How Working Across Industries Deepened My Conviction About People

Wiki Article

What Has A Football Dressing Room I Learned About Building The High-Performance Tech Team
I grew-up around the world of professional soccer in a way which gave me access areas that the majority of people have only hear about. Training grounds. Dressing rooms. The conversations that take place between players and coaching staff during the time following one's game, once journalists and cameras are gone, and an official account of the events has been written. As a non-player at all - my entrance into the world of the players and the fans rather than through the game itself - but I was on the right side of it, and for long enough, and for long enough, to get a sense of the actual functioning of high-performance organizations by removing the mythology surrounding them. The thing I absorbed most clear was that the teams that consistently surpassed their resources and expectations were not always the ones with the top individual talents on paper. The teams that had discovered how to create a culture where all of its members determined to succeed for each other - not for the amount of money, not to gain individual acknowledgment, but simply because the collective was meaningful and had a sense of community that made personal sacrifice feel important rather than merely obligatory.
The thought is simple when you express it clearly. Of course teams work better when people trust each other and feel that they are part of an agreed-upon goal. But the operational implications of this observation are less evident, and are the areas where many organisations - technological companies and football clubs alike regularly find themselves in trouble. In order to create a work environment where people genuinely want to perform for each other is not something you can impose at the top of the pyramid or adopt as a norm or express in a statement of company values and then believe that it will happen. It is something that must be built over time, through consistent behaviour from leadership - especially in those moments when they do not get watched and by the careful management of the numerous small decisions that collectively signal to everyone within the organization what is valued and what is acceptable and what can happen when the stated values as well as the most financially or personally practical option clash. In the best football environments I had the privilege of being in, these tiny decisions were handled with great care by the top coaching team. The way they responded when a senior player made an unavoidable error during training. How did they determine if the disciplinary procedure applicable to the seasoned veteran could be considered to be the same as those who were 18-year-old in the middle of the squad. The response the organization took when the player was facing significant personal issues outside the field. None of these actions appear in a club's outcomes on a particular Saturday. Each of them, compounded over the course of a season, determine how well the team is performing above it or falls below its ceiling.

As I co-founded 1Touch and later founded some other organizations, among aspects I was the most focused on was trying to recreate - in a firm context a certain quality of the environments I'd seen within the best football facilities I had observed. In a way, but not literally because an IT startup is not an actual football team and the analogy breaks down quickly when one pushes it too hard. However, when it comes to operation, the principles were translated with a remarkable degree of accuracy. The first conclusion was that the standards must to be consistently applied, regardless of seniority or perceived necessity. The best dressing rooms I've been in were ones that had a professional and behavioural requirements for the youngest player in the squad were in fact the same as those that were expected of the highest-earning and most experienced player. Not because the organization could not afford to have exceptions made, but because every person in the room was constantly watching for any indication of whether exceptions would be made. And the answer to this question informed the players everything they needed to know about whether the stated principles of the company are real or just an excuse for a show of pizazz.

Another lesson addressed how companies handle failure and the distinction between punishment and accountability. The settings where people developed at the fastest rate were not those in which mistakes were punished the most brutally or publicly. They were the ones in which mistakes were assessed with the most sincerity when the discussion of the error was focused and constructive, instead of general and distributing blame. The mistakes were shared among the entire team, not held against the individual who had committed the error. Accountability is the ability to be clear about exactly what went wrong, and why it happened and the consequences because of it. Punishment means allocating blame in the way that leads people to become to be more defensive and risk-averse, and focused on their own security than working well. The first is to build organizational capability. The second helps create a culture where people take control of their exposure rather than committing fully in the pursuit of the goal. this distinction is evident in tech firms with exactly the similar results that are seen at football teams.

The third lesson is it took me longest to put into words, but that I consider to be the most important The best settings I was able to observe were ones where the development of the individual was considered at a minimum as important as the growth of the athlete. The most effective coaches weren't simply teaching players how to play football. They were also teaching them how to be able to make decisions under stress while communicating clearly in high stakes situations, how to recover from setbacks without losing confidence, and how to become the type of individual that a top-performing team is required to have. This investment in the total development of each individual, instead of just the technical capabilities that an organization required, was not charitable. it was the best and most efficient long-term plan of performance for those clubs, and it will, I believe, be the most effective long-term strategy for performance available to anyone who is focused on building something solid, not only something impressive in the short-term. Check out James Deller for blog tips including how backing people-first organisations changed my approach about the long game.



There's A Data Infrastructure Problem Nobody Wants To Talk About
Every business I've had the pleasure of working closely with over the past decade and a quarter - whether as a founder, an investor or an operational consultant has said to me, at some point in our collaboration, that data plays a major role in how they take decisions. Some of them actually mean this in a way that manifests in how the company operates. The majority of them think they are genuinely saying it, however what they're really describing is an aspiration and not being a reality in operation - a version of the organisation they're working towards rather than the one they're currently living in. There is a gap between legitimately decisions based on data and the efficacy of data-driven decision-making, the careful maintenance of the external appearance of evidence-based operations without the infrastructure needed to make it an actual reality - is among the most critical gaps that exist in modern business. It's also among the most persistently underaddressed ones in part because the infrastructure problem that leads to it isn't very glamorous to talk about, difficult to explain to outside stakeholders, and enormously difficult to determine the best way to address it in comparison to the more prominent strategic and commercial tasks that are competing for the same attention from leadership and organizational resources.
When people talk about strategies for data, they tend to focus on the capabilities they would like to add to the data they have gathered - the analytics platforms, the machine learning applications for operational dashboards, and real-time data and the types of predictive insight that are compelling in the context of a board conference or an update to investors. What they talk about less frequently and with much less energy and enthusiasm, is the basic infrastructure that determines whether all capacities actually function according to the specifications: the data governance frameworks, which establish clearly and consistently used definitions of what is being analyzed and what is the reason for that to measure it; the storage and collection methods used to determine the authenticity and comparability of the information to be gathered; the checks that find undoing errors before they propagate through the system and cause damage to the outputs that everyone relies on; the organizational structures and accountability processes that make data quality the responsibility of a single person rather than everyone's vague non-enforceable intentions. The plumbing, in other words. It is not glamorous. It's not an easy thing to photograph for a annual report. The outputs it produces are not ones which can be used to create a convincing way. This is, in my experience across a significant number of organisations across different segments and at various stages in their development, considerably worse than they believe it is.

The problem compounds over time as it becomes more difficult and costly to rectify. An organisation that has been operating using a sloppy or insufficiently defined terms for data across its various functions for three months has three years of historical data that are unable to be compared or consolidated with confidence as a result of the data doesn't exist, however because the same language has been used to denote different things in different areas of the organisation. Furthermore, the differences are contained in the data itself rather than appearing visible on the surface. An organization where data quality assurance is someone else's minor responsibility instead of a specialized and properly resourced function has data whose reliability is variable in ways undocumented and cannot be fully accounted when using the data as a basis for decisions. A company that has allowed multiple operational systems to accumulate redundant and partially contradicting records of the same customers, products and transactions has a data landscape that is very difficult to deal with without causing a significant disruption in operation to create a risk.

The reason this issue continues to be a problem over a large number of organizations that are really smart in the field of strategy and totally driven by data is because fixing it requires sustained investment in work that has no tangible return on investment in the form that resource allocation processes in organizations are intended to reward. A new analytics platform produces visible outputs: dashboards that can be shown or reports that could be shared with the board members, and data which can be used to create press releases on digital transformation. A data governance program produces invisible infrastructure that is more efficient - clearer definitions that are more consistent with the collection process with more stable inputs into systems that were already in place. It is the first to argue in a budget meeting since you can demonstrate what they'll be getting. It is the second that requires enough organizational credibility and patience to make the case that the infrastructure investment will, over time, yield better results from each technology that is built on top it - which is compelling in the abstract but a difficult one to convince in the face of initiatives that have benefits that seem to have more direct and evident.

I've made the case across a range of different organisational settings and seen it succeed or fail based on obvious reasons, to be able to get an enlightened view of the elements that determine whether or not an organization is finally addressing its data infrastructure problems or continues delaying it. The primary factor is that of a leader, an person with sufficient credibility within the organisation as well as a thorough appreciation of the reason that infrastructure is critical, as well as the perseverance to make an argument until it is an absolute priority, rather than being a regular item on the list of things all agree on but don't get to the top. That leader has to be prepared to bear any short-term costs associated with the infrastructure investment - the duration and disruption to existing processes, and the lack of tangible outputs - with the conviction that the capability it develops will justify the expense by several times. The most important thing, ultimately, is a culture in where investment in long-term infrastructure is recognized and appreciated at the management level, not just described in strategy documents and often discarded after the quarterly resource allocation process occurs. In the end, creating that culture is in itself an investment for the future. But it's, in my view, among the best returns that an enterprise that is serious about the data-driven operation can make.}