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Workshop on Challenges and Novel Approaches for Industry 4.0

By Michael Opieczonek, Innobooster Robotics and Reik Leiterer, Innobooster Databooster

Joint Event by Innovation Booster Robotics and Innovation Booster databooster
March 16th, Biel/Bienne

The event was organized on premises of Switzerland Innovation Park Biel/Bienne. As this is a center of innovation, premises of a smart-factory and cobotics center, the symbolic meaning of this location resonated well with the event. The event started with keynote talks, addressing the topics of smart factory, cobotics, human-machine-interaction and general trends in the field of robotics and data-driven value-added services. After a networking lunch, an interactive, moderated design thinking workshop for identifying challenges and developing ideas and solutions was organized.

In form of impulse presentations. 5 speakers give inspiring insights into their research and application areas as well as highlighting current challenges to solve within their respective fields:

Prof. Dr. Sarah Dégallier Rochat, Lead of Humane Digital Transformation at Bern University of Applied Sciences delivered a presentation on Robots as tools: New approaches to robot integration for SMEs. She highlighted that Swiss SMEs are the makers and can turn workers into makers via the concept of augmented worker. Dr. James Hermu, Postdoctoral Researcher in the Learning Algorithms and Systems (LASA) Laboratory at EPFL, delivered a presentation on Real Time Adaptive Systems for Human Robot Collaboration. He talked about methods to teach robots to perform skills with the level of dexterity displayed by humans in similar tasks. Philipp Schmid, Head Industry 4.0 & Machine Learning at CSEM (Swiss Center for Electronics and Microtechnology), delivered a presentation on Industry 4.0 and Machine Learning. He highlighted the need of how machine learning and robots can automate processes at industrial sites and hence increase future of smart-factories. Dr. Renaud Dubé, CTO and Co-Founder of Sevensense Robotics, delivered a presentation on Visual AI: Empowering a new generation of mobile robots. We learned about robots visual capabilities and challenges: lighting and viewpoints changes and understanding semantics.

Prof. Dr Marc Pollefeys, Professor of Computer Science at ETH Zurich and the Director of the Microsoft Mixed Reality and AI Lab in Zurich, delivered a presentation on spatial computing and the industrial metaverse. He gave interesting examples how metaverse can be used for instructional trainings of workers at industrial settings and how spatial computing is contributing to more sophisticated mapping and localization of robots.

The ideation workshop followed in the afternoon and was moderated by the facilitators Prof. Dr. Patricia Deflorin and Dr. Jürg Meierhofer. The workshop took the format of sequences that build on each other – from identifying and understanding the problem to designing the right solutions. The workshop sessions were closed with the presentations of the devleoped ideas and solutions.

The identified challenges could be roughly clustered into 3 categories.

Cluster 1 relates to the general challenges of integrating automation (both on software and hardware side) into existing processes. On the one hand, this includes the necessary technological knowledge and understanding of WHAT one wants to implement – on the other hand, it also includes the expertise or competence development within the company on HOW it can ultimately be integrated. Decision-makers, employees and customers must all be integrated into this process,

and employee acceptance and training/up-skilling must be ensured – all while considering the short- and long-term cost-benefit relations, ethical and moral issues, and cultural acceptance.

Cluster 2 refers to machine learning systems that react more flexibly/dynamically to process changes. On the one hand, regarding rapidly changing environmental conditions,

on the other hand, related to highly dynamic process sequences (small batch manufacturing). This requires not only innovative approaches in human-machine interactions (intuitive, ease-of-use handling, no-code environments etc.) but also standardization in processes and interfaces as well as further developments in modular and self-learning ML systems. In this context, the challenge also arises as to how and whether the individual, experience-based knowledge of experts in a company can be transferred to (semi-)automated processes, e.g., the transformation of human intuition in process understanding to rule-based robot-supported systems.

Cluster 3 concerns the (extended) use of cobots/robots in the field of maintenance. This concerns the large area of logistics/ergonomics, from pick-up, sorting and movement of highly divers component categories, to complex processes in material/surface inspection, automated damage repair/replacement, and assembling and dismantling of large rail vehicles. In these processes, the reliability/accuracy requirements are a major (technical) challenge and addressing them would often involve very high costs.

For each cluster, the workshop participants focused on some of the identified challenges and discussed possible solutions.

Solutions Cluster 1:

  • Guideline/framework for the integration of automation processes into existing workflows, considering management, customer, and employee’s perspectives (at the meta-level).
  • Framework for integration and regular assessment of compliance with ethical and moral guidelines and legal framework conditions.
  • Guideline/framework for the practical implementation of automation processes in the company regarding the involvement of employees: internal acceptance, considering employee’s needs, training/education (up-skilling) and empowering.
  • Needs assessment for automation solutions in industry (standardization, interfaces, usability/interactivity).

Solutions Cluster 2:

  • Development of automatization solutions that can meet the requirements of low volume/small batch production or highly variable process flows.
  • Development of ML systems with improved flexibility in terms of self-learning/self-optimizing components so that they can better adapt to changing environments and high-complex processes.
  • Development of monitoring systems to capture unconscious, intuitive human components in the manufacturing process and convert them into a rule-based, machine-executable program (e.g. ViT).

Solutions Cluster 3:

  • Development of a tunnel scanning and cleaning system to identify and remove paint from vehicles – a combination of intelligent optical sensing for detection and characterization of paint and non-destructive automatization for cleaning/removal of paint while preserving the underlying paint/coating etc.
  • Development of a tunnel scanning system to identify and characterize surface damages/deformations on large vehicles – a combination of intelligent passive and active optical sensing, resulting in a digital 3D-representation and classification of surface damages/deformations.
  • System development of an automation solution for different tasks as a mobile implementation which works inside of large vehicles.
  • Conceptual development of a holistic system (identifying segments, sub-processes, requirements) to support logistics/ergonomics, both in terms of the potential of autonomous (e.g., for, sorting, transport) and worker assistance systems (e.g., exoskeleton, human-robot collaborations).

If you are interested in the ideas and/or you want to further explore these challenges and ideas, we welcome your submission for proposals during the calls by both boosters:

Calls for Proposals for Funding – Innovation Booster Robotics    (next one – due April 28th)
Calls for Proposals for Funding – Innovation Booster Databooster

You can sign up for newsletters on our websites as well as follow us on LinkedIn.

Newsletter Innovation Booster Robotics
Newsletter Innovation Booster Databooster

Smart services for sustainability – circular servitization

By Jürg Meierhofer, ZHAW

In a distinguished group of highly experienced people, we discussed how value is created in business ecosystems and differentiated between the individual and the organizational perspective. It was very inspiring to have diverse industry representatives in the same room and to create a common understanding. Departing from economic value creation, we extended our scope to the ecological dimension. An intense discussion arose how ecological value can be created without negatively impacting the economic value. There were statements that economic value creation is still the predominant requirement, and that in many cases, also a slight reduction of economic value for the sake of ecological value would not be accepted. However, the increasing relevance of sustainability and upcoming regulations might change this balance in the near future.

Lunch & Lecture @ Paulus Akademie Zürich: Innovation Made in Switzerland – the most innovative country in the world!?

By Reik Leiterer, Exolabs

How has Switzerland managed to top the Global Innovation Index rankings for years? State and cantonal funding agencies and regional think tanks play a decisive role. But what is an innovation anyway and how is the degree of innovation measured? What is the difference between an idea, an invention, and an innovation? And what entrepreneurial prerequisites does “real” innovation need?

These and other questions were discussed during the Lunch & Lecture series at the Paulus Academy in Zurich on 1st of February. Gundula Heinatz Bürki, the managing director of the data innovation alliance, took the participants on a journey through the history of innovation in Switzerland and showed how ideas, inventions and innovation are connected. Using the example of various global innovation rankings, she explained the multitude of criteria that go into such rankings and why Switzerland benefits from the innovative large enterprises with a high rate of patent applications.

It became clear that there is still room for improvement related to innovation promotion at SME and start-up level and how the federal government, cantons and regional associations have been active in this area in recent years. The possibilities of the Innosuisse programs such as the Flagship Initiative or Innovation Booster were presented, cantonal initiatives in Zurich, Vaud, and Grisons analysed and the ideas behind the regional research clusters driven by Switzerland Innovation discussed. Bottom line: Interested participants, exciting discussions and the conclusion that Switzerland has an enormously high innovation potential, but that it still needs programs and initiatives such as the Databooster in order to keep top positions in the rankings in the future. Because there is one thing that does not exist in the innovation process: stagnation.

Workshop on Flexible working conditions in STEM

By Nicolas Lenz, Xurce and Gundula Heinatz Bürki, data innovation alliance

The Alliance’s commitment is manifold. The network is not only committed to professional exchange, but also wants to contribute to social challenges.

Society is changing, and in recent years the labor market has been shaken up considerably. The need for new work models, more home office and a generally improved work-life balance are sprouting up.

After an introduction of Priska Burkard from techface about facts and figures to the actual situation of women in tech force we discussed the challenges and first ideas about possible solutions.

A workshop dedicated to these topics led to surprising results!

  • Employers have adapted to the new circumstances and offer (from their point of view) flexible working conditions.
  • However, many of them fail to make the working conditions visible to the outside.
  • In daily business, the working conditions are not lived, promises are too often broken.
  • The needs of the employees often do not coincide with the offers of the employers.
  • In a typical organization, people with different needs work together. This also means (from the employees’ point of view) that they do not all need the same working conditions.

We consider it important to further deepen these discussions. The next step is to find the right form of discussions and then produce a tangible output.

We very much welcome all input. Anyone who wants to participate in the discussions, please contact or

8th R&D-Conference in Industry 4.0

By Focus Topic Leads Industry 4.0: Patricia Deflorin, FHGR, Philipp Schmid, CSEM and Philipp Hauri, Industrie 2025

Research meets Industry – from ideas to business cases.

In the knowledge that networking and cooperation with universities is an important success factor for the innovation activities of companies, Industrie 2025 initialized the “R&D Conferences on Industry 4.0”.

In these conferences, you will get an overview of the topics of the near future in an efficient way and get insights what is being researched and developed at universities and universities of applied sciences in the field of Industry 4.0.On the 24th of January, they invited for the 8th R&D Conference on Industry 4.0, which was hosted by HSLU (Lucerne University of Applied Sciences and Arts) in the city of Rotkreuz.

After the welcome by Philip Hauri (Industrie 2025) and an inspiring keynote from Stephan Keller (V-Zug, HSLU), 23 university projects linked to emerging topics in the fields of Artificial Intelligence, Smart Factory, or Digital Twin were presented.

In this context, Sybille Aeschbacher from Innosuisse presented, how knowledge transfer from universities to industry can be promoted and what tools are available in Switzerland for this purpose – where of course the Innobooster Databooster is part of it. Next to the talks, a poster exhibition gave the participants the opportunity to get in direct contact with the speakers and learn more about the projects presented.

The conference convinced with the knowledge and innovative spirit among the speakers and participants and how the intensive exchange between research and industry was noticeable during the whole time. And in the end, it has once again become clear that rapid technological developments only develop their full potential when the corresponding business cases are in place.

“Responsible AI” as focus theme 2023

By Markus Christen, UZH, Christoph Heitz, ZHAW and Karin Lange, La Mobilière

On December 15, eight members of the Data Ethics Expert Group met to look back to this year’s achievements and to determine the theme of 2023. The group decided to focus its activities on “Responsible AI”.

In the year 2022, the main activities of the Data Ethics Expert Group focused on three events, one internal event for discussing recent research projects on data and AI, one public event presenting the Digital Trust Label (see and one public event organized together with Economiesuisse and the UZH Digital Society Initiative (see, where more than 30 industry representatives exchanged on practices to improve data ethics.

Furthermore, expert group member Christian Hauser reported on a funding success that emerged out of expert group activities: Innosuisse will fund the project “Entwicklung eines digitalen Dialogsystems für die heuristikbasierte Integration von ELSI in datenbezogene Entscheidungen bei Schweizer Unternehmen”. This project is partly based on the Data Ethics Code created by the Expert Group and published in 2020.

For 2023, the activities of the expert group will be centered around the topic of “responsible AI”. Due to the recent attempts of the European union to put forward AI regulation, many companies are starting to question the practical implications. Thus, the expert group aims to support the clarification process by focusing on the potential compliance challenges and related consultation needs companies may have. The expert group thus aims to organize a series of events and other activities (e.g., member survey) to first assess the compliance challenges associated with the current draft of the EU AI regulation and then to discuss the concrete needs that follow from those challenges. A detailed program will be worked out in Q1 2023 by the expert group leaders.

Innovation = Risk + (Crazy?) Value Creation

“Innovation is going beyond state of the art – which means risk” – Anton Demarmels, Swissmem

“Innovation heisst, die Grenzen des State of the Art zu überschreiten – und das bedeutet Risiko” – Anton Demarmels, Swissmem.

The well-known song, 12 Days of Christmas, is heard around this time of year, just as we at the Databooster were “gifted” 12 idea talks at the every first Project Day and Christmas Lounge event. The Databooster ideas were in various phases of development across the data clusters “Industry 4.0”, “Smart Services”, “Ethics”, and “Sustainability”.

Das bekannte Lied “12 Days of Christmas” hört man um diese Jahreszeit, und auch wir beim Databooster wurden am ersten Project Day und der Weihnachtslounge mit 12 Ideenvorträgen “beschenkt”. Die Databooster-Ideen befinden sich in verschiedenen Phasen der Entwicklung in den Datenclustern “Industrie 4.0”, “Smart Services”, “Ethik” und “Nachhaltigkeit”.

What set this event apart from usual success stories was the melting pot of – at first glance – bizarre but brilliant future innovation ideas generated during several rounds of breakout sessions. Sure, data can be used for predictive maintenance on infrastructure, and sensors could detect changes in living organisms such as plants. But what happens if we let the boundaries of normal brainstorming fall away to broaden the group idea horizon? Imagine a technology that would allow sustainable, plant-based or wooden infrastructure, such as bridges, to be built, which would employ drones to survey potential maintenance spots, fire off a signal to the relevant sensors, and the plant-based infrastructure could regrow those areas of concern.

Was diese Veranstaltung von den üblichen Erfolgsgeschichten abhebt, war der bunte Mix aus – auf den ersten Blick – skurrilen, aber spannenden Ideen für Zukunftsszenarien, die in mehreren Runden von Breakout Diskussionen entwickelt wurden. Klar, Daten können für die vorausschauende Wartung von Infrastrukturen genutzt werden, und Sensoren könnten Veränderungen in lebenden Organismen wie Pflanzen erkennen. Aber was passiert, wenn wir die Grenzen des normalen Brainstormings ausdehnen, um den Ideen-Horizont der Gruppe zu erweitern? Stellen Sie sich eine Technologie vor, die den Bau nachhaltiger, pflanzlicher oder hölzerner Infrastrukturen wie z. B. Brücken ermöglicht, bei der Drohnen eingesetzt werden, um potenzielle Wartungsstellen zu überwachen sowie Signale an die entsprechenden Sensoren zu senden, so dass die pflanzliche Infrastruktur die betreffenden Bereiche regenerieren könnte.

Why bother generating such wild ideas across separate industries? Because innovation can’t happen without bold steps. And bold, risk-oriented action can’t happen with a limiting mindset or an isolated environment. The Databooster puts the right people together to enable limits to fall away, ideas to be tested, pushed, and refined into tangible innovation.

Warum macht man sich die Arbeit, solch wilde Ideen quer durch verschiedene Branchen zu kreieren? Weil es ohne wagemutige Schritte keine Innovation geben kann. Und kühnes, risikokalkuliertes Handeln kann nicht mit einer einschränkenden Denkweise oder einer isolierten Umgebung geschehen. Der Databooster bringt die richtigen Leute zusammen, damit Grenzen wegfallen und Ideen getestet, vorangetrieben und zu greifbaren Innovationen weiterentwickelt werden können.

A painting is made up of both broader as well as finer brush strokes. Likewise, gaining an insider’s perspective on the broad range of stages in the ideas’ development enabled the participants to see the bigger picture of the Databooster program – with stumbling blocks experienced and successes celebrated.

Ein Gemälde ist ein Werk, das sowohl aus breiteren als auch aus feineren Pinselstrichen besteht. Genauso ermöglichte die Insider-Perspektive auf das breite Band der Ideen-Entwicklungsphasen den Teilnehmern, das Gesamtbild des Databooster-Programms zu erkennen – samt erlebten Stolpersteinen und gefeierten Erfolgen.

Let’s get into the specific talks.

Ideas such as “Predictive Maintenance for wind machines” by SSM Schärer Schweiter AG and “Cavity pressure-based machine learning for advanced injection molding processes” by Kistler Group have already conducted Deep Dives and evaluated their data. SSM found that their data does not yet allow for any conclusions to be drawn about future failures, so they must explore further avenues. URMA AG Tools and Machining have also already dug extensively into their idea; collected, evaluated and gained insights from initial data gathered.

Kommen wir nun zu den einzelnen Vorträgen.

Ideen wie das Thema “Predictive Maintenance for wind machines” der SSM Schärer Schweiter AG und “Cavity pressure-based machine learning for advanced injection molding processes” der Kistler Gruppe haben bereits Deep Dives durchgeführt und ihre Daten ausgewertet. SSM hat festgestellt, dass ihre Daten noch keine Aussagen über zukünftige Ausfälle zulassen, weshalb sie weitere Ansätze untersuchen müssen. Auch die URMA AG Tools and Machining hat sich bereits intensiv mit ihrer Idee auseinandergesetzt, erste Daten gesammelt und ausgewertet. Sie konnten bereits daraus Erkenntnisse gewinnen.

Certain ideas are at the stage where the technological feasibility is investigated and the data models to be used are being researched. One of the start-ups, Vivent, talked about a plant stress algorithm and sensors that would read the electric radiation emitted from plants to detect the stress situation of the plants, e.g. even in mild droughts, to alert commercial farmers. Such projects are particularly exciting, as they have great innovation potential due to the research still being conducted.

Einige Ideen befinden sich in der Phase, in der die technologische Machbarkeit untersucht wird und die zu verwendenden Datenmodelle erforscht werden. Eines der Start-ups, Vivent, sprach über einen Algorithmus zur Erkennung von Pflanzenstress und Sensoren, die die von den Pflanzen abgegebene elektrische Strahlung messen könnten, um die Stresssituation der Pflanzen zu erkennen, z.B. bereits bei milden Dürreperioden, um Landwirte zu warnen. Solche Projekte sind besonders spannend, da sie aufgrund der noch laufenden Forschung ein hohes Mass an Innovationspotenzial haben.

Lastly, there were early-stage idea stories by Swiss Re and Thinkgate, currently in the Databooster Shaping Stage. They mapped out the stakeholder’s and customers’ needs and laid out exactly how their data-based innovation projects bridge them. The overviews respectively included automating benefit identification in insurance, and centralizing data on flight irregularities and mitigating services thereof on a platform for consumer convenience.

Schliesslich gab es noch Ideengeschichten von Swiss Re und Thinkgate, die sich derzeit in der Databooster Shaping Stage befinden. Sie erarbeiteten die Bedürfnisse der Stakeholder und Kunden und schilderten konkret, wie ihre datenbasierten Innovationsvorhaben diese überbrücken. Zu den jeweiligen Ideen gehörten die Automatisierung der Leistungsidentifizierung in der Versicherungsbranche und die Zentralisierung von Daten über Flugunregelmässigkeiten und deren Abhilfe in einer Plattform, um den Verbrauchern die Neu-Orientierung zu erleichtern.

The festive apèro, dotted with fine Christmas “Guetzli”, led to the participants mingling, exchanging their impressions, and gave rise to the opportunity for further synergies. The event closed with feedback from the participants.

Der festliche Apéro, bestückt mit feinen Weihnachtsguetzli, bot den Teilnehmerinnen und Teilnehmern die Möglichkeit, sich zu begegnen, ihre Eindrücke auszutauschen und weitere Synergien zu knüpfen. Die Veranstaltung schloss mit einem Feedback der Teilnehmer.

“The Databooster builds a platform to guarantee exchanges.”“One always sees the same ideas being presented – except here! It’s astounding how unusual some of the innovation ideas across the panel were.”“It’s interesting how the applications proceeded in how they are able to move ideas forward across such a variety of industries. Coming to this event, one truly sees that there’s a real community to help you”.

“Der Databooster baut eine Plattform, um den Austausch zu garantieren.””Man bekommt immer die ähnlichen Ideen präsentiert – ausser hier! Es ist beeindruckend, wie originell einige der Innovationsideen im gesamten Forum waren.””Es ist spannend, wie die Bewerbungen verlaufen sind, wie sie Ideen in so unterschiedlichen Branchen vorantreiben können. Bei dieser Veranstaltung sieht man, dass es eine echte Gemeinschaft gibt, die einen unterstützt”.

Thank you to the participants for their active engagement! Missed out? Don’t worry, we are hosting the next Project Day on April 18, 2023!

Vielen Dank an die Teilnehmer für ihr aktives Engagement! Sie haben es verpasst? Keine Sorge, wir laden Sie herzliche zum nächsten Projekttag am 18. April 2023 ein!

Dilemma between value creation and value destruction with data

By Jürg Meierhofer, ZHAW

Successful Databooster presentations at the “smart maintenance insights” conference

On November 23, the “smart maintenance insights” conference was held in collaboration with easyfair. The Databooster framed the event with presentations by Andrew Paice and Jürg Meierhofer, who highlighted the focus topic “Dilemma between value creation and value destruction with data”.

Andrew Paice opened the presentation series with the topic “What data is enough for smart maintenance?”. The presentation started from the common view that data is seen as a panacea – “If I have enough data, I can do anything with machine learning – e.g. smart maintenance”. He outlined that, in contrast, more information does not necessarily lead to better decisions. These issues are particularly pressing in maintenance, where you really need the right data to make good decisions. How do you know if you have the right data or enough data? In the presentation, the use of machine learning was discussed and explained with examples from research.

After two very interesting industrial contributions by Thomas Faulhaber from Membrain Switzerland and Dominik Doubek from Sonic Technology AG, Jürg Meierhofer closed the arc with his presentation “How do Smart Service create sustainable value?”. Even if sufficient data of good quality is available, business-relevant value is not automatically created for the economic actors. When using so-called smart services (data-driven services), the goal must be to increase performance and reduce risks for the diverse actors in an ecosystem. In addition, smart services allow providers to differentiate themselves and strengthen customer relationships. Thus, the use of data for novel services has great strategic importance. However, without knowing the value of their data, it is difficult for companies to make the decision for the potentially large investments in its collection and processing.
The presentations are available on youtube:

Dilemma zwischen Wertschöpfung und Wertzerstörung mit Daten

Von Jürg Meierhofer, ZHAW

Erfolgreiche Databooster Präsentationen an der Konferenz “smart maintenance insights”

Am 23. November fand in Zusammenarbeit mit easyfair die “smart maintenance insights” Konferenz statt. Der Databooster umrahmte den Anlasse mit Präsentationen von Andrew Paice und Jürg Meierhofer, welche das Fokusthema “Dilemma zwischen Wertschöpfung und Wertzerstörung mit Daten” beleuchteten.

Andrew Paice eröffnete die Vortragsreihe mit dem Thema “Welche Daten reichen für smart maintenance?”. Das Referat ging aus von der verbreiteten Ansicht, dass Daten als Allheilmittel angesehen werden – “Wenn ich genug Daten habe, kann ich mit Machine Learning alles machen – zB Smart Maintenance”. Er legte dar, dass hingegen mehr Informationen nicht unbedingt zu besseren Entscheidungen führen. Diese Fragen sind besonders dringlich in der Instandhaltung, wo man wirklich die richtigen Daten braucht, um gute Entscheidungen zu treffen. Wie weiss man ob die richtigen oder genügend Daten hat? Im Vortrag wurde der Einsatz von maschinellem Lernen diskutiert und anhand von Beispielen aus der Forschung erläutert.

Nach zwei sehr interessanten Praxisbeiträgen von Thomas Faulhaber von Membrain Switzerland und Dominik Doubek von Sonic Technology AG schloss Jürg Meierhofer den Bogen mit seinem Referat “Wie schaffen Smart Service nachhaltig Wert?”. Auch wenn genügend Daten in guter Qualität vorliegen, entsteht nicht automatisch Business-relevanter Wert für die wirtschaftlichen Akteure. Beim Einsatz sogenannter smart Services (Daten-getriebener Dienstleistungen) muss das Ziel darin bestehen, für die diversen Akteure in einem Ecosystem die Leistung zu steigern und die Risiken zu reduzieren. Zudem können sich die Anbieter mit smart Services differenzieren und die Kundenbeziehung stärken. Die Nutzung von Daten für neuartige Services hat somit grosse strategische Bedeutung. Ohne den Wert ihrer Daten zu kennen, ist es für die Unternehmen aber schwierig, den Entscheid für die potenziell hohen Investitionen in deren Erhebung und Verarbeitung zu treffen.

Die Vorträge sind auf youtube verfügbar:

Geospatial insights for all – from unique applications to future trends

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

The Expert Group Spatial Data Analytics used the 2022 General Assembly of the Data Innovation Alliance in Zurich to organise an expert meet up beforehand – and 18 experts from research and industry took the opportunity and participated in the event. The aim of this event was, on the one hand, to identify topics of particular interest for the spatial data community, which will then be taken up at special events in 2023. On the other hand, current trends in the field of geodata and applications/solutions related to geodata were presented and discussed. The meeting was concluded with the presentation of exciting data sets and tools that are of great importance in the current work of the participants.

In the area of trends, possible thematic clusters of particular interest were outlined, developments in methodological approaches were presented and new approaches to solutions and applications were discussed.

(© Zhu Difeng – AdobeStock)

In the context of the UN’s Sustainable Development Goals (SDGs), the Disaster Mitigation and Response theme complex stands out – themes, that are also of central importance in Switzerland and where geodata and their use/analysis are key to protecting the environment, infrastructure, and the population. This is linked to the wide field of Location Intelligence, e.g., visualizing (You all know heat maps, don’t you?) and analysing volumes of spatial data (often linked with non-spatial data), to enable holistic planning, insights for problem-solving, and advanced spatio-temporal forecasting.

Regarding data acquisition and evaluation, many new sensors, algorithms, and software packages are currently being developed in the field of 3D representation. This applies not only to the functionalities in existing solutions (e.g., 3D-GIS), but also to the linking of spatially explicit information with, e.g., the classic 3D model approaches in infrastructure planning (BIM) – with which we have gained another concept in the spatial universe: GeoBIM.

A lot of data means new possible approaches – and more and more use is being made of Machine Learning (ML) methods. But ML has very specific requirements for the data to unfold its full potential. One way to meet these requirements is to generate so-called Synthetic Data. This can not only help with an insufficient data basis, but also anonymise data in such a way that an exchange beyond the boundaries of one’s own organisational unit is possible even when working with sensitive information.

Also very exciting are the developments around SaaS applications and No-Code platforms, which will certainly lead to a strong increase in the use of spatial data. With the Metaverse, an additional field of development has opened in the last few months, which enables the spatialisation and visualization of our online activities. Hype, bubble, or opportunity – we will see.

New ideas, research projects and exciting applications were discussed in the subsequent exchange session: from the data pooling of freely available data (by Nicolas Lenz – Litix) and the integration of cloud computing services into locally running applications (by Dominique Weber – WSL), via interactive platforms for the joint work on requirements relating to the development/planning of spatial systems (Luis Gisler – cividi), to the power of customized machine learning tools in applied research (by László István Etesi – FHNW/ATELERIS). At this point, thanks to the presenters for the exciting insight!

You missed the Expert Day? – Don’t worry, there will be another one next year, along with several other exciting events on the topic of Spatial Data Analytics. Simply visit the website – and join the meet-ups where you can exchange ideas and initiate new collaborations with experts from research and industry. We are looking forward to you!

The majority of AI projects fail, right?

By Mark Pfaendler, La Mobilère

The transition towards data-driven business models has become highly relevant to most companies and industries. However, according to a Gartner publication back in 2018, managers find that 85% of AI use cases have not lived up to expectations or worse are deemed unsuccessful. Given this number being rather high, we were eager to validate this finding within our Expert Group “Data Driven Business Models” – held during the last meetup of this year – the Expert Day 2022. To bring our experts together, we organized a well visited workshop with 25 experts & practitioners from both industry & academia.

Our main undertaking was to first discuss the high failure rate reported by Gartner, followed by drivers behind successful AI projects. For the latter, we developed an AI project assessment framework covering 5 dimensions (Strategy, Talent & Leadership, Ways of Working, Data & Governance, Technology & Tooling). Each dimension comprises a set of best practices serving as prerequisites for running AI projects successfully, which D ONE has collected over the years from working with numerous clients from different industries. In this blog post, we would like to summarize the most important talking points & key takeaways.

First things first, our experts were skeptical about the AI project failure rate of 85%. In their opinion, a failure very much depends on how it is defined & the perspective the responsible team together with stakeholders take on failure in general. This is something interesting to better understand for which we will collect responses from a wider audience within our network.

The main goal of the workshop however was to discuss the AI project assessment framework. As an exercise, we asked the participants to apply the framework to their project experience & prioritize the set of best practices per dimension. The following table summarizes the results collected.

To wrap up, our 3 key takeaways from this workshop are:

  1. An AI project failure rate of 85% has been heavily challenged – this needs further investigation.
  2. According to our respondents, managing stakeholders & their expectations seems to be the strongest driver of AI project success.
  3. The project assessment framework was well received which allows us to go more in detail next time & start conducting a survey to collect solid results worth sharing with the public.

The workshop was the perfect opportunity to sound & refine the presented AI project assessment framework with industry experts who deal with success stories & challenges day by day. With this, we would like to thank all participants for the active discussion & contribution to make this workshop a success. We will keep you posted on the next steps taken!

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