Data show that on the one hand, the relationship between the African continent and Western countries is getting worse. On the other hand, Russia and China increase their relations with Africa by providing them with digital and technical infrastructure and using their military and economic predominance. But could these data have been a fundamental part of solving the slacking relationship between Western and African countries?

During this year’s Munich Security Conference from 17th to 19th February, Katharina Schüller (CEO, STAT-UP) was invited by Dr. Zoe von Finck (Podcast-Host “ichbinsofrei”) to discuss the question “Did we lose Africa?” together with Dr. Florence Gaub (Special advisor to the EU’s foreside commissioner).

To answer the question, we must have a look at the data. During the UN general assembly in 2022, many Africans abstained from condemning the Russian war on Ukraine. Also, the African countries’ votes were significantly pro-China, which could be a result of Xi Jinping’s massive diplomacy campaign. Since starting his reign in 2013, China has been very active in visiting states and trying to build diplomatic relations.

There’s a lot more data that could be interpreted to show the reasons for the European loss of African sympathy. For example, the Afrobarometer, which surveys African citizens about their opinions, former voting data to predict future behavior, or the so-called “hard facts” such as military and economic presence, debt, or foreign direct investments.

Standing alone, none of these data can give us the answer to whether we lost Africa or not. To get the whole picture, all the puzzle pieces must be structured and connected to each other. This can be done with so-called “self-organizing maps”, created with a tool called Viscovery.

‘Trust is a statement about the future. That's why so many people trust ex-President Trump, he's authentic. In times of constant communication, it is impossible to constantly pretend. The model of the rational, emotionless bureaucrat has had its day and is no longer convincing,’ says futurologist Dr Florence Gaub.

As part of the Munich Security Conference in February 2024, our founder and CEO, Katharina Schüller, joined Dr Florence Gaub and Zoé von Finck at Salon Luitpold to discuss the probabilities of nuclear bombs being used, possible apocalypse scenarios and information hygiene.

‘Many decision-makers don't even know how to switch off the tracking system on their mobile phones; fear also clouds our view of risks and leads to wrong decisions. The media often misquote and misinterpret statistics. The biggest risk in times of disinformation - fuelled by generative AI - is therefore the lack of data and AI literacy,’ observes statistician and AI expert Katharina Schüller. She concludes: ‘A basic course in data literacy could help.’ So is the increasing disinformation due to a lack of data and AI literacy the reason for the population's dwindling trust in politics?

Finally, things get philosophical - Dr Florence Gaub criticises the prevailing mood in Germany: ‘Either things stay as they are or we all die. But how do we want to be?’ Listen in.

Video: https://youtu.be/dKW3d4V1OW8?si=1N-DkMuRcv1Xjr0v

Podcast: https://ichbinsofrei.podigee.io/405-ki-desinformation

Last year, the trio discussed a key question: ‘Could we lose Africa?’. You can find the corresponding podcast episode from MSC 2023 here: https://ichbinsofrei.podigee.io/38-ibsof

Background information:

https://www.stat-up.com/who-we-are

https://www.ndc.nato.int/about/organization.php?icode=189

Podcast, feedback, comments etc.: Guest requests, comments: https://www.instagram.com/ich.bin.so.frei/

Kontakt@ich-bin-so-frei.org

Non-profit organisation: www.ich-bin-so-frei.org

Discover Your Music Style with Data

Have you ever wondered if your hobbies and social background can predict your taste in music? In a recent project, we explored just that! By analyzing data from a large dating service, we created a tool that uses demographic and hobby data to predict music preferences. Here’s how we did it.

The Dataset

We used anonymized data from around 120,000 members of a European dating service. The data included 24 attributes, such as age, education level, and hobbies, as well as information on their musical preferences. Our goal was to understand how these variables influence people’s tastes in music.

Our Approach

To create our model, we built a self-organizing map (SOM), which is a type of machine learning model used to visualize complex data. In this case, the SOM helped us group people based on their social and hobby-related attributes, making it possible to predict their musical tastes.

Here’s how it works: by answering a few simple questions about yourself (like your age and hobbies), you can find out which group you belong to in the map. Then, based on your group, we predict which music styles you're most likely to enjoy. The model takes into account 8 different genres, from Rock/Pop to Jazz/Blues.

What Can You Learn?

By testing your own profile against the model, you can see where you fall compared to other respondents. For each music genre, the model gives you a score, showing how your preferences match up with others in your group. Maybe you’ll discover you’re more into Jazz than you thought, or perhaps your love for Rock is right on point!

Why It’s Useful

Understanding personal preferences, especially when only limited data is available, can be tricky. However, models like this have broad applications. They can be used in product design, marketing, customer engagement, and even social network analysis. This kind of data-driven insight helps tailor products and services to specific target groups.

How We Built It

We preprocessed the data by standardizing variables and ensuring no missing information. Then, we used the SOM model to group respondents into approximately 400 clusters, allowing us to create detailed predictions for each person’s music preferences.

The results were based on attributes such as age, education level, and hobbies like theater, literature, and dance. After refining the model, we ended up with a map that can help predict music styles based on someone’s profile.

Try It Yourself

Curious to see how it works? In our showcase, you can input your own social and hobby preferences to find out which music styles people like you tend to enjoy. It’s a fun way to explore how your interests might influence your taste in music

In a world flooded with data, discerning meaningful patterns from clickstream data is akin to finding a needle in a haystack. Traditional analytics offer a superficial glance at user behaviour but fail to capture the interactions that define the digital experience. Targeted optimization of the website requires understanding the interests and wishes of the users. To do this, it is necessary to define personas from the raw data and examine their character.

Click data from visits to the Viscovery website were recorded for a six-month period to transform raw click data from their website into actionable insights. Then, a self-organizing map (SOM) has been used to segment user behaviour into distinct profiles or personas. This approach not only identified typical usage patterns but also provided a detailed map for real-time visitor classification.

The analysis revealed 13 distinct clusters of behaviour, each offering a window into the diverse ways visitors engage with the Viscovery website. From casual browsers to focused users, the behaviour classes were meticulously interpreted, providing a comprehensive blueprint for understanding visitor intent and preferences.

The implications of the analysis offer a multitude of benefits:

• Website Optimization: Tailoring web experiences to match personas, enhancing usability and engagement.
• Targeted Marketing: Delivering personalized advertisements and content, significantly increasing conversion rates and customer satisfaction.
• Subscriber Relationship Management: Utilizing behaviour profiles to refine subscriber interactions and offerings.

The unique visual approach to multi-dimensional user data enables the understanding of the users on a higher level. While a simple histogram of dwell times shows, how long people tend to stay on a certain page, the clustered view reveals common patterns, unexpected similarities and the assignment of an actual personality instead of just single numbers. Once we know the persona, we can easily design and implement strategies for improved usability. By prioritizing privacy and anonymizing data, it sets new standards for responsible data use in web optimization. The case study is more than a technical achievement. It's a visionary approach where data empowers innovation and customization. As businesses strive to navigate the digital landscape, this analysis offers a roadmap to success in an increasingly competitive online world.

If you’re interested in discovering the results of the analysis in form of an original SOM, visit our showcase!