The death of third-party cookies has been long foretold. Ever since cookies were invented in 1994, questions have been raised over their privacy implications and potential misuse, yet they have persisted, unloved but indispensable. However, it seems like the death knell has finally sounded for third-party cookies, with both Apple and Google finally taking concrete steps to rein in their use. But given that third-party cookies and mobile Ad IDs still underpin a huge amount of the web and its economics, what will the future be like without them? And more importantly, will it actually be a better future than what came before?
The 2010s were a big decade for Chief Data Officers: from a standing start at the beginning of the decade, CDO has risen to become an indispensable C-suite role, with almost two thirds of Fortune 500 organizations hiring one.
But the role of CDO, especially outside of the US, is still poorly defined, and CDOs are frequently not set up for success within their organizations. Is the job a poisoned chalice?
Amidst all the craziness of the global coronavirus pandemic, it’s easy to forget that the world keeps turning, and that mundane things like new privacy laws coming into force are still happening. The impact of COVID-19 on global privacy practices is the stuff of a future post, but in the meantime, let’s distract ourselves with a little light reading about California’s new privacy law, the California Consumer Privacy Act (CCPA) and how it compares vs GDPR.
As we’ve established earlier in this post series, Data Science is a process, with quite a lot of repetitive elements. Many Data Science projects involve a familiar set of tasks to identify, clean and prepare data, before finding the best model for the scenario at hand. And despite the mystique around the whole profession, many Data Scientists spend a lot of time complaining about all this repetitive work. But any repetitive process is ripe for automation, and Data Science is no exception. Enter the field of “AutoML”.
As I mentioned in my first post in this series, the central purpose of Data Science is to find patterns in data and use these patterns to make useful predictions about the future. It’s this predictive part of Data Science which gives the discipline its mystique; even though Data Scientists actually only spend a relatively small fraction of their time on this area compared to the more workaday activities of loading, cleaning and understanding the data, it’s the step of building predictive models which unlocks the value hidden within the data.
Every September in France, summer-weary parents pack their children off to school for la rentrée (‘the return’) and return to work after the idleness of August. The break from the métro, boulot, dodo routine of daily life enables both students and their parents to throw themselves back into their work and studies with renewed vigor. … Read more
Ah, GDPR. Like the guy (or girl) you matched with on Tinder six months ago who got less interesting the more you got to know them, it just won’t go away. It keeps sliding into your DMs with teasing headlines like, “Data Protection Authority of Baden-Württemberg Issues First German Fine Under the GDPR” or “Washington Post offers invalid cookie consent under EU rules“. And there you were thinking you were done with it back in May, when you sent all your users that “Please respond to this email to stay on our mailing list” email and threw that giant banner about cookies up on your website.
Ask any Data Scientist and they will tell you that the process of ‘wrangling’ (loading, understanding and preparing) data represents the lion’s share of their workload – often up to as much as 80%. However, that number is not as alarming as it may at first seem. To understand why, let me tell you about my living room.
Ask an Analyst, particularly a Digital Analyst, how they’d like to develop their career, and they are quite likely to tell you that they want to get into Data Science. But in fact the two disciplines (if they can even be described as separate disciplines) overlap considerably – some would even say completely. So what is the difference between Analytics and Data Science?