By Miriam Baumgartner, Agroscope, Stefan Rieder, Identitas and Jürg Meierhofer, ZHAW
On July 3, 2023, we conducted an insightful databooster idea shaping workshop in a domain with traditionally rather low degree of digitalization, but yet a considerable potential to create value for the actors of the ecosystem. Precision livestock farming allows the automatic monitoring of animal health and welfare. Its potential has been proven particularly in the dairy sector. Not so much attention has been paid to the digital monitoring of equine welfare, although the well- being of horses is of great concern to their owners and of public interest.
We created a layout of the business and private ecosystem and evaluated the needs terms of jobs and pains of different actors. At the same time, a strong focus was put on the economic quantification of the pains and their potential mitigation or elimination, which provided some results which were previously not obvious in their qualitative and quantitative dimension and which contribute relevant elements for a business case.
The workshop was substantially complemented by the expert knowledge from «Hochschule für Wirtschaft und Umwelt Nürtingen-Geislingen (HfWU)» and BestTUPferd GmbH (Berlin) and moderated by Jürg Meierhofer from the databooster.
Co-creation for solving tomorrow’s challenges today
On June 22nd-23rd, the IEEE 10th Swiss Conference on Data Science (SDS2023) took place in Zurich/Schlieren. The first day was dedicated to interactive workshops and held in the amazing JED event location in Schlieren. With 15 workshops, more than 350 participants and a lot of enthusiastic feedback, we can draw a very positive conclusion. At this point, we would like to thank again all workshop organizers and the spirit of all participants – without your commitment and active participation, the workshop day would not have been possible! In this blog, we would now like to briefly present 4 selected workshop formats which, within the framework of the databooster program of Innosuisse, had a particular focus on co-creation and ideation.
The workshop Responsible AI – Explainability, Transparency and Fairness of data-based applications in practice, was organized by CLAIRE (R. Chavarriaga), the Expert Group Data Ethics (C. Heitz), Eraneos (B. Müller) and the applied universities FHGR (C. Hauser) and ZHAW. The event started with short keynote speeches by Xavier Renard (AXA Group Paris) and Arman Iranfar (CertX), who addressed the challenges around the concepts and levels of fairness, non-discrimination, explainability or transparency as well as the upcoming regulatory frameworks in the frame of the AI Act and the related requirements.
Subsequently, various break-out groups were formed, and challenges and solutions with a focus on practical implementations were developed and discussed. The key challenges identified were: i) What is a structured approach to develop responsible AI?, ii) How to set up a risk assessment which is suited for addressing the risk-based approach required by the EU AI act?, iii) How can data scientist be connected with other stakeholders for making sure that the engineering of AI is fully connected with an integrated risk assessment?, and iv) How to measure the “degree of responsibility” of responsible AI?
For identified challenges for which no solution was found during the workshop, specific innovation support programs were presented offering possibilities to be able to continue the work in depth afterwards.
In The Power of Knowing Where, the participants discuss existing and brainstorm potential future use cases across various sectors. Using open-source datasets, the participants were able to implement their ideas in the subsequent hands-on session and to investigate and predict natural hazards relating to climate-change in Switzerland – from the exploration, visualization, and manipulation of location-based data towards the use of it in predictive modelling.
In Innovation & Business Cases, the participants got insights into the business potential of geospatial data and value-added service presented by Jonas Weiss (IBM). Furthermore, legal requirements on ESG reporting and risk assessments of large investments were discussed and challenges but also opportunities presented – paving the way to completely new business perspectives and new business models that can be exploited. The STDL presented success stories and use this to lead to an open and interactive discussion round allowing participants to bring their specific questions and case studies to the table for constructive feedback – and to push ideas further with new approaches, new motivation, and new contacts.
P. Hutzli and B. Russinello (la Mobilière) gave in their workshop an Introduction and ROI of Knowledge Graphs, based on three examples in watch industry, energy and insurance. They presented, why Swatch, BKW and Mobiliar have chosen Knowledge Graph technology over classical approaches to combine data and metadata from many different sources into one coherent data network. The complete process was discussed – from the initial pain points and how they built their solutions to the business cases and the positive returns on investment. Inspired by these specific journeys, the participants were motivated to identify similar pain points and use cases in their own organization, to develop a simple road map and to calculate a quick Return on Investment (ROI). Afterwards, the specific business cases were presented for immediate feedback, mutually inspiring everybody to look for new use cases.
The Expert Group Smart Maintenance (Lilach Goren Huber, Manuel Arias Chao – ZHAW) organized the workshop Deep Learning for Predictive Maintenance: Scalable Implementation in Operational Setups. In this workshop, the gap between the state-of-the-art research on the one hand, and industrial implementation, on the other hand was discussed.
One of the underlying theses for this was that the technological and algorithmic development is driven primarily by academia and less by industry which stands in contrast to other applications of DL such as image recognition, speech recognition and gaming, which are driven by industry giants like Google, Meta, or Microsoft. In a co-creation setup, the following challenges were addressed, and solutions discussed based on the use-cases of wind turbines and aircraft engines: i) dealing with the lack of labeled historical faults, ii) effective combination of domain knowledge for fault isolation, iii) upscaling the Fault Detection and Isolation (FDI) algorithms to multi-component systems, and iv) quantifying uncertainty in fault detection problems.
The SDS2023 workshop day convinced with the awesome atmosphere, highly committed workshop organizers and open-minded participants interested in exchange and cooperation! Thanks a lot for this inspiring day!
At this year’s IEEE Swiss Conference on Data Science there was a very informative workshop on Generative AI in Practice. The presentations and discussions in this workshop made clear that the generative AI technologies, and Large Language Models (LLMs) in particular, are very versatile and powerful. It also became apparent, however, that the business potential of LLMs largely remains unclear.
Figure 1: DALL-E Visualization of a Talking Machine
OpenAI’s AI chatbot ChatGPT has already gained over 100 million users within the first two months of its release. This makes this internet service the fastest growing of its kind. In comparison, the runner-up, Tiktok, took a full nine months to reach a similar number of users. The potential of the underlying technology, LLMs, is undisputedly recognized by business leaders. According to a study by Gartner among business executives1, 45% of respondents have already intensified their investment in artificial intelligence (AI) as a result of the success of ChatGPT.
LLMs are extremely large-scale artificial neural networks that are trained with terabytes of textual content to complete texts. LLMs can therefore generate new content and thus belong to the class of generative AI solutions. In contrast, discriminatory AI models can only establish assignments or classifications between different inputs and predefined outputs.
LLMs can be used for a variety of purposes, such as text summaries, sentiment analysis, or named entity recognition. Although there are already initial indications of the productivity gains that can be achieved through the use of LLMs, the role of this technology for the business models of industrial companies is still largely unclear.
The Impact of LLMs on Business Models
In the following, we present the findings derived from own prototypes and a comprehensive analysis of more than 50 real-world use cases pertaining to the application of LLMs within various companies. A distinction can be made between four mechanisms of how business models are changed with LLMs:
new customer benefits
new sales and communication channels
increased business process automation
improved use of information resources
New Customer Benefits
LLMs play a crucial role in operating personal assistance systems. Instacart, a grocery delivery service, utilizes LLMs to address nutrition queries and offer personalized product recommendations.
Furthermore, LLMs serve as personal coaches, particularly valuable in the realm of learning. Khan Academy, an educational platform, employs LLMs to detect errors in programming tasks and generate helpful solution hints.
LLMs also possess the capability to independently generate content that is relevant to customers. Copy.ai, an online service, exemplifies this by creating blog posts, social media content, and website material based on bullet-style keywords and predefined language styles.
Additionally, LLMs facilitate voice-based interactions with machines. Mercedes, for instance, integrates LLMs into the infotainment systems of their premium vehicles to provide comprehensive answers to complex customer questions while driving.
New Sales and Communication Channels
LLM-based chatbots offer significant advantages in automating sales and customer service processes. In Switzerland, the insurer Helvetia has successfully implemented an LLM-based chatbot to handle inquiries regarding their product range.
Another notable example is Solana, a blockchain operator, leveraging ChatGPT in their customer service operations. By utilizing LLM-based chatbots, Solana effectively assists customers in resolving intricate service-related challenges, ensuring a seamless user experience.
Increased Business Process Automation
LLMs offer remarkable potential in enhancing automation within information-intensive business processes. The Radisson hotel chain has effectively employed LLMs to automate the handling of customer inquiries and cancellations, enabling swift and accurate responses. Additionally, LLMs generate helpful suggestions for emails and review responses, streamlining communication and enhancing customer satisfaction.
Another notable application is observed in Swiss Migros Bank, where LLMs play a pivotal role in partially automating mortgage application processing. By intelligently recognizing case-specific requirements and evaluating text-based customer documents, LLMs assist in expediting and improving the efficiency of the application review process.
Improved Use of Information Ressources
The fourth mechanism focuses on the enhanced exploitation of information resources. Morgan Stanley, a securities trading company, exemplifies this by leveraging LLMs to facilitate employee access to and evaluation of internal documents. Through the application of LLMs, Morgan Stanley streamlines the process of retrieving and analyzing crucial information, ensuring efficiency and informed decision-making within the organization.
Likewise, Zurich Insurance capitalizes on LLMs to automate contract evaluation and ascertain the validity of insurance claims. This strategic employment of LLMs empowers Zurich Insurance to effectively and efficiently assess the presence of a claim, ultimately leading to enhanced operational processes.
Figure 2: Four Mechanisms in Osterwalder´s Business Model Canvas
Current Challenges in the Commercial Application of LLMs
When evaluating the strategic importance and necessity for action concerning the commercial application of LLMs, companies are faced with three fundamental questions.
What are technical risks associated with the use of LLMs?
One significant challenge arises from the risk of LLMs generating false or inaccurate statements, commonly known as hallucinations. However, advancements in prompt engineering, which involve carefully formulating textual instructions, have already proven effective in mitigating this risk to a considerable extent. Additionally, the development of fact-checking methods is underway to ensure that the output generated by LLMs is rooted in accurate and verified information.
Another crucial technical concern revolves around the security of sensitive data shared during the prompting process. The potential exists for malicious actors to employ targeted prompts, referred to as Training Data Extraction Attacks, to extract training data from LLMs. Consequently, it is imperative to eliminate the possibility of shared data being utilized to train publicly accessible LLMs. Alternatively, dedicated LLMs can be utilized to safeguard the confidentiality of shared data.
What legal framework conditions need to be taken into account?
When utilizing LLMs, it is essential to adhere to relevant data protection regulations, particularly if personal data is being processed. This entails fulfilling information obligations and respecting individuals’ rights to information, similar to other AI applications.
Additionally, companies need to consider the evolving legal landscape, such as the European Union’s AI Act. The current draft of the AI Act specifies certain requirements for LLMs, including the prevention of generating illegal or discriminatory content and the disclosure of copyrighted content used during training. However, a comparison of different LLMs reveals that most models do not fully comply with these requirements, particularly regarding copyright compliance. Therefore, it is crucial for companies to carefully assess and ensure compliance relevant legal obligations when making long-term technology decisions involving LLMs.
In which areas should companies invest in LLMs?
Providers of standard software and Internet services are already investing heavily in LLMs. This includes areas such as sales management, customer service, marketing and knowledge management. Non-software companies will likely source such software rather than build it themselves.
More interesting for non-software companies are application areas in which LLMs have a direct impact on their value propositions or business-critical business processes. For example, robotics manufacturer Boston Dynamics uses LLMs to enable voice-based interaction between users and machines. Ivaldi, a distributed production specialist, uses LLMs to help maintenance teams troubleshoot. Rolls-Royce uses artificial intelligence to harness unstructured data and optimize supply chain management.
These illustrations highlight the substantial innovation potential of LLMs, extending beyond software companies to various other industries. Particularly noteworthy is the possibility for non-software companies to harness this potential by reimagining user interactions or unlocking significant optimization opportunities.
Warum brauchen wir eigentlich digitale Business Ökosysteme?
Mit diesen Hypothesen sind wir in den Tag gestartet:
Kundenseitig: Kunden benötigen nicht mehr einfach Produkte für “Punktlösungen”, sondern Begleitung entlang der Customer Journey, mit über die Zeit wechselnden Bedürfnissen, die im Netzwerk eines Business Ökosystems abgedeckt werden können.
Anbieterseitig: Diesen vielfältigen Bedürfnissen können einzelne Unternehmen oft nicht gerecht werden, da sie nicht über die vielfältigen Ressourcen verfügen, sondern spezialisiert sind. Im Verbund mit anderen Unternehmen lässt sich aber die Vielfältigkeit erreichen.
Digital: die Kundeninteraktion sowie die Organisation der Ökosysteme lassen sich über digitale Plattformen und Schnittstellen effizient und mit Skaleneffekten implementieren.
Die gehaltvollen und lebhaften Referate sowie der interaktive Workshop haben diese Hypothesen über den Tag hinweg wiederholt aus verschiedenen Blickwinkeln beleuchtet und interpretiert. So ergab sich bis zur Abrundung des Tages vor dem Abschluss-Apéro ein umfassender Eindruck der Leistungsfähigkeit von Business Ökosystemen, wovon hier nur ganz rudimentär und ohne Anspruch auf Vollständigkeit ein Eindruck wiedergegeben werden soll:
Ökosysteme bieten einen Mehrwert für alle Beteiligten und unterstützen die Ökologie.
Sie festigen die Kundenbindung und bieten direkte und indirekte Netzwerkeffekte.
Ein Produkt mit einer DNA versehen für die Rückverfolgbarkeit und die Kunden für den Wert davon sensibilisieren.
Ein Ökosystem für die “smarte” Gestaltung einer geographischen Region (vs. ein transaktionales Ökosystem).
Mit einem Ökosystem den KMU Kunden alles rund um ihre Administration abnehmen und sich dabei nicht selber ins Zentrum setzen (Anmerkung der Redaktion: “Ecosystem vs. Egosystem”!).
Ein vorerst digital aufgebautes Ökosystem zur Vernetzung von Robotern mit dem Potenzial, später menschliche Aktivitäten (z.B. Servicepersonal) zu integrieren.
Durch Transparenz im Ökosystem Kosten sparen und dieses resilienter machen gegen externe Schwankungen.
Der Workshop hat den Konferenz-Teilnehmenden die Möglichkeit geboten, ihre eigenen Challenges einzubringen und diese aus den Perspektiven Ressourcen, Geschäftsmodellen und Datennutzung zu beleuchten. Daraus sind zahlreiche potenzielle Projektideen entstanden, die nun auf eine Weiterverfolgung warten, z.B. im Databooster Innovationsprozess (https://databooster.ch/innovation_process/).
Herzlichen Dank an alle Referierenden (Reihenfolge gemäss Programm): Andrin Egli – Swisscom, Amrit Khanna – Concircle, Oliver Walter & Filipa Pereira – Rieter / Haelixa, Pascal Gurtner – Smarter Thurgau, Natalie Jäggi & Linus Schenk – Die Mobiliar, Gundula Heinatz Bürki – databooster & data innovation alliance, Marc Wegmüller – Wegmüller AG, Remo Höppli – Earlybyte & Kemaro, Rainer Deutschmann – Migros.
6th Conference Perspectives with Industry 4.0: Digital Ecosystems – May 31, 2023, Winterthur
By Jürg Meierhofer, ZHAW
Why do we actually need digital business ecosystems?
We started the day with these hypotheses:
On the customer side: customers no longer simply need products for “point solutions,” but rather assistance along the customer journey, with needs that change over time, which can be covered in the network of a business ecosystem.
On the supplier side: Individual companies often cannot meet these diverse needs because they do not have the diverse resources but are specialized. However, in association with other companies, variety can be achieved.
Digital: customer interaction as well as ecosystem organization can be implemented efficiently and with economies of scale via digital platforms and interfaces.
The substantial and lively presentations as well as the interactive workshop throughout the day repeatedly illuminated and interpreted these hypotheses from different angles. Thus, by the rounding off of the day before the closing aperitif, a comprehensive impression of the performance of business ecosystems emerged, of which only a very rudimentary and non-exhaustive impression will be given here:
Ecosystems offer added value for all actors involved and support ecology.
They strengthen customer loyalty and offer direct and indirect network effects.
Digital partner integration enables circular economy (keyword “fully loaded trucks”).
Adding a DNA to a product for traceability and making customers aware of the value of it.
An ecosystem for “smart” design of a geographic region (vs. a transactional ecosystem).
Using an ecosystem to relieve SME customers of everything around their administration, while not putting themselves at the center of it (editor’s note: “ecosystem vs. egosystem”!).
An ecosystem built digitally for the time being to connect robots with the potential to integrate human activities (e.g. service personnel) later on.
Saving costs through transparency in the ecosystem and making it more resilient to external fluctuations.
The workshop gave the participants the opportunity to bring in their own challenges and to address them from the perspectives of resources, business models and data use. Numerous potential project ideas emerged from this, which are now waiting to be followed up, e.g. in the databooster innovation process.(https://databooster.ch/innovation_process/).
With the CAS Smart Service Engineering of ZHAW School of Engineering we had the chance to spend two days this week at the Mobiliar Forum Thun (many thanks Fabrizio Laneve) . Moderated by Ina Goller in very focused and agile way, we further developed and refined the mutual value creation in the ecosystems of our service innovation cases.
It turned out that ecosystem design is a) extremely important for value creation, b) not trivial, c) creates new resources by successfully integrating existing resources. Our ambition was to quantify the value stream in the ecosystems. It turned out that while even the quantification of economic value is very demanding, but often possible, the quantification of social and emotional value streams is a widely open field which requires further research. We are looking forward to this challengin yet extremely rewarding work.
The practical conference on digital ecosystems in Winterthur just before this workshop (http://www.zhaw.ch/i40konferenz) was the ideal kick off to this workshop, in which we all became co-creative ecosystem designers ourselves.
By Manuela Hürlimann, ZHAW and Thomas Zaugg, Roche
On May 10th, 2023, the “Natural Language Processing in Action” Expert Group of the data innovation alliance and SwissNLP organised a meeting in Zurich with three exciting presentations on speech processing.
Oscar Koller, a principal applied scientist at Microsoft, presented on the use of end-to-end neural systems for automatic speech recognition in Swiss German. He discussed how the current industry paradigm of hybrid ASR is being replaced with end-to-end models, such as those that have been winning recent benchmarks. Oscar shared the results of his team’s comparison of different neural network architectures, and highlighted the advantages of using transducers for improved real-time performance in their work.
Claudio Paonessa, a researcher at FHNW, discussed how recent advances in speech-to-text, text-to-speech, and translation for Swiss German can be combined with a large language model to create a voice-based conversational assistant. He shared a demo of the model in action, showcasing its ability to give apt replies. However, he also acknowledged that processing time still needs to be reduced to give a real-time feeling, and suggested reducing model size as one possible solution.
Dr. Edith Birrer, a senior researcher at iHomeLab, HSLU, presented results from her team’s work on using speech processing in the context of home care. Together with international project partners, they ran interviews and workshops to identify potential use cases for home care workers. While they had originally planned to focus on care documentation, their results showed that most care workers found supporting services – such as a to-do list that can be ticked off verbally – to be more useful. They implemented three use cases and tested them in a lab with carers, showing a high level of enthusiasm among users, but emphasizing the need to address data privacy concerns before such technologies can become widely used.
After the presentations, attendees enjoyed an apéro and continued discussing the topics at hand.
By Patricia Deflorin and Reik Leiterer, databooster, Photos by Industrie 2025
Mastering current challenges with foresight
On the 9th of May, the 10th edition of the “Industrieforum 2025” by Industrie 2025 focused on challenges and solutions related to digital transformation and innovation in the industry sector. Adam Gontarz, President of Industrie 2025, opened the event (btw fantastically moderated) with a look back at 10 years of the Industrieforum and why the topics of that time are still highly relevant today.
One of the program highlights was already reached with the keynote by Dr. Thomas Zurbuchen, several years head of science at NASA. In a convincing manner, he got to the heart of the drivers of innovation without forgetting to discuss the related challenges from different perspectives. With his personal lessons learned and a 500-year-old quote by H. Cortez (1519), which is remarkable in its actuality regarding disruptive innovation, he gives an extremely inspiring start to the day:
“To lead change, be willing to burn your ships in the harbour.”
From our perspective as a network for data innovation and data-driven value creation, the subsequent presentations offered an exciting insight into the increasing importance of data and the associated fields of application in industry/manufacturing. Johann Hoffman from MR Maschinenfabrik Reinhausen, for example, highlighted the need for data governance, without which Industry 4.0 cannot be successfully implemented. From cybersecurity and digital, economically sustainable services in mechanical engineering to the concept of meta factory and the use of AI – the data reference was strongly present in almost all presentations. That’s why the databooster program exists – to encourage the development of innovative ideas in this area!
On the exhibitor side, there were exciting players in the data field to discover. CLUE Security Service AG, for example, presented operational approaches for early detection of IT threats and the protection against attacks – both for critical local infrastructure and for web-based infrastructure. OCTOTRONIC GmbH offers comprehensive solutions in the frame of an Analytics Landscape for process monitoring, data management and planning optimization. And akenza.io supports the manufacturing industry by adding remote monitoring and predictive maintenance capabilities to existing products and equipment based on their low-code IoT platform with visualization and data processing capabilities.
The event convinced with the awesome atmosphere, the very good speakers, open-minded participants interested in exchange and cooperation and a perfect organization. Therefore: we are looking forward to the 11th edition!
May 3, 2023 – By Gundula Heinatz Bürki and Reik Leiterer, data innovation alliance. Photos Markus Burren, Oracle
How can the databooster, the innovation program of data innovation alliance powered by Innosuisse, help to smooth the path for the first steps of innovative ideas – and most importantly, lead to the right direction in the process?
10 innovation teams presented their Databooster-funded ideas on the 3rd of May, directly opposite the Zurich airport at the beautiful Oracle venue. At this point – many thanks to Oracle, our host on this day. After the amiable welcome by the Oracle team, the participants got insightful presentations about the process and the first outcomes of the individual innovation journeys as well as the future path and direction now being taken.
The first block of presentations covered the potential of customized holistic energy solutions (Endaprime), challenges around the complexity of manufacturing process data (CSEM), and the development of tools for the automated detection of fake news (ZHAW). Following a Q&A session, the participants particularly appreciated the exchange of experiences on the problems encountered and the related solutions, as well as the open communication about wrong directions and obstacles that still exist.
This lived and shared culture of failure is very much in line with our understanding of innovation. The block was concluded with a presentation by Andreas Steiner and Patrick Jung from TEK, an organization that facilitates the transfer of knowledge from universities to SMEs in the industrial sector and supports the rapid and economically sustainable implementation of innovation projects.
After a short interactive sequence on the concept of future thinking and foresight, another block of presentations followed. These included a presentation on intelligent energy systems in the field of e-mobility (eneXan), a framework for standardized and automated ESG reporting (Ascentys), and the ethical and social requirements for the use of AI in law enforcement (Eraneos).
The lively discussions during the networking coffee break about the presented projects and ideas had to be interrupted at some point, however, as the last block of presentations also provided exciting insights into the work of the innovation teams.
Starting with a radical approach for achieving the optimum in terms of collaboration and operational efficiency from a company’s workforce (Peerdom), through automated risk analysis in data projects (Xurce) and the support of companies in the digitization process via an independent and open consulting network composed by agile and specialized consulting cells (Swiss Digital Network), to support decision tools for health care providers (Rewoso).
During the subsequent aperitif, new contacts were made and the next steps and possible collaborations were discussed. The variety of project ideas and their high degree of innovation, the noticeable motivation and enthusiasm of the various innovation teams, and the number of already successfully implemented solutions have shown that we have taken the right path with the databooster approach of sharpening the ideas and providing financial support for the first steps of innovation. Maybe next time you are one of the presenting teams? – If you are interested, please contact us or visit our website!
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.
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