I grew up in the world of elite football, in a manner that afforded me access to locations that most people know about. Training grounds. Dressing rooms. Conversations between players and coaching staff after an athletic event, when the cameras and journalists are gone and the official version of events has already been recorded. There was no player in any way - my entrance into the world of sports was via playing with people rather than the actual game itself - but I was there enough and for long enough, for me to grasp something vital about what high-performance environments can do once you take away the mythology that surrounds them. The thing that I learned most evidently was that teams that consistently beat their resources and their expectations were not always the ones that had the best talent on paper. They were the ones that have figured out how to create an environment where members of the team committed to performing for each and not to earn amount of money, not to gain individual recognition, but because the collective was meaningful and had an atmosphere that made individual sacrifice feel worthwhile rather than an obligation.
This statement is evident when you express it clearly. The truth is that teams function best by having people who trust each other and believe in the same goal. However, the implications for operational use of this are not as obvious and are where the majority of organizations – businesses in the field of technology and football alike - always get into difficulties. To create a community where people actually want to do their best for each other isn't something you can command from the top down, or adopt as a norm or express in a statement of values for the company and expect to see it manifest. It has to be earned in time, through consistently displaying leadership behaviour - especially in those moments when they do not get watched and through the careful management of the numerous small decision-making processes that collectively convey to everyone in the organisation the things that are valued and accepted, and what actually happens when the stated values along with the most commercially or personally appropriate choice are at odds. In the most successful football settings I had the pleasure of working in, those decisions were made with incredible care by the top coaches. The way they dealt with situations where an older player made an unavoidable error during training. Which disciplinary criteria used to deal with the veteran who was twenty years old was genuinely the same as the standard applied to the eighteen-year-old on the edge of the team. The response the organization took when one of the players was experiencing a serious personal problem outside the field. The results of these decisions do not appear in a team's outcomes on a particular Saturday. All of them, when accumulated throughout the season, decide whether the team's performance is above but falls short of the limit.
In the time I co-founded 1Touch and later built other organisations, one of aspects I was the most conscious of was trying to recreate - in a business context - the kind of the environment that I had experienced in the most prestigious football venues I had been a close associate of. However, not in the literal sense, as an IT startup is not an football club and this analogy becomes a bit shaky when you go too far. However, in terms of operational principle, the lessons translated with remarkable fidelity. The first idea was that standardization needs to be followed consistently, regardless of seniority or perceived absoluteness. The most comfortable locker rooms I've ever been in were ones where the behavioural and professional standards expected of the youngest players in the team were in fact the same as those for the highest-earning most experienced player. This isn't because the club could not have afforded to have exceptions made, but as a result of everyone who was in the room was permanently watching for signs that exceptions might be made. And the response to that question showed them everything they needed to know about whether the stated values of the organization were genuinely true or merely cosmetic.
The third lesson focused on how organizations deal with failure and the difference between punishment and accountability. The places where the players grew fastest were not the ones where the consequences of mistakes were dealt with brutally or publicly. They were in the areas where errors were dealt with most openly while discussing the mistakes was focused and constructive, instead of general and distributing blame. Also, where learning was shared by the entire group, rather than being held against the individual who committed the mistake. Accountability is the ability to be clear about what went wrong, the reason it happened and the changes that occurred that resulted from it. Retribution means distributing blame an approach that causes people to be vulnerable and defensive and concerned with their own safety than to perform well. The first helps build organisational capacity. The second helps create a culture where people take control of their risk rather than being fully committed to the mission, and this distinction manifests in technology firms with precisely the same results as it does with football players.
Third lesson is the most difficult for me to comprehend. longest to articulate clearly, but that I consider to be the most important my observations: the most positive environments I observed were ones where the progress of the individual was regarded equally as the development of the athlete. The most effective coaches weren't only educating players on how to play football. They were teaching them how consider their thinking under pressure in a clear and concise manner, how to communicate when faced with high stakes, how to bounce back from setbacks without feeling defeated, and to be the leader that a well-performing team has its players be. This investment in the total advancement of the individual instead of only in the technological skills the team immediately required, was not charitable. They were the single most effective long-term performance plan that could be used by these clubs. It is, as I see it, the most effective long-term approach to performance that is available to all organizations that are determined to build something long-lasting rather than something just stunning in the short run. Check out James Deller for website advice including how scaling tech companies confirmed what i suspected about what matters.
This Is The Data Infrastructure Problem Nobody Wants To Talk About
Every company I've dealt closely with during the last year and a-half - whether as an investor, a founder or as an operational adviser has said to me, at some point during our relationship, that data is the main factor in the way they decide. A few of them truly believe it in a manner that will be evident in the way they actually run their business. Most of them believe they're making a statement, however what they're talking about is something that is more of an aspirational idea than actually a present operational reality it's a model of the one they're creating in contrast to the reality they're currently living. The gulf between driven by data and the outcomes in data-driven decision-making - - the careful management of the exterior appearance of information-driven operation, without the infrastructure that makes it real - is one of the most important gaps within modern business. It's also among the most neglected ones as a result of the infrastructure-related issue that creates it isn't very glamorous to discuss, difficult for external stakeholders to understand and extremely difficult to prioritise against the more obvious strategic and commercial work that requires the same leadership attention and organisational resources.
When companies talk about their strategies for managing data, they are more likely to focus on the capabilities they plan to develop on top of their existing data. They talk about analytics platforms, the machine learning applications operating dashboards in real time which provide the kinds of a predictive insights that sound really compelling in the context of a board meeting or an update to investors. What they usually talk about less frequently and with less energy and energy, is the infrastructure that determines if all the capabilities will work as promised: the data governance frameworks that define clearly and consistently used definitions of what's being measured and why collecting and storing processes that evaluate the reliability and comparability of data which is being stored; quality checks that find or correct any errors before they spread across the system and cause damage to the outputs that all rely on; and the organisational structures and accountability mechanisms that make data quality an ongoing and explicit responsibility instead of relying on everyone's vague and not enforceable goal. The plumbing, also known as. The plumbing is unglamorous. It's difficult to photograph to be used in an annual report. There are no results which can be used to create a convincing way. And, in my experience across a substantial number of organisations across different fields and at different stages of development, significantly worse as the organization thinks it to be.
The issue increases over time by becoming challenging and expensive to fix. An organisation which has operated with inconsistent or poorly defined terms for data across its various tasks for the last three years has three years old data that is unable to be reliably aggregated or compared it is not because the data doesn't exist, however because the same term has been used to describe different things across different areas of the organisation, and the differences are hidden in the data itself, instead of being apparent from a distance. A company whose data quality assurance is someone else's primary responsibility, and not a dedicated and properly resourced function is one whose data's reliability differs in ways not documented, and thus is not systematically considered when the data is used in making decision. An organisation that has allowed multiple operational systems to create overlapping and partially conflicting information about the same customers, products and transactions has a data landscape that is truly difficult to rectify without causing significant disruption to operations that it poses a risk.
The reason why this problem is recurrent across a wide range of organizations that are genuinely intelligent about strategy and are genuinely committed to data-driven operations is because addressing it requires an ongoing commitment to work that does not produce visible quick-term results of the sort that organizational resource allocation procedures are designed to reward. An analytics platform that is new produces visible outputs such as dashboards that are easily demonstrated as well as reports that are shared with the board, and insights that can be translated into press releases on digital transformation. A data governance program creates an invisible infrastructure with clearer definitions, more consistent collection processes as well as more reliable inputs to existing systems in already in place. This is a simple thing to justify during a budget discussion because you are able to demonstrate what they'll get. The second is a matter of having enough organizational authority and grit to prove this investment would eventually produce better outcomes from every new capability that is added to it - which is an appealing argument in the abstract, but not easy to compete with initiatives whose benefits have a greater impact and are easily visible.
I've made the case in enough different organisational contexts and witnessed it succeed or fail based on reason that is predictable, to have an accurate understanding about what makes an organization finally tackles their data infrastructure issue or continues delaying it. The main difference is determined by a leader - an person with sufficient credibility within the organisation as well as a thorough understanding of why infrastructure is vital, and enough perseverance to continually make this argument till it is an absolute priority, rather than just a repeated item on a list of things everyone agrees are important but don't achieve the status of being a top priority. A leader must be willing to take on the immediate cost of the infrastructure investment - the delay that it will take, the disruption of routine processes, and the absence or evidence-based output - with the conviction that the ability it builds will justify the expense many times over. What's required, ultimately it is a culture where the long-term investments in infrastructure are thought of as a priority and is rewarded at upper levels of management, not simply mentioned in strategic documents, but then consistently deprioritised when the quarterly resource allocation meeting takes place. Making that change is, in itself, a long-term commitment. But, in my opinion, one of the highest-return investments an organisation which is serious about a data-driven operation could make.}
