Personal recommendations are probably the strongest referral. If your
friends tell you to checkout a restaurant they recently discovered, you are
highly likely to follow their advice (assuming a general level of trust
between you and your friends). For e-commerce companies and online
platforms, recommendations are of critical importance. No wonder that
plenty of research has been attributed to the development of the
“perfect” recommendation engine.
Barely a week passes without the announcement of yet another (seed)
funding round for a startup, which claims to utilize “artificial
intelligence”, “deep learning”, “machine
learning” or “proprietary algorithms”. Algorithms, it
seems, are at the core of almost all ventures these days – is that
really the case?
How predictable is voter behavior? Could big data really swing an
election? The past US presidential campaign and the debate about the effectiveness of Cambridge Analytica have fueled
the discourse about the role of data in election campaigning.
Open data in biomedicine is a gold mine that can strengthen innovation
in pharmaceutical R&D. In combination with the right analytics, public
data helps identify therapeutic targets and ligands, enhance clinical
development, and boost portfolio management efficiency. The challenge is to
purposefully integrate abundant and heterogeneous data scattered across
The volume of data in the world is increasing exponentially, and with it
the opportunity to take on the many environmental, social and developmental
challenges facing the world. In other words, big data represents a catalyst
for achieving the Sustainable Development Goals, as set by the United
Nations (UN). The opportunity is there for governments, business, academia
and civil society to drive forward this movement. And the UN is looking to
be of enabling forefront of this movement.
In times where seemingly every second “The Economist” Special Report
focuses on either Artificial Intelligence (AI) or Big Data, general
expectations regarding current technological capabilities are higher than
ever. Rightly so, as there have been so many notable advances in recent
years. What does this mean for trend detection?
The story immediately went viral: Big Data company Cambridge Analytica
and its sophisticated psychographic models helped Donald Trump to secure
the victory in the 2016 presidential election. The story played to all
prevalent fears in the age of big data: privacy, microtargeting,
behavioural steering. But now – with far less media buzz – the
company admits that it was never really involved in the Trump campaign.
What can we learn from this ‘scam’?
When the results were in on last June 26th, most of those watching were
surprised or even shocked that a majority of Britons had just voted to
leave the EU. Even among those who cast their vote for “Leave”, many
said they had not anticipated the vote to come out in their favour, and
some even stated they had “not wanted it to happen”.
Biomedicine is a central driving force of the rise of big data.
High-throughput screening and the increase of computing power have led to
the generation of vast amounts of data, opening new avenues for