Category Archives: Clement Fredembach

Is Collaboration killing Creativity?

Tuesday December 20th, 2016

If collaboration is good, is more collaboration better? Project management methodologies that have been successful in production-centric environments, e.g., agile, dev-ops, lean are increasingly being deployed in big data projects. However, big data projects are a combination of production and creative work. Software engineering and development is arguably production-centric and well-suited to optimisation workflows. On… Read More »

What Buffy the Vampire Slayer tells us about a Trump presidency and Brexit

Wednesday November 9th, 2016

Big data is not a substitute for research (plus how Brexit and Trump’s victory were predicted by Buffy the Vampire Slayer) In a world where the amount of data still grows exponentially and its analysis becomes ever more sophisticated, 2016 has seen two shocking elections results that pretty much no one including the winning party… Read More »

Game Of Thrones – Who Dares Dies. Okay, So Who’s Next?

Wednesday June 22nd, 2016

By Dr. Clement Fredembach “Who shot JR?” Two generations ago, Dallas had the whole global village on tenterhooks (his killer turned out to be the oilman’s wife’s sister who was pregnant with his child… ask your Dad). Today, Game of Thrones is keeping the deadly tradition alive. Now, the question before each episode is “Who’s… Read More »

Who’s next? Predicting Deaths in Game of Thrones – Part 2: Event-based survival modeling

Wednesday April 20th, 2016

In part 1 we saw that network graphs provide invaluable information in terms of understanding story development, character importance, and “character death” prediction with limited external input. Stories are arguably more likely to be written according to a small set of rules rather than a strict adherence to graph theory. In this post, we use… Read More »

Who’s Next? Predicting Deaths in Game of Thrones* – Part 1: From books to social networks

Tuesday April 19th, 2016

<<Mandatory Spoiler Warning>> Information from the first 5 books is discussed, which corresponds to the first 5 TV seasons with some variations (the TV series is more advanced but omits a number of secondary characters for clarity and budgetary reasons). <<End of Warning>> Does fiction really have to make sense? This blog explores the application… Read More »

A little less quotation (and a little more ambition please)

Tuesday March 22nd, 2016

Your new big data initiative is not living up to expectations? Stop copying and try something new. If your social media feeds are anything like mine they are awash with inspirational quotes, sometimes at the expense of original content. Interestingly, data science/big data/analytics is in a similar situation, where copying and replicating what others are… Read More »

Is Your Company Product or Service Centric? Do Your Customers Agree?

Wednesday November 25th, 2015

Mine their comments to find out. In an ideal world, all companies would have a portfolio of excellent products delivered with remarkable service. Competition in the marketplace however dictates that company resources have to be allocated where they can be most effective. Understanding whether customers see your company as product-centric or service-centric does therefore matter.… Read More »

Data Science – What if the Needle is Made of Hay?

Friday September 25th, 2015

“Finding a needle in a haystack” is perhaps the most overused quote of the data science trade, with most material promising to sift/burn/search the haystack faster than before to find the vast stack of needles it hides underneath. In reality, however, there is not always a needle and even when there is, we may not… Read More »

Data Visualisation is Evil

Thursday June 11th, 2015

With apologies to Edward Tufte What do Jeremy Clarkson and Data Visualisation have in common? They both are very successful and talk directly to the “heart”, bypassing the cognitive system. They radiate an aura of authority that one has to unravel layer by layer because their very premise could be accurate or faulty. Unfortunately, like… Read More »