My Digital FootPrint

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 observation...

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 visua...

What are we asking the questions for? This link gives you access to all the articles and archives for </Hello CDO> This article unpacks questions and framing as I tend to focus on the conflicts, tensions, and compromises that face any CDO in the first 100 days — ranging from the effects of a poor job description to how a company’s culture means that data-led decisions are not decisions. I love this TED talk from Dana Kanze at LSE.  Dana’s talk builds on the research of Tory Higgins who is credited with creating the social theory “ Regulatory Focus ”  This is a good summary if you have not run into it before. Essentially the idea behind “Regulatory Focus” is to explore motivations and routes to getting the outcome you want. The context in this article is to explore how a framed approach to questions creates biased outcomes. One framing in Regulatory Focus centres on a “Promotion Focus” which looks for “ gain ” which can be translated as finding or seeking hope, adva...

Click here to access all the articles and archive for </Hello CDO> Day zero, you have arrived and you have 100 days to plan. ( H 0 ) How do you determine if your new company is addressing the underlying issues that hold back data from being what they imagine it can be?  The issues that hold back an organisation from really capturing the value of data are at a  minimum: org structures, people issues, a lack of accountability, and incentives. Whilst having a CDO, doing data science, having analytics, using artificial intelligence, testing data quality, and a world class data governance structure make a difference; true transformation will remain a struggle if the structural issues remain. The question is how do we, as a CDO, test the fitness of our organisation's preparedness for data.  If the results are acceptable to the leadership team is one of politics and is way beyond  the scope of this article.   To test the fitness of your organisation's prep...

Click here to access all the articles and archive for </Hello CDO> A very modern problem for leadership, executives and boards is, in fact, a very old problem; it is just that the scale, impact and consequences have increased.  Evidence .   There are two sides to the evidence coin, the proof the problem is the priority problem for scarce resources, and the other side is that that solution is the one that will drive the outcomes we desire.  Very different evidence requirements, and each side have many faces. Complexity and uncertainty mean that the historical preference for gut feeling and big leadership power plays provide insufficient rigour in the decision-making process. However, evidence gathering and presentation have similar issues.   Evidence can always be found to support the outcome you want (is this a problem or not), and incentives mean that evidence about which solution creates the best outcome will be lost, reframed and distorted.  Unpi...

The last article was about being railroaded in the first 100 days, recognising that you are forced into a decision.  In this one I wanted to unpack that “data” shows that using science in an argument (say to defend against railroading) just makes the members of the team more partisan (aligned to their own confirmation bias and opinions).  As the #CDO, your job is to use data and science, therefore in the first 100 days, with this insight, you are more likely to lose more people than win friends, lose more arguments than win and create bigger hurdles?   What I suggest, based on this work is that to overcome “proof by science” is to use curiosity to bring us together. Image source: from a good article by Douglas Longenecker --- Dan Kahan , a Yale behavioural economist, has spent the last decade studying whether the use of reason aggravates or reduces “partisan” beliefs. His research papers are here . His research shows that aggravation and alienation easily win, irrespec...

Subject: Agenda item for our away day on strategy and scenarios To: CEO and senior exec team We should congratulate ourselves on the progress made, however as your CDO, I am now going to make a case that we measure too much, have too much data and that as a team, we should reflect on the next thing that data can support us in! We have bought into “Data is the new oil,” and whilst we know the analogy breaks down below the veneer, the message is beautifully simple and has empowered the change to a data and digital business. The global pandemic has accelerated our adoption and transformation, and we are in a better place than March 2020. However, sticking with oil, we know that the extraction process has downsides, including carbon release, messy, and difficulty locating economic wells.   Amongst data’s most significant downsides are legal liabilities, noise and the wrong data.  I can easily hide data’s downsides through dashboards.  Our dashboards are based on trickle-do...