Author: dsa_admin

No Time To Die

The organization of the 11th meeting of the Spatial Data Analytics Expert Group included some unexpected twists. After postponing the original meeting in September, we also had to switch to an online format at short notice on the new date. Although the excitement couldn’t quite compete with a real agent movie, we were at least pleased that we could finally welcome a large number of participants.

The real excitement came from the announced contents. Dr. Joachim Steinwendner from FFHS had offered to host the meeting and had prepared a program with the topic GIS and Health. The two announced talks were titled after Bond movies.They addressed the interface between GIS and Health, once from the pharmacological point of view of and once from the perspective of geoinformatics.

PD Dr. Stefan Weiler focused on the first view. In his talk “On Her Majesty’s Secret Service” he presented the role of geodata in medicine with numerous illustrations (e.g. the Corona dashboards). Joachim Steinwendner then changed the perspective in his talk “The World Is Not Enough”. He asked the audience to imagine a GIS in which the coordinate system did not map the world, but rather the human body.

The meeting ended in an informal exchange under wonder.me. Plans were made for future collaborations or at least for the next visit to the cinema.

Diversity: Fully Exploit the Potential of Data Science

Just around 25% of the participants of the 8th Swiss Conference on Data Science are female. This aligns with the report from the World Economic Forum that claims that women make up only an estimated 26% of workers in data and AI roles globally. But why? And how can we change that?

We talked to Christian Hopp from BFH, who has addressed academic careers, gender, and diversity in his research, and Teresa Kubacka, who works as a freelance data scientist at Litix and is a member of a community “Women in Machine Learning and Data Science Zurich”

Why do you think diversity is important in Data Science?

Christian Hopp: Very generally, why would anyone think that diversity should not be important in Data Science? Aiming for example at gender balance or the equal representation of minorities in Data Science means fully exploiting the talent pool and fairly distributing opportunities.

Software, algorithm, or more broadly technology development in general, is first and foremost an interactive process, where various actors are involved and where communication and collaboration help to combine different knowledge bases. Hence, to fully exploit the potential of Data Science, it is paramount to involve individuals from all sorts of backgrounds (gender, ethnicity, age, socio-economic background, etc.). Diversity broadens the search horizon, it may help to develop insight and products/service offerings that are more responsible, more ethically, and socially acceptable. When the process itself is more inclusive, so will the final solutions developed be. This may range from making AI applications less racist or including more female-centered views when developing algorithms. If we fail to pay attention to diversity, many of the unconscious biases that have been uncovered may find their way into algorithm development. In the end, we may end up with severely biased algorithms and less trusted by potential user groups.  Diversity in Data Science can endorse these relevant values and viewpoints already during the technology development process. Values like diversity and inclusiveness of the development teams/companies need to be front and center to ensure responsible and inclusive technology and innovation development. 

Teresa Kubacka: Data science needs diversity because we live in diverse societies.  Although we tend to think that “data is the new oil” and “data speaks for itself”, data is not part of the natural world as the oil or the force of gravity, but it is people who actively create data. People decide which data is important enough to collect and what to leave out. People decide which research question is worthwhile the effort and which projects to invest in. This is why if our goal is to create data-driven products that make sense for all the members of society, we have to include a diverse representation of society in the process of creating those products and defining what is important. We have many examples of data projects that backfired spectacularly because they have been designed and developed by a homogeneous group of people (for example a health monitoring app that doesn’t have a functionality to track menstruation). Luckily, we also have many examples of projects where inclusive data science projects led to more empowerment or have driven positive change. This holds for all kinds of diversity, not only gender diversity. Last but not least – can we afford to lose talented data scientists only because they don’t have “the right appearance (gender, skin color, etc.) for the job”? 

What’s keeping women out of Data Science?

Christian Hopp: To be honest. I sincerely do not fully know, but I wish to understand. We have done prior research in STEM fields, focussing on female academic careers. We found that gender stereotyping attributes lower field-specific ability to women. In sum, women aspiring for an academic STEM career with leadership responsibility are confronted with “double” incongruity: First, they are experts in domains that are clearly male-dominated, subjecting them to severe biases stemming from the perception of their abilities. Second, even an aspiration for leadership was still seen as atypical for women. It could be that careers in data science are fraught with problems because women have to fulfill expectations in very male-dominated environments. 

Also, interactions with colleagues and superiors played a similarly important role in academic careers in STEM fields. Oftentimes role models are important to pursue a certain career path. Especially early career stages are sensitive periods in which influential imprints may be left. A lack of prominent role models might keep women out of data science as a career choice. But to answer this more fully, we would need more empirical evidence as to how individual aspects of gender imbalance interact and co-evolve with systemic ones. Formal and informal rules, proximate social structures, organizational culture, professional networks, couple perspectives, prevailing stereotypes, and individual motives may all interact here.  

Teresa Kubacka: In my experience, it’s not the lack of interest. I meet plenty of women who are fascinated by data science and are highly competent to become good data scientists. Many obstacles that they face are the same for women in tech in general. Here I’ll focus on the ones most characteristic for data science. 

One group of obstacles is related to a stereotypical perception of who can be a good data scientist: it is a person with a formal degree in an area historically predominantly given to men (computer science, mathematics, etc.), so women are statistically more likely to be perceived as not having the “right” qualifications. This happens also because a data scientist is often perceived to be a better software engineer and there is low awareness of a big variety of different flavors of data scientist roles among recruiters as well as applicants – some roles are more product-oriented, there are many teams where a data scientist needs to be a good communicator and structured, analytical thinker in the first place, some data science roles have a strong UX and product design component. 

Another group of obstacles is more of a mundane nature, but can, unfortunately, be a real deal-breaker. One big issue is a lack of data science roles with 60% workload and a relatively small market for freelancers, which in Swiss reality means that women who have to share a large portion of family responsibilities cannot easily work as part-time data professionals. It is also not easy to make a transition into data science gradually on the job, without investing time after work into getting a certification (like a CAS or a Bootcamp). Working women with family responsibilities are particularly impacted by this because they cannot afford the time. 

The third group of obstacles comes from within the existing machine learning/data science community. Some things that used to be perceived as normal in male-dominated communities are perceived as hostile by many women. For example, until not long ago one of the most important AI conferences used to be called “NIPS” with a pre-conference event called “TITS”. Only after severe criticism, it has been hesitantly renamed to “NeurIPS” (link 1, link 2). As a woman wanting to enter the field, you start asking yourself: will I be taken seriously there if they picked an acronym for a conference after a female body part? 

The last thing that I think is also relevant is that on one side, requirements for data scientists written by some recruiters are unrealistic, but on the other side, many women don’t believe that they can apply and do the job if they fulfill only part of the requirements and learn the rest on the job. This is why it’s important to build up their confidence – for example by creating networks of female professionals who can exchange experiences, by creating inclusive environments that allow for free experimentation and learning by doing, and by engaging in different mentoring programs both as mentor and mentee. 

How can you support and push diversity forward?

Christian Hopp: Generally, I think it is important to increase awareness through communication. Organizations need to put the benefits of diversity front and center. Not only on the webpage and other communications but in the hearts and minds of people working in data science. We need to wholeheartedly embrace the notion that diversity leads to better, more inclusive, and more innovative outcomes. Second, and that being said from a middle-aged, white, male professor from a non-academic family background trying to educate the next generation of data scientists, we need to activate and communicate through role models. Career paths in data science need to become more visible for women, diverse, or minority individuals. Third, we need more mentoring for diverse, minority individuals in companies but also in academic training.

Teresa Kubacka: I can give some examples based on the activities in our community. We organize meetups and workshops aimed to support women and gender minorities in data science and machine learning. Our community members can talk about their data science projects and learn new skills in a friendly environment. We try to give an opportunity to speak to people who have different kinds of data science roles and life situations to present a variety of inspiring role models. We put a lot of emphasis on events where the participants can engage in informal coaching and at least once a year we try to organize a career event. We also support other communities for women in tech, for example by participating in the conference “WeTechTogether”, where more than 20 communities took part, and where WiMLDS, Litix, and Databooster organized a geodata workshop together. 

However, we can only do that much and there is still much more systemic change that needs to happen. There is no one single solution and every organization will have to find its own path. Some solutions come as an answer to the obstacles to diversity I described before. For example, the Swiss job market would need to open up for more part-time work in data science. Over the last few years, we have already seen an increase in 80% workload positions. So if you are a manager and have an open position for a data scientist, consider making it a 60-100% position or a job-sharing position. If you have an employee with a strong analytic skill that is inspired by data science, think if there is a way for this person to learn some data science skills within their current role. As a general rule, we are all biased and use stereotypes, so it’s always good to check your bias and privilege, and question your instinctive hiring choices because they may act against inclusiveness and diversity. If you design a product or start a project, put effort into assembling a diverse team. If you can, encourage people from historically non-privileged groups to participate in high-profile projects and give them credit and recognition for their effort. Once an organization embraces diversity as its value, and not as an option for interested participants, many of these changes will follow naturally as a consequence of this choice. 

The Art of Data Fusion

By Nicolas Lenz (Litix), Stefan Keller (OST) and Reik Leiterer (ExoLabs)

Geodata are used in various industries and academic fields and often have to meet specific requirements in order to be used, for example in terms of geometry, recording time point or semantics. But often different geodata sets have similar geometric properties but different semantics or are captured at different times – or vice versa. Accordingly, the added value arises when your data sources start to «talk to each other», connection points between the data are used or possible gaps can be filled. In this context, there is a multitude of technical terms, which are sometimes used differently depending on the subject area, sometimes are used synonymously and sometimes are used inappropriately in their terminology – so you will read about «append», «merge», «relate», «link», «connect», «join», «combine», or «fuse», just to mention a few.

In the last meeting of the Spatial Data Expert Group on the 4th of November, this topic was presented and discussed, and the challenges and potential of the concept were highlighted. This included a critical examination of the semantic classification as well as the presentation of various possible applications in research and industry. Our host was the UZH Space Hub at the University of Zurich, represented by Dr. Claudia Röösli.

Representation of individual tree characteristics based on multi-temporal airborne 3D-LiDAR data, in situ measurements, and multi-spectral satellite data. Fuses data – or not?

So, what is Data Fusion – with a strong focus on spatial data? For some, it means more a list of different data sets, with a narrative relating one data set to the next. For others, it means visualizing different data sources on the same graph to spot trends, dynamics, or relations. In the spatial domain, the basic concept of data fusion is often the extraction of the best-fit geometry data as well as the most suitable semantic data and acquisition times from existing datasets.

The keynote was given by Dr. André Bruggmann, Co-CEO, Data Scientist and Geospatial Solutions Expert at Crosswind GmbH. Under the motto “Unlock the Where.”, he presented how data fusion techniques help customers gain new insights, from (spatial) visualizations and web applications to facilitate strategic business decisions (e.g., selection of optimal point of sale locations). In addition, he presented a project where data fusion techniques are applied to make detailed and future-oriented statements about the assertiveness of e-mobility and identify relevant trends for the automotive industry.

Dr. André Bruggmann from Crosswind – “Unlock the Where”

These inputs led to an exciting discussion between the experts present – not only on the technical implementations presented, but also regarding the potential for optimisation and possible future cooperation. This is exactly how the initiators of the event had envisioned it – an open and inspiring exchange in line with the basic idea of open innovation.

Are you also interested in spatial data and its applications? Then come to the next expert group meeting on 15th of December on the topic of GIS and Health, hosted by Dr. Joachim Steinwendner from FFHS.

Smart Service Innovation for Adapting to the Pandemic Situation – Successful Smart Services Summit 2021

This image has an empty alt attribute; its file name is Summit21-1.jpg

By Jürg Meierhofer

On October 22, the expert group Smart Services welcomed worldwide top experts to the fourth Smart Services Summit. The focus was on how Smart Services allow firms to adapt in the COVID-19 pandemic. Examples of remote and collaborative working have created new forms of co-delivery where customers are integrated into the service processes. Such a change requires a mindset change for more traditional firms as the service model migrates from ‘do it for you’ to ‘do it yourself’ or some mix of ‘do it together’. Considering service science, the switch makes perfect sense as it means that the full set of resources within the ecosystem are now being used rather than only a part. Services can be delivered faster and at lower costs with the support of new technologies and when working with the customer in a co-delivery mode. The changes are leading to new value propositions and business models today and will lead to an evolution in Smart Services in the future. The changes themselves must be understood, and we may need to consider new or different implementation and delivery models for Smart Services. These new working approaches may also requite use to re-evaluate both training and education.

Across the papers and presentations, it became apparent that digital service innovation has substantially changed and accelerated since the start of the pandemic. Customer needs and service processes have undergone dramatic disruption, which is still ongoing. A common thread throughout all the papers was the concept of the ecosystem thinking, which was discussed from a wide field of perspectives and in a comprehensive way. In line with the concept of the Service-Dominant Logic, the needs of the different actors in the ecosystem need to be identified and integrated into the design of the services and the integration of the various resources in the ecosystem. The ecosystem perspective not only integrates the different human actors, but also technological, digital resources.

Innovation through intensive collaboration allows to switch different perspectives and innovation approaches. This results in seamless value propositions and solutions for the beneficiary actors, which is a necessary prerequisite for economic value creation. Well-designed service experiences based on a consequentially customer-centric view and approach are thus at the basis of value creation.

This transition to digital service innovation in ecosystems requires not only fundamental changes of the technological platforms. In particular, collaboration across actors, organizations, and industry requires a new level of trust, culture, skills, marketing approaches and innovation frameworks.

Many thanks to all those who spoke at, and attended, the Smart Service Summit. A big thinks to IBM, data innovation alliance, ZHAW Zurich University of Applied Sciences and Lucerne University of Applied Sciences and Arts for supporting the event.

data innovation alliance at the AI+X Summit

The ETH AI Center celebrated its first birthday on October 15, 2021, at the AI+X Summit and the data innovation alliance was there to congratulate and to join the inspiring crowd. The day started with workshops.

David Sturzenegger and Stefan Deml from Decentriq organized one of the workshops on “Privacy-preserving analytics and ML” in the name of the alliance.

It was our first in-person workshop again, and such a great experience for us. We gave an overview of various privacy-enhancing technologies (PETs) to a very engaged and diverse audience of about 30 people. We had in-depth discussions about the use-cases that PETs could unlock, and also presented about Decentriq’s data clean rooms and our use of confidential computing. Our product certainly generated a lot of follow up interest, especially from those who wanted to reach out to demo the platform. We were also joined by a guest speaker from Hewlett Packard who spoke about “Swarm Learning”.

David Sturzenegger, Stefan Deml

Melanie Geiger from the data innovation alliance office attended the workshop about AI + Industry & Manufacturing led by Olga Fink from ETH. The overall goal of the workshop was to identify the next research topics. Small groups with representatives from manufacturing companies mixed with researchers discussed the challenges and opportunities of predictive maintenance, quality control, optimization and computer vision. We identified research topics such as more generalizable predictive maintenance methods that work for multiple machines or even multiple manufacturing companies. But we also realized that some challenges are more on the operational side or applied research like in the integration of the method into the whole manufacturing process and closing the feedback loop.

In the evening the exhibition and the program on the main stage attracted 1000 participants. We had many interesting discussions at our booth with a wonderful mix of students, entrepreneurs, researchers, and people from the industry. Of course, we also saw many familiar faces and due to the 3G policy, we got back some “normality”.

Spatial Data Analytics at the
#wetechtogether conference, 02.10.2021

By Nicolas Lenz, Litix Applied Data Science

Spatial Data was a workshop topic at the #wetechtogether conference which took place on October 2. The data innovation alliance, its member Litix and WiMLDS Zurich sponsored and organized two workshops entitled “Jump into Geodata”. The participants learned how to access Swiss geoservices with Python and how to use them for the presentation and analysis of geodata. We would like to thank the participants for their attendance and the lively discussions in the workshops. We hope that this will lead to new innovative geospatial projects in the future!

Innovative ideas around geodata are indeed welcome, since Spatial Data Analytics is one of four focus topics in the Databooster initiative. Project ideas related to geospatial data have increased chances of receiving Databooster support!

Being part of the #wetechtogether conference was a great experience for data innovation alliance and Litix. We support the main goal of the conference to empower people to bring more diversity into tech. We are already looking forward to the 2022 edition!

Visualizing Swiss Open Government Data

Benjamin Wiederkehr, Interactive Things
June 28, 2021

Open Government Data has the power to transform how governments engage citizens. But looking at today’s open data platforms, we have to ask the question of how accessible, usable, and shareable open data for the majority of people truly is?

Turning a downloaded spreadsheet into an insightful visualization requires design expertise. Querying data via an API requires programming knowledge. Sending the link to a data source puts the burden to make sense of it onto the recipient. Users expect from open data to discover facts and tell stories, not to wade through spreadsheets in search of answers. Organizations aspire to provide open data to improve transparency and increase engagement, not to fill a complicated file cabinet with it. Future platforms must lower the barrier to access and bridge the gap to use open data for everyone.

This presentation shares our learnings from building such a platform: visualize.admin.ch. Commissioned and co-created by offices of the Swiss Federal Administration, we envision a new way to better serve citizens through linked open data: a self-service interface that empowers users to visualize open data based on smart defaults and design best practices. Furthermore, the service empowers the user to boost the reach of open data with options to share and embed these visualizations with proper attribution and reliable reproducibility. The audience learned all about the underlying design principles, the impact of participatory development methods, and the benefits of user-centric open data services.

Problem: What makes working with data so difficult?

  • Organizations maintain a variety of scattered data sources often locked in information silos.
  • Standard analytics software products might be powerful but are complicated to use for non-technical users.
  • Results from existing products are un-designed, un-responsive, and un-customizable.
  • Results from existing products can’t be easily shared in their interactive and dynamic form.

Solution: How does visualize.admin.ch solve these problems?

  • Access with confidence: Give users secure and regulated access to your data through our unified interface to search and browse the most up-to-date data sets independent of their original information silo.
  • Visualize for efficacy: Empower users to visualize your data with compelling charts and maps in our intuitive visualization editor that comes with smart defaults and design best practices built-in.
  • Share for impact: Boost the reach and engagement of your data with our flexible options to share and embed the visualizations with proper data source attribution and reliable reproducibility.

References:

Expert Group “Blockchain Technology in Interorganisational Collaboration” meeting 29.04.21

The 12th meeting of the expert group “Blockchain Technology in Interorganisational Collaboration” took place over lunch on the 29th of April.

First, the members were informed about the opportunities of the innovation process of the databooster. The innovation process gives the benefit of exploring and testing ideas together with experts in the field. Moreover, one can get support for project funding. The iterative process involves different steps such as scouting for ideas, setting up a call, shaping and re-shaping the challenge and ultimately setting up a deep-dive workshop (https://databooster.ch/expertise/).

After these introductory remarks, the expert group hosted Daniel Rutishauser from inacta AG to give an overview on the new DLT law and future trends in the blockchain sector. Daniel presented inacta’s hypotheses to four key areas of the blockchain space: crypto assets, token economies, DLT solutions, and DLT base layer. In particular in the area of crypto assets, Daniel explained that Switzerland has a competitive advantage due to the new favorable DLT law and he expects that many of the future crypto assets will be issued and traded in Switzerland. Besides many other topics, the current hype around NFTs (non-fungible tokens) and its effect on the digital art market gave rise to much discussion among the experts. The members could not agree on the fundamentals for the immense prices that this new market has realised. Considering the number of open questions, NFTs might be a topic that deserves a meeting for itself.

The online meeting was concluded without an apéro, but with many new insights on the blockchain sector gained.

Service Lunch Smart Services: Pollux – Digital Alpine Twin

Marco Zgraggen, Geschäftsführer, Sisag AG, Daniel Pfiffner, Geschäftsführer, ProSim GmbH
Date of presentation: 16.03.21

Die Firmen Sisag AG, Remec AG und ProSim GmbH haben einen Bergbahnsimulator entwickelt, der es ermöglicht, Alpine Destinationen wie beispielsweise Skigebiete mit ihrer Infrastruktur in kurzer Zeit digital abzubilden und Entwicklungsmöglichkeiten zu testen und auszuwerten. Dabei geht es vor allem um Kapazität, Kosten des Betriebs und Verhalten der verschiedenen Gäste im Skigebiet.

Der Simulator hat dabei zwei Anwendungsgebiete. Einerseits ist dies die strategische Entwicklung der Bergbahngebiete. Dabei kann die Frage sein, was die ideale Dimensionierung eines zu ersetzenden Liftes ist und was die Auswirkungen der Dimensionierung auf das restliche Gebiet sind. Ebenso können neue Pistenführungen oder neue Anlagen und ihre Auswirkungen im Gebiet im Voraus getestet werden. Andererseits dient der Zwilling der operativen Entscheidungsunterstützung. Beispielsweise was passiert, wenn ich heute unter einer gewissen Anzahl Gäste eine weitere Piste öffne oder einen Lift schliesse, oder wie viele Kassen muss ich öffnen, damit die Wartezeit an der Talstation nicht zu gross wird.

1. What was the Challenge?

Es gab mehrere anspruchsvolle Entwicklungsschritte. Einerseits war es sicher die Zieldefinition des Projektes. Was sind die Fragestellungen, welche die Bergbahngebiete wirklich beschäftigen. Am Anfang wurde vor allem in Richtung Kapazitätsplanung entwickelt. Im Laufe des Projektes hat man festgestellt, dass die Kostenberechnungen ein ebenso wichtiger Teil für den Nutzen der Software ist.

Ein weiterer anspruchsvoller Schritt war, das Personenverhalten von verschiedenen Personengruppen im Gebiet abzubilden. Diese konnten durch agentenbasierte Simulation gut und generisch abgebildet werden.

2. By which Service-oriented Approach did we Solve it?

Der digitale Zwilling wird für jede Alpine Destination individuell gebaut und parametrisiert. Durch die vorgängige Entwicklung einer Bibliothek für Alpine Destinationen kann dies in kürzester Zeit realisiert werden. Die Software wird dem Betreiber anschliessend auf einer Plattform zur Verfügung gestellt.

3. What are our learnings?

Dies ist eine Umsetzung eines Digitalen Zwillings. Es wurde von der Zieldefinierung, über die Umsetzung, bis hin zu den Weiterentwicklungsmög

Ask the Experts

New structure, new logo, new concept: the expert group “Machine Learning Clinic” is a unique pool of expert knowledge. Our last meeting aimed to connect experts willing to share their knowledge with companies in need of expertise to push AI-projects forward. Despite the hype around AI and deep learning of the last years, only a few deployed solutions are running in industry. Why is this? What are the missing bricks? One of the missions of the ML-Clinic is to overcome this gap between lab and real-world applications.

During registration we identified needs and experts on the following hot topics:

  • Data Management
  • Vision Inspection
  • Cloud Integration
  • Hardware / Edge-Processing

During a 90min virtual meeting we connected people, exchanged experience, and brainstormed about new ideas. With the new open-innovation initiative www.databooster.ch and the support from Innosuisse there are many possibilities to support companies on their ML-journey.

In a familiar round we discussed about real cases from Roche, Sulzer, SBB and others. One common issue is data quality, availability and working with rare scenarios. How to deal with missing, wrong or corrupted data. How to train robust neural networks based on such datasets. There is no easy solution but there are more and more ideas how to deal with these common industrial issues.

Beside the deep technology discussions another highlight was the “non-virtual apéro package” which all participants received before the event. Even though we only communicated through Bytes over a glass fibre everyone had a real chilled beer and some nuts in their hands – what beer would be better to stimulate the real neurons than the AI beer: DEEPER

Overall a successful event and we hope you tune in for the next get together of the ML-Clinic!

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