My Digital FootPrint

The (un)intended consequence of focusing on data, looking for significance, determining correlation, testing a hypothesis, removing bias and finding the consensus is that you ignore the outliers.  Hidden in the outliers of data are progress, innovation, invention and creativity, and the delay is that by ignoring this data and the signals from it, we slow down everything because we will always be late to observe and agree on what is already happening with those who are not driven by using data to reduce and manage today's risk.  Our thrust to use data to make better decisions and apply majority or consensus thinking creates delays in change and, therefore, increases future risk.  ------ In our increasingly data-driven world, the unintended consequence of data often manifests as delay. While data is hailed as the lifeblood of decision-making, its sheer volume and complexity can paradoxically slow down processes, hinder innovation, and impede productivity. This phenomenon underscores

I have been exploring the research and concepts that bias and trauma are deeply linked.  The linkage and directionality are much debated.   Trauma creates bias, and equally, bias creates trauma. It would appear that either can be a starting point, but they definitely feed each other, creating complex positive (healing) and negative (detrimental) feedback loops which extend beyond the individual and their immediate relationships to wider society.     Using systems-mapping to address Adverse Childhood Experiences (ACEs) and trauma: A qualitative study of stakeholder experiences  https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0273361 Why does this matter, as all data has a bias?  Fundamental to a decision-making role based on data is to demand that we recognise bias and try to remove bias; however, I am now thinking that if we remove the bias, we assume there is no trauma, and therefore, everyone will be rational.  Yes, there are some big ugly assumptions in that state

A problem (among many) with data is that many people ask questions that are easy.  How many and who clicked this button? These are easy to ask, occupy time, fill in KPI cards and are often easy to answer. Why do so few kick back to ask if it is the right question?  Why did they click the button? Oh, we don’t have that data! But we can create constraints that mean we get biased data as we don’t understand human behaviour in context.  ---- In 1973 two behavioural scientists, John Darley and Daniel Batson published " From Jerusalem to Jericho: A study of Situational and Dispositional Variables in Helping Behavior ." It was an investigation into the psychology of prosocial behaviour . Darley and Batson picked students who were studying to be priests at the Princeton Theological Seminary to determine how situational factors influenced prosocial behaviour. Hypothesis : When someone is kind to another, is that because he or she has some innate qualities that lead to kindness—or be

I am aware that a human limitation is our understanding of time.  Time itself is something that humans have created to help us comprehend the rules that govern us.  Whilst society is exceptionally good at managing short-time frames (next minute, hour, day and week),  it is well established that humans are very bad at comprehending longer time frames (decades, centuries and millenniums).  Humans are proficient at overestimating what we can do in the next year and wildly under-estimate what we can achieve in 10 years.  (Gates Law) Therefore, I know there is a problem when we consider how we should value the next 50,000 years.  However, we are left with fewer short-terms options each year and are left to consider longer and bigger - the very thing we are less capable of.  Why 50,000 years The orange circle below represents the 6.75 trillion people (UN figure) who will be born in the next 50,000 years.  The small grey circle represents the 100 billion dead who have already lived on earth

Predator-prey models are helpful and are often used in environmental science because they allow researchers to both observe the dynamics of animal populations and make predictions as to how they will develop/ change over time. I have been quiet as we have been unpacking an idea that with a specific data set, we can model user behaviour based on a dynamic competitive market. This Predator-prey method, when applied to understand why users are behaving in a certain way, opens up a lot of questions we don’t have answers to.   As a #CDO, we have to remain curious, and this is curious.  Using the example of the rabbit and the fox. We know that there is a lag between growth in a rabbit population and the increase in a fox population.  The lag varies on each cycle, as does the peak and minimum of each animal.  We know that there is a lag between minimal rabbits and minimal foxes, as foxes can find other food sources and rabbits die of other causes. Some key observations.   The cycles, whilst

As a human I know we all have biases, and we all have different biases. We expose certain biases based on context, time, and people. We know that bias forms because of experience, and we are sure that social context reinforces perceived inconstancy.  Bias is like a mirror and can show our good and bad sides. As a director, you have to have experience before taking on the role, even as a founder director. This thought-piece asks if we know where our business biases start from and what direction of travel they create. Business bias is the bias you have right now that affects your choice, judgment and decision making. Business bais is something that our data cannot tell us. Data can tell me if your incentive removes choice or aligns with an outcome.  At the most superficial level, we know that the expectations of board members drive decisions.  The decisions we take link to incentives, rewards and motivations and our shared values .  If we unpack this simple model, we can follow (the b

It is suggested that all which glitters is gold when it comes to data: the more data, the better. I have challenged this thinking that more data is better on numerous occasions, and essentially they all come to the same point. Data volume does not lead to better decisions.    A “simplistic” graph is doing the rounds (again) and is copied below. The two-axis links the quality of a decision and the person's capability with data.  It infers that boards, executives and senior leadership need to be “data-savvy” if they are to make better decisions. Data Savvy is a position between being “data-naive or data-devoid” and “drunk on data.”  The former has no data or skills; the latter is too much data or cannot use the tools. Data Savvy means you are skilled with the correct data and the right tools. This thinking is driven by those trying to sell data training by simplifying a concept to such a point its becomes meaningless but is easy to sell/ buy and looks great as a visual.  When you do