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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.

About our Blog

This blog is a place for us to reflect on data science, AI, and machine learning. Hence, it covers a broad array of topics: technical considerations, our view on certain industries, interviews with researchers, thought leaders, and industry experts, as well as light-weight visualisation.

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Data Trails #2 – Snapshots from the history of data visualisation

Once we have grown used to a new technology, it is hard to turn back and imagine what it was like when it was all new and unheard of. We do not even have an idea of what it was like before the internet anymore.

A Modern Visualization of Group Comparisons

More and more companies, governmental institutions and researchers employ data-centric methods to derive insights from data. Visualizing complex patterns in a simplistic yet intuitively comprehensible way is more important than ever.

Data Trails #1 – Snapshots from the history of data visualisation

81 years of budget data and various categories in three diagrams – the United States Fiscal Chart from the 1870 US census atlas is a real blockbuster in the history of data visualisation. The atlas as a whole is full of interesting graphics and has a widespread reputation as an early gem of data visualisation.

Data Science for Pharma – A Short Case Study

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 data sources.

Artificial Intelligence and Law

Legal hackathons are popping up with such increased frequency, one might think that the legal bar exam would by now include some mandatory tech section. That’s not quite the case yet, but academics and representatives of the legal profession are not tired to emphasize that after retail, insurance and banking, law is the next major sector to get disrupted by tech.

Big Data and the United Nations’ 2030 Agenda

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.

Trend Detection – Delineating possibilities and utopia

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?

Übersättigung im Profifußball: Wie viele Zuschauer kommen zu Fußballspielen?

Bei vielen Freundschaftsspielen der deutschen Fußballnationalmannschaft – wie jüngst gegen England – sind in letzter Zeit einige Sitzplätze frei geblieben. DFB-Teammanager Oliver Bierhoff warnte bereits vor einer “Übersättigung” des Fußballs. Gibt es generalisierbare Treiber für die Beliebtheit einer Fußball-Partie? Ein kleines Experiment – abseits der großen Fußballbühne.

A Question of Data Quality

80% of work in data science projects is dedicated to data quality assessment, data preparation and integration. Applying and tweaking the algorithms, improving the performance of models (basically all the fun stuff) covers only 20%. What’s the reason for this?