The construction industry has been undergoing a digital transformation, with data science playing an increasingly important role in improving efficiency, reducing costs, and enhancing safety. However, to fully realize its potential, it’s essential to promote open innovation and collaboration between data scientists and construction experts.
For this purpose, the first open innovation workshop on “Construction & Data Science” was recently held in Zurich, Switzerland, bringing together 40 participants from both fields to explore the intersection of construction and data science.
Findings
To improve cooperation between the construction industry and data science, the workshop formulated central key findings:
Focus on creating structured data sets across projects, teams, and companies and move away from managing data in CAD systems.
Don’t wait for new standards to be defined, learn to combine information from different sources and standards by mapping it.
Consider using other ways of pulling data from CAD tools, like Speckle or an IFC-based modeling tool like Blender.
Engage in open source or open data projects and learn from others, especially in rapidly evolving technology.
Check out sites like www.opensource.construction/ to proactively work towards changing the status quo and improving the industry.
The summary is also nicely put together in the Linkedin post by opensource.construction’s President, Maximilian Vomhof.
The Event The workshop aimed to identify potential problems and solutions in this area and to promote open innovation and collaboration between data scientists and construction experts. In this blog post, you will find a report on the insights gained from the workshop and highlight the importance of open innovation in these industries.
The workshop was organized jointly by opensource.construction, bauen digital Schweiz, and the data innovation alliance at Amstein + Walthert in Oerlikon. After a short introduction to the Databooster program, the passive part for the participants was over!
The workshop was structured according to the double diamond framework from design thinking, which consists of four stages: discover, define, develop, and deliver. After the four groups got to know each other, they explored the problem space at the intersection of construction and data science or data engineering. In this phase, the focus was on generating a wide range of ideas and potential solutions by asking “why” questions and understanding the workings of the construction industry as a data scientist – essentially, “falling in love” with the problem.
At this workshop, “love” was certainly in the air, with many questions generated. Of these, five problems were identified as most relevant:
Optimized building scans
Open-source door planning configurator
Linked product data
GIS data linked with building laws
Open data environment for storing and linking element-independent information
In the second part of the workshop, powerful teams were formed around the ideas generated. They not only focused on potential solutions but also on related business cases, which added a practical, pragmatic dimension to the ideation process.
One of the solutions produced was standardization, particularly in the light of data formats. However, it was agreed that establishing standards and achieving consensus could take a long time – a luxury that the construction industry simply doesn’t have. It was essential to identify quick and effective solutions that could be implemented without major delay.
Despite the apero long being ready, the teams were still deeply engaged in finding innovative solutions that could be launched swiftly. The teams agreed to follow up on their ideas and continue pushing for change, possibly using the Innovation Booster Databooster, something we are particularly happy to hear.
In conclusion, we believe that the Innovation Booster Databooster initiative is a valuable resource for teams to develop and implement their solutions generated using the double diamond design thinking structure. We look forward to seeing how these ideas will shape the future of construction and data science!
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.
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:
The Data Innovation Alliance’s second Expert Day in March 2023 was a hub of activity as experts from four key areas – Smart Maintenance, NLP & AI Technology, Spatial Data, and Smart Services – gathered to share their insights and mingle with researchers and industry professionals. The event kicked off with leaders from each Expert Group pre-discussing their plans for 2023, generating a wealth of innovative ideas for joint events and initiatives, and paving the way for exciting collaborations in the (near) future.
But that’s not all! The NLP and Digital Health groups are teaming up to bring you joint events that will revolutionize the way we approach data. And with the next Expert Day set for August 2023, featuring four expert groups once again, get ready for even more ground-breaking discussions and initiatives, organized jointly with other Innovation Boosters. Keep an eye on our events calendar for more information.
While the keynote speech may not have met expectations in terms of insights, it set the stage for what was to come – dynamic discussions and collaborations in the expert group break sessions. To ensure everyone had access to the wealth of information shared, short summaries of the discussions were written by participants in each room.
In short, the second Expert Day was a superb success, bringing together a diverse group of experts to debate their ideas and shape the future of data innovation.
Smart Services for Sustainability – Circular Servitization by Jürg Meierhofer
The Smart Services for Sustainability – Circular Servitization discussion was a dynamic conversation among highly experienced individuals from different industries. They explored how value is created in business ecosystems, focusing on both individual and organizational perspectives.
It was inspiring to have diverse industry representatives in the same room and to create a common understanding. Departing from economic value creation, the group extended its scope to ecological factors. An intense discussion arose about how environmental value can be created without negatively impacting economic value. Statements that economic value creation is still the predominant requirement were made, meaning that in many cases, even a slight reduction of economic value for the sake of ecological value would be treated with suspicion. As sustainability becomes increasingly relevant and regulations loom, the balance between economic and ecological value may shift in the near future.
Overall, the Smart Services for Sustainability – Circular Servitization discussion was thought-provoking and left participants eager to continue exploring the intersection of business and sustainability.
Spatial Data by Reik Leiterer
In a room buzzing with ideas, each data expert chimed into the discussion about the creation of a platform that would benefit cantons, individuals, and service providers. There was a shared understanding that it might not be possible to cater to everyone’s needs and that a simpler visualization and analytics approach may be the way forward. However, some uncertainties still remained, such as identifying where the necessary data is available and how it can be integrated, setting limits, and ensuring that data is not misinterpreted. Despite these challenges, the group remained enthusiastic about the potential benefits of the platform and is looking forward to overcoming these obstacles.
NLP & AI Technology by Lina Scarborough
The group opened the floor with how chatbots are great to answer questions, but what happens when users don’t know where to begin asking questions? This is a common issue in legal situations where the average client may not have the necessary background to understand what information is needed. Retrieval augmented language models like KATIE have emerged as a solution to this problem. These models use grounded reasoning and promote a chain of thought to handle complex queries and create a context for users who may not know what subset of questions to ask.
With the rise of machine-generated text, it’s becoming more difficult to distinguish between human and machine-generated content. While probabilistic token selection and frameworks like SCARECROW can help scrutinize machine-generated text, it can still be difficult, to nigh impossible, to identify. However, ChatGPTZero, an app that uses watermarking to create a statistical fingerprint in the sampling method, claims to be able to detect whether an essay is written by ChatGPT or a human – for instance, ChatGPT generally makes redundancy errors whereas humans make grammatical mistakes. This approach hopes to maintain the integrity of human-generated content in the face of increased machine-generated text.
The discussion then flowed into a lively and engaging presentation on how AI technology can make the tricky SQL “minefield” as easy to navigate as a soccer player scoring a goal – literally, by demonstrating SQL prompts on the soccer World Cup!
Smart Maintenance by Melanie Geiger
The five use case presentations highlighted the versatility of data technology in different applications, showcasing how it can be adapted to meet various needs. With input data ranging from domain knowledge to error log data, these use cases demonstrated how AI models can process and analyze complex data sets to provide valuable insights and decision support.
One of the key themes that emerged was the use of AI for diverse condition-based maintenance, specifically anomaly detection and fault diagnosis. By leveraging ML algorithms, these use cases were able to detect potential issues and predict equipment failures for timely maintenance and preventing downtime.
The highlight of the event was not only the apèro treats, but the opportunity to engage with the 60 participants and learn about their projects, challenges, solutions, and ideas for collaboration. Many attendees seemed to share this sentiment, as numerous participants were still engrossed in conversation at the end of the event, and some discussions had to be continued elsewhere. Those who wish to follow up on these conversations have the option to do so at SDS2023. On a more lowkey note, maybe you wanted to add someone on LinkedIn and send them a message. Here you go, this is your reminder!
Our conclusion of the event: the Alliance has many experts in various subtopics of data-driven value creation, but only together we can move faster.
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 su
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.
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.
“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!
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: https://www.youtube.com/watch?v=Sh7VxTTFpYY&t=10s
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.
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.
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!
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