As we pass the first anniversary of the start of the most recent tech hype tsunami (farewell NFTs, we hardly knew ye), it’s starting to become a little clearer how many enterprises might start their journey with Generative AI – and it’s in areas that are the traditional domain of the CIO, rather than the CDO. So, does that mean that the CDO role will be sidelined in the rush to GenAI? Or should the CDO suit up and go head-to-head with the CIO to lay claim to this growing area? Can we expect a CDO-CIO Rumble in the Jungle?
A side-effect of all the time I spend breathing the rarified alpine air of the CDO community is that my SQL skills have become rather rusty. So I’ve been intrigued by the idea of using the code-generation capabilities of tools like ChatGPT and Bard to write SQL for me. But how good is the current crop of LLMs at creating SQL code that not only works, but generates the insight you’re actually looking for? I decided to find out.
You arrive, slightly frazzled, a few minutes after the agreed time for lunch. At the door, you thrust the obligatory bottle of wine into your mother-in-law’s hand and say that you’re sorry you’re late; the traffic was terrible. “Which way did you come?” pipes up your father-in-law from the hallway, and you immediately realise that you have made a grave error.
As you stand, dazed, nodding along while nursing a room-temperature glass of Pinot Grigio, you find yourself thinking, “didn’t I just have to endure a conversation like this with the CMO this week?”
If you’re a CDO (in either name or responsibility), chances are you’ve had to write a data strategy. If you haven’t, you may feel that everything would go much more smoothly if you were able to pull it out of your bag and wave it in the face of every naysaying executive stakeholder who dares to question your work, with a righteous cry of, “it’s in the data strategy!” Sadly, naysayers are not so easily swayed. But there are some things you can do to at least raise the chances of your data strategy causing the C-suite to fall gratefully in line.
The demise of cookies has been long foretold. Ever since they 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 at last 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 life be like without them? And more importantly, will it actually be better than it was before?
The 2010s were a big decade for Chief Data Officers: from a standing start ten years ago, 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 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.