My Digital FootPrint

It is all about averages.  If you have a single dimension data set, say height, with a large data set, it is probable that one user will be the average of the entire set. In a two-dimensional data set, height and weight, it is probable that one user may have these two characters as the average of the entire set. In a three-dimensional data set, inside leg measurement, height and weight, it is tending toward impossible that one user may have the three average characters of the entire set. More data makes it more probable, but also more characteristics make average persons more improbable, as mean, mode or medium. Facial recognition uses about 80 nodal points, it is (im)possible that a single data subject in large data set will be average on all points.    The point is that we think more data will create a better understanding of our users. This is unlikely to be the case. What we need to determine are the boundary conditions where the data we have access to enables better decisions.  

The purpose of this post is to explore the topic of CONSENT, which I have been writing about for over 6 years. I have unpacked consent in many articles and have concluded that as we unpack each layer of consent, we find that it is not what you thought it was. CONSENT is a mix of technology, ethics, policy, law, requirements, economics, data, marketing and trust to name a few. There are three paths leading from consent being requested to consent being agreed by the user. Path 1 is that you use your design skills to manufacture the users consent, using colour, fonts, buttons, processes with the intent to gain consent without interfering with the real business purpose. Lowest possible barrier. Path 2 is where we depend on each team (marketing, sales, operations and tech) doing their own thing and determine their own methods to gain and confirm consent to satisfy their requirements. Confusion often becomes evident Path 3 is where the organisation designs consent to be aligned to its own

peeling back the layers — thanks to  James Abell  for the minecraft illustration. We love the model or analogy about peeling an onion. We peel back one layer to reveal a new similar layer, each layer enabling us to offer a new idea or thinking and adding complexity. Often we use this model for ourselves to get to our inner core and what values drive us. C onsent:  in digital context is being explored in many places by many people.  Kantara  and MEF are two good examples. However, I am finding that as I peel, explore and uncover the “onion” of layered consent, I find that the next layer is not more onion [ with deeper inner meaning driving me to a core philosophy ] but rather I find something totally new, indeed I don;t start with an onion but a coconut. Inside my coconut I find an orange, then a Kiwi, then a grapefruit, passing a passion fruit and then a dragon fruit. Peeling this inner core, I hope to find inner meaning but it only reveals a two spouted teapot! Why use di

The image come from https://iclif.org/articles/give-employees-freedom-within-framework/ a super interesting post "Give Your Employees Freedom Within a Framework"

This thought piece explores how business executives ought to be debating control over user (data), is less about where data is collected and stored but rather where, or rather how, individual data is used, monetised and by whom. -- Given that platform companies such as Apple, Facebook, Twitter, Google, Baidu, Amazon, Alibaba, Tencent Xiaomi as examples complicate, confuse and officiate what they are actually doing with our personal data, how can leaders position their business to become truly customer centric and put the customer first. As a context, economics defines utility companies (gas, electricity, water, telecoms) as only having one true differentiator - price. Given the ubiquity and certainty of one unit of electricity is the same from where-every you buy it, the market players create bundles and offers to hide the actual price and to make comparisons between the same utility very difficult or near impossible. However, what happens when you don't have a “price” e.g. Fac

Source : Data Science Central   By: Jennifer Lewis Priestley love the thinking, however I would put governance at the top and moral's at the bottom, ethics (as group) above morals, then maths & computer science ( as philosophies ) Then algorithms then communications. Why governance at the top, as we need to be accountable and ethics are not accountable.    

Data Governance is somewhat easier to define using wiki on 17th March 2019 Data governance is a data management concept concerning the capability that enables an organization to ensure that high data quality  exists throughout the complete lifecycle of the data. The key focus areas of data governance include availability, usability, consistency [1] , data integrity and data security and includes establishing processes to ensure effective data management throughout the enterprise such as accountability for the adverse effects of poor data quality and ensuring that the data which an enterprise has can be used by the entire organization. Data governance encompasses the people, processes, and information technology required to create a consistent and proper handling of an organization's data across the business enterprise . It provides all data management practices with the necessary foundation, strategy, and structure needed to ensure that data is managed as an asset and tran