Why Working Across Industries Reinforced Operational Discipline About People

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What Has A Dressing Room For Football Teach Me About Building The High-Performance Tech Team
I was raised in the world of professionals in a way which allowed me access to places that most people just heard about in books. Training grounds. Dressing rooms. The conversations that take place between players and coaching staff in the hours after an event, after the media and cameras are gone, and the official version of events has already been written. As a non-player me - my entry point into this world was via playing with people rather than the actual game itself. However, I was close enough and for long enough, for me to grasp something vital about how high-performance environment actually work after removing the mythology surrounding them. The thing that I learned most clear was that the teams that consistently surpassed their resources and their expectations weren't the ones with the highest individual performance on paper. They're those who had discovered how to establish a setting where all the members committed to performing for each and not to earn cost of the individual acknowledgment, but simply because the collective had a meaning and a culture that made every personal sacrifices felt worthwhile, rather than merely obligatory.
The idea is clear when it is stated clearly. Teams work best when they trust one another and are able to believe in a common cause. But the operational implications of this are less clear, and are where most organisations - businesses in the field of technology and football alike - often get into difficulties. In order to create a work environment where people genuinely want to perform for each other is not something you can dictate from the top-down or create as a policy or express in a statement of company values, and think that it will come to fruition. It has to be built gradually, through a consistency in the behaviour of leaders - particularly in the moments that don't get a lot of attention - and through the judicious management of the thousands of small decisions that collectively indicate to all employees in the company what is actually valued and what can be tolerated in the real world, and what happens when the values stated as well as the most financially or personally suitable option conflict. In the top football environments I was a part of, those small-scale decisions were taken in a manner that was incredibly thoughtful by the most skilled coaching staff. How they reacted when one of the players made an error in training that was avoidable. The disciplinary standards used to deal with the veteran who was twenty years old was really the same as the one applied to the 18-year old who was a bit off the edge of the squad. How the team responded when an athlete was facing the aftermath of a personal crisis outside the game. All of those decisions don't will be reflected in the club's performance on any given Saturday. The sum of all these decisions, over the course of a year, determine which team's performance is higher it or falls below its limit.

When I founded 1Touch in the past, and later started various other companies, one of the things I was most careful about was attempting to recreate - in a enterprise context a quality of environment I had observed in the best football environments I had been around. Not literally, because an IT startup is not like a football team and so the analogy breaks down quickly if you go too far. But at the level of operating principle, the lessons were interpreted with remarkable accuracy. The first idea was that standardization needs been consistently implemented, regardless of position or unassailability. The best dressing rooms I had been in were those where the behavioural and professional standards expected of a young player in the squad were, in reality, the same standard required of the highest-earning, most skilled player. Not that the team was unable to afford to make exceptions, but due to the fact that everyone within the room was constantly checking to determine whether any exceptions would be made - and the response to that question showed them everything they needed know about whether the stated principles of the company were truly true or simply a flimsy display.

The second lesson dealt with how organizations handle failure and the difference between accountability and punishment. The settings where people developed fastest weren't those where the consequences of mistakes were dealt with severe or were most widely discussed. They were the ones where errors were most thoroughly analysed while discussing the mistakes was focused and constructive rather than general and allocating blame. Moreover, mistakes were shared among the team rather than pinned against the individual who made the mistake. Accountability means being clear about what went wrong, the reason it was wrong and what has changed in the process. The concept of punishment is to assign blame in a way that makes people to be more defensive and risk-averse, and preoccupied with defending themselves than having a good performance. The first builds organisational capability. The second develops a culture where people are able to control their own exposure rather than committing fully in the pursuit of the goal. this distinction is evident in tech firms with exactly similar results that are seen with football players.

The 3rd lesson is the one I took the longest to explain clearly, however it is the most important of all: the best environments I have observed were ones where the development for the individual was taken at least as crucial as the growth of the performer. The most effective coaches weren't just teaching their players how to play football. They were also teaching them how to be able to make decisions under stress while communicating clearly during high-stakes situations, ways to bounce back from setbacks with out losing faith, and how to be the kind of player that a high-performing team must have its players to be. It was an investment in the complete personal development of each member, and not just in technical abilities that the team required, was not charitable. In fact, it is probably the most effective long-term strategy for performance available to those clubs, and it is, to my mind, the most effective long-term strategy for performance available to any organisation that is committed to creating something long-lasting, not just something that is impressive in the short-term. Read James Deller for website examples including what making investment decisions changed what i look for about what matters.



Data Infrastructure Problem Nobody Wants To Talk About. Data Infrastructure Problem Nobody Wants To Discuss
Every business I've had the pleasure of working closely with over the past decade and a quarter - whether as an investor, founder or as an operational adviser - has told me, at some point in our time together, that data is the main factor in the way they make decisions. Some of them actually mean it in a way which is apparent in the way the organisation actually operates. A majority of them believe they are genuinely saying it, however what they're really describing is the aspiration of actually a present operational reality some version of the enterprise they're aiming for and not the one that they currently operate in. The gap between truly data-driven decision-making and the results in data-driven decision-making - - the careful management of the outward appearance of information-driven operation, without the infrastructure necessary to make it an actual reality - is among many of the most significant gaps found in the current business. It is also one of the biggest gaps that are not addressed, in part because the infrastructure issue that creates it is genuinely unglamorous to discuss, challenging for external stakeholders to understand and extremely difficult to determine the best way to address it in comparison to the more prominent strategic and commercial work that demands the same attention of leaders and organisational resources.
When organizations talk about the strategy for data, they tend to focus on the capabilities they would like to develop on top of their existing data. They talk about data analytics platform, machine-learning applications as well as the real-time operational dashboards and the types of predictive insights that are truly compelling in a board presentation or an investor update. What they tend to talk about less often and with much less energy and enthusiasm, are the core infrastructure that is the determining factor in whether all of those capabilities actually function as claimed: the information governance frameworks which define clearly and consistently used definitions of what is being analyzed and what is the reason for that to measure it; the storage and collection procedures that establish the credibility and comparability of the data being captured; the quality assurance methods that spot and fix errors before they get propagated throughout systems and affect the results that everyone is counting upon; the organization's structures and accountability mechanisms that make data quality an ongoing, explicit responsibility instead of relying on everyone's vague and unenforceable intention. The plumbing, also known as. The plumbing isn't glamorous. It's not easy to photograph for a annual report. There are no results which can be used to create a convincing way. And it is, in my experience in a vast number of organizations operating in different industries and at different levels of development, significantly more difficult than the business believes it to be.

The issue gets worse over time in ways that are becoming harder and more expensive to fix. A company which has operated without a clear or consistent set of terms for data across its various roles for three consecutive years has three years old data that is unable to be compared or consolidated with confidence as a result of the data has not been created, but because the same language has been used to mean different aspects of the organisation. Furthermore, the differences are buried in the data, rather than being evident on the surface. An organisation where data quality assurance has been a secondary responsibility, rather than a specialized and properly resourced function has data whose reliability has a range of variations that are not systematically documented and therefore can't be effectively accounted for when using the data to determine the outcome. A company that has allowed multiple operational systems to collate overlapping and partial conflicting records on the same customers, products or transactions has a data environment that is extremely difficult to correct without significant disruptions to the operation to pose a risk for the organization itself.

The reason this issue is present in so many organizations that are actually smart in the field of strategy and totally determined to implement a data-driven strategy is because fixing it requires long-term commitment to work that does not provide visible gains in the short-term that allocation of resources processes are intended to reward. A new analytics platform provides tangible outputs, such as dashboards that can be demonstrated, reports that can be shared with the board, as well as insights that can be translated into press releases about digital transformation. Data governance programs create invisible infrastructures - better underlying definitions and more consistent collection processes as well as more reliable inputs to existing systems in use. The first is relatively straightforward to justify in a budget conversation because you can show people what they'll gain. It is the second that requires sufficient organisational credibility and patience to convince people you believe that this infrastructure initiative will, over time, yield better results from each feature built on top it. It's an impressive argument in abstract but it can be difficult to compete with initiatives that's benefits can be seen immediately and more obvious.

I've made this case in various organizational contexts and witnessed it be successful or fail based on clear reasons to have an extremely clear understanding of what will determine if an organization finally tackles its data infrastructure issue or continues to defer it. The difference is typically at the level of a leader. It's an individual with sufficient organisational credibility and a clear comprehension of why the infrastructure is critical, as well as the perseverance to make that argument to the extent it becomes an absolute priority, rather than just a repeated item on a list of things everybody agrees is important, however they don't always get to the top. The leader must be able to bear expenses in the short term of infrastructure investment - - the time or disruption to existing processes, the lack or evidence-based output - with the certainty that the long-term capabilities it creates will justify that investment several times more. What is required, ultimately is a framework which investment in long-term infrastructure is recognized and appreciated at the top level, not only articulated in strategy documents and regularly discarded during the quarterly discussion on resource allocation is held. In the end, creating that culture is in itself an investment over the long term. It is however, in my opinion, one of those investments with the highest returns an organization that is committed to data-driven operation could make.}