I’m just back from Scotland where – besides having some lovely time hanging out with friends – I attended the annual Lands of Loyal (LOL) Data Science conference in Alyth. The event is an informal get together for the Data Science & Business Intelligence graduates from the Dundee University. This time my talk was among those selected for the day: I decided to rework my last blog post on Personally Identifiable Data (and not!) to a 20-minute presentation and packed it with questions (some of which I cannot answer) in regard to our identity on the web.
The conversation around the Right to Explanation reminded me of Mandela Effect. Just as Mandela’s death is believed by many to have happened before his real time of death, Right to Explanation is falsely attributed to GDPR’s collection of laws. An offshoot from early GDPR conversations, the rule has now developed its own literature on the internet. Posts suggesting that the law threatens Artificial Intelligence have flooded Google (examples here, here, and here), while uncertainty-fueled paranoia has taken over LinkedIn. Is it misinformation spread on the internet in its finest or is there more to the discussion? I suggest we review what a Right to Explanation is and why an absent law is causing so much stir on the world wide web.
Summary: Business instinct | When sums add up | Data-driven decision patching
This is a story about companies who like aggregations a bit too much. Data-driven decision making seems to be the new holy grail in management, but can the numbers always be trusted? What is key in data-savvy businesses: the people, the right technology, or – spoiler alert – is it something more fundamental? These questions become particularly urgent in the new economy as failing to embrace data can be a major growth impediment or worse, a dead sentence to the business.
Summary: Wanted: Data Scientist | A bird in the hand is worth two in the bush | A little stir | The infallible art of taking steps back
In this article I will look at how organisations can engineer their own Data Science team without loosing their mind in the process nor spending big money. As more and more companies want to be data-driven, they join the frantic search for the right staff to fuel these initiatives. Finding a data-fluent resource is not easy: according to McKinsey, “by 2018, the United States alone could face a shortage of 140,000 to 190,000 people with deep analytical skills as well as 1.5 million managers and analysts with the know-how to use the analysis of big data to make effective decisions.” The hunt is on for a skill set that is still relatively new to the market, and it is only starting to be taught at the universities. My belief is that fishing for a Data Scientist ‘superstar’ is often counterproductive and inevitably leads to a realisation that one person cannot do it all. Instead, investing in appropriate training of the current staff can lead to long-lasting benefits for the company.