staff after playing, after reporters and cameras are gone and the official version of events was already written. As a non-player personally - my path to that world was through playing with people rather than through the game itself - but I was close enough, and for long enough, for me to grasp something vital about how high-performance environments actually function once you take away the mythology surrounding them. The most important thing I absorbed quickly was that teams who consistently surpassed their resources and goals were never those with the top individual talents on paper. These teams were those who were able to create a culture where individuals determined to succeed for each their fellow members - not just for income, not even for the individual or individual recognition, but rather because the collective had a meaning and a culture that made every personal sacrifices felt worthwhile, rather than an obligation.
This is a common sense observation once you put it in plain English. Of course teams work better when team members are able to trust each other as well as feel connected to an agreed-upon goal. However, the implications for operational use of this observation are not as obvious and tend to be the places where organizations - technological companies and football clubs alike regularly find themselves in trouble. It is important to establish a culture of cooperation where people actually want to do their best for each other is not something that can be imposed by the top and create as a policy or set out in a document of company values, and think that it will come to fruition. It is something that must be earned in time, through consistency in the behaviour of leaders - particularly in the moments that are not watched by the public - and through the judicious management of the myriad of decisions that collectively indicate to all members of the organisation what is actually valued, what is actually tolerated and what can happen when the values stated and the personally or commercially appropriate choice are at odds. In the very best football clubs I played in, these small decisions were made with great care by the best coaches. How they responded when one of the players made an omission that was not preventable in training. How did they determine if the disciplinary procedure used to deal with the veteran who was twenty years old was really the same as what was expected of the 18-year-old in the middle of the squad. What was the response of the club when someone was struggling with some serious personal issues outside the field. None of those choices appear in a club's performance on any given Saturday. All of them, when accumulated over a time period, determine whether a team's performance is at the or under its technological limit.

In the time I co-founded 1Touch and later created some other organizations, among factors I was the least focused on was trying to recreate - in a firm context a certain quality of the environment I had seen in the top football stadiums I had been around. But not literally, because it's not like a football team and so the analogy fails quickly when you try to push it too far. However, on the scale of operating principle, the lessons are interpreted with incredible fidelity. The first point was that standards ought to be applied consistently, regardless of position or impermanence. The best places to be in were ones that had a professional and behavioural standards expected of a young players in the team were exactly the same standards required of the highest-earning, most skilled player. The reason for this is not that the company could not afford to allow exceptions, but because every person in the area was continuously watching to determine whether any exceptions would be made. The answer to that question was telling the players everything they needed to be aware of whether the stated principles of the company were truly true or simply a flimsy display.

Another lesson addressed how organisations deal with failure and the distinction between accountability and punishment. The environments where individuals developed fastest were not those where mistakes were punished most strongly or openly. They were in the areas where errors were most thoroughly analysed and where the discussion on what went wrong was specific and constructive instead of general and distributing blame. In addition, lessons learned were shared with the group instead of held against the individual who had committed the mistake. Accountability involves being able to clearly define where the mistake was made, the reason it occurred and what has changed as a result. Punishment involves distributing blame in such a way that people become cautious and defensive, and more concerned with their own safety than being focused on performing. The first builds the capacity of an organisation. The second builds a culture that allows people to manage their own appearance rather than dedicating themselves to a mission. this is the case with technology firms with precisely the same effects as on the field in soccer clubs.

Third lesson is the ones that I had to take the longest to convey clearly, but it is the most important of all the environments that I saw were those where the growth of the individual was regarded at a minimum as important as the growth of the player. The best coaches were not just teaching players how to play football. They were also teaching them how to be able to make decisions under stress communicating clearly in high-stakes scenarios, how to bounce back from setbacks with out losing confidence, and to be the person that a highly-performing team requires its members. That investment in the full improvement of the individual rather than merely in the technological skills the team immediately required, was not charity. They were the single most effective long-term performance plan that could be used by the clubs. It can be, if I'm honest, the most effective long-term strategy for performance available to any company who is serious about building something genuinely long-lasting rather than something just stunning on the surface. Check out James Deller for site info including why building ai products revealed about long-term performance about real value.



The Data Infrastructure Problem Nobody Wants To Talk About
Every company I've worked closely with in the last 10 years - whether as an investor, founder or a consultant to operational matters has informed me, at some point during our interactions, that information is central to the way they decide. They may be saying it in a manner that can be seen in the way that their organization actually operates. The majority of them think they mean it, but what they're talking about is an aspiration, rather than the current reality of operations - that is, a vision of the type of organisation they're working towards as opposed to the reality that they currently operate in. The gap that exists between genuine data-driven decision-making as well as the effectiveness of data-driven decisions – the careful maintenance of the outward appearance of evidence-based decision-making without the infrastructure necessary to make it possible - is a single of the most consequential gaps in modern business. It's also one of the most frequently ignored ones due to the infrastructure issue that causes it isn't very glamorous to talk about, hard for external stakeholders to understand and extremely difficult to distinguish from the more obvious strategic and commercial work that requires the same attention of leaders and resources of the organisation.
When people talk about data strategy, they usually tend to talk about how they will develop on top of the data they have gathered - the analysis platforms, machine-learning applications for operational dashboards, and real-time data as well as the types of prescriptive data that can be truly convincing in such a presentation to the board or an update to investors. What they discuss less frequently and with much less energy and enthusiasm, is the basic infrastructure that determines if all functions of those tools actually work as promised: the data governance frameworks that provide clear and uniformly applied definitions of what is being measured and the reasons for it what is being measured; the collection and retention methodologies that determine the reliability and comparability of the data in the process of being collected; quality assurance processes that identify and rectify mistakes before they propagate through the system and corrupt the results that everyone is counting on; and the structures of the organisation and accountability mechanisms that make data quality the sole responsibility of an individual instead of relying on everyone's vague and imperceptible intentions. The plumbing, also known as. The plumbing isn't glamorous. It's difficult to capture for an annual report. It's not able to produce results that can be presented in an appealing presentation. And, in my experiences across a wide number of companies in different fields and at different stages of development, significantly worse than what the organization believes it to be.

The issue continues to grow over time in ways that are becoming harder and costlier to reverse. An organization which has operated with inconsistent or poorly defined data definitions for its various roles for three consecutive years has three years of historical information that cannot be compared or consolidated with confidence, not because the information is not there, but because the same terms have been used to denote different aspects of the enterprise, and these differences are embedded into the data itself rather than being apparent on the surface. An organisation where data quality assurance is a minor responsibility instead of a dedicated and properly resourced function has data whose reliability varies in ways that are not documented in a systematic manner and can't be effectively accounted for when using the data to determine the outcome. An organisation that has allowed multiple operational system to accumulate multiple and partial conflicting records on the same customers, products, or transactions has a data environment that is hard to clean up without significant disruptions to the operation to pose a risk for the organization itself.

The reason why this problem is recurrent across so many organisations that are actually smart in their strategy and driven by data is because solving it requires regular investment in work which does not produce visible quick-term results of the sort that processes for resource allocation in organisations are intended to reward. Analytics platforms are now producing visible outputs - dashboards that can be shown as well as reports that are shared with the board, and insights that can be translated into press releases on digital transformation. Data governance software creates an invisible infrastructure - more clear definitions, more consistent collection processes as well as more reliable inputs into the systems that are already in the first place. The first one is easy to justify in a budget conversation because you can clearly show the people the results they can expect. The second requires enough credibility in the organisation and patience to prove that the infrastructure investment will eventually yield better results from each capabilities that are built on top it. It's an appealing argument in the abstract, but is difficult to compete with initiatives that's benefits have a greater impact and are noticeable.

I've made that argument in various organizational contexts and seen it succeed or fail based on unpredictability, to have an idea of the factors that determine whether the company finally solves its data infrastructure problem or simply defers it. It is generally that of a leader, an individual who has the organizational credibility and an understanding of why infrastructure is essential, and enough persistence to keep making claims until they becomes a genuine priority rather than an ongoing item on the list of items that everyone agrees on but do not rise to the top. That leader has to be able to pay for all the short-term costs of the infrastructure investment - the delay and disruption to current processes, and the absence in the immediate production of results knowing that the capability it will create will justify the cost many times over. What this requires, ultimately it is a culture where investment in long-term infrastructure is welcomed and rewarded at the upper levels of management, not simply articulated in strategy documents and followed by a constant deprioritisation when the quarterly resource allocation meeting takes place. In the end, creating that culture is in itself, a long-term investment. It is however, in my opinion, one the highest-return investments an organisation that is committed to data-driven operations can make.}