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The significance, statistical and otherwise, of a partnership approach to self-service analytics

“Here we go again,” I thought on reading the BBC news headline “Statistically significant” rise in net migration to the UK.* I expected another attempt to squeeze a story out of an inconclusive set of figures, using the fact that it just broke the[…]

Holding out for a hero? Go for flexible and sustainable data science instead

Out of the latest Big Data trends one of my least favourite is the Data Superhero.  It’s fashionable to talk with great excitement about a new breed of data science superhero, who is essential for analytics success.   No hero, no project. How long[…]

Everyone’s invited: don’t make data science a member’s only club

Like Groucho Marx* I’m not a big fan of clubs. Which is why I’m dead against any attempt to turn an open-access, multidisciplinary vision like data science into one. Like Groucho, are you also not keen on institutionalising data science into a[…]

How Do You Keep Control of a Multi-billion Dollar Project?

Major construction projects always come with major scrutiny. There is even greater need to keep the project under control and to troubleshoot problems before potential knock on effects arise. This means project managers need easy access to critical[…]

Report on the current state of play in the adoption of adaptive trials designs by Kenneth Getz of Tufts CSDD

There’s a really good article in Applied Clinical Trials on the state of play in the industry of the use of adaptive clinical trials by Ken Getz of Tufts, click here for full article  reports that in their survey of the industry, about 20% of phase[…]

Buying the analytics platform was the easy bit - now how do I use it?

If we were to believe the hype, peddled both within the media and by technology vendors, turning big data into enormous business benefit is simply a matter of writing a cheque and buying the latest, greatest analytics platform.

The rise of the embedded data scientist - Lessons from the pharmaceutical front line

Pharmaceuticals are increasingly using predictive science as a platform for R&D.  Critical to this approach is building models from non-clinical and clinical data. What makes this tough is that you have to integrate and interrogate multiple data[…]

Taming in vivo data – the last (data) frontier in pharmaceutical research?

Historically, in large pharmaceutical R&D operations, in vivo studies have occupied a slightly awkward position in terms of data management. Data from the earliest stages of research – high throughout experimentation and the like – while not without[…]

Patient sub-population effects: The regulator’s dilemma

At the DIA/FDA statistics workshop last week, amongst the many and varied interesting presentations, the one that most caught my eye was Dr Armin Koch’s talk on examples of patient sub-populations. (Dr Koch, of the Hannover Medical School, is a[…]

Explainability: machine learning and human guidance

Lurking in the shadows of the big data revolution, roams a contentious claim that stubbornly refuses to lie down. Put simply, it says that feeding machine learning algorithms with the right data means that scientific models (or any other type for[…]

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