This presentation (in German) introduces consortium research as a method for conducting design science research (DSR) in collaborative environments with partner companies from various industries. The presentation outlines the fundamental principles of consortium research, illustrates the method using examples from the Competence Center Corporate Data Quality (CC CDQ) and identifies three questions for discussion. The presentation was held at the 2015 International Conference on Wirtschaftsinformatik in Osnbrück.
NeoDash - Building Neo4j Dashboards In MinutesNeo4j
NeoDash is an open-source, low-code dashboard builder for Neo4j that allows users to build interactive dashboards with graphs, tables, charts and other visualizations using Cypher queries. It has over 500 active users across 50 countries. Dashboards created in NeoDash can be customized and published to predefined users. The presentation demonstrates how to use NeoDash to build dashboards from Neo4j data in minutes without extensive programming knowledge. Support options for NeoDash include training, extensions and help with installation from Neo4j professional services.
House Temperature Monitoring using AWS IoT And Raspberry PiRoberto Falconi
Brief description and useful links:
Developed smart home automation project to measure your house's temperature and send it on your smartphone.
LinkedIn profile: https://www.linkedin.com/in/roberto-falconi
GitHub repository: https://github.com/RobertoFalconi/HouseTemperatureMonitoring
Hackster full description: https://www.hackster.io/Falkons/house-temperature-monitoring-using-aws-iot-and-raspberry-pi-3b6410
SlideShare presentation: https://www.slideshare.net/RobertoFalconi4/house-temperature-monitoring-using-aws-iot-and-raspberry-pi
YouTube video: https://www.youtube.com/watch?v=gQxOSbcN79s
Bi Architecture And Conceptual FrameworkSlava Kokaev
This document discusses business intelligence architecture and concepts. It covers topics like analysis services, SQL Server, data mining, integration services, and enterprise BI strategy and vision. It provides overviews of Microsoft's BI platform, conceptual frameworks, dimensional modeling, ETL processes, and data visualization systems. The goal is to improve organizational processes by providing critical business information to employees.
Digital Transformation at the University of EdinburghMark Ritchie
This slide set provides an overview of the Digital Transformation portfolio of projects being led by Information Services at the University of Edinburgh. The slide set provides the current position on 30th April 2017. We'll update the slideset as work progresses.
Digital Transformation is about more than just technology. Our goal is to adopt new "digital first" ways of working which leverage technology, further our mission and provide a world class experience for our students and staff. Digital Transformation is a major, multi-year portfolio of programmes and projects. The current programmes are:
- Enterprise APIs - to develop a flexible and secure API framework to support development and deployment of user focused solutions more quickly and at lower cost
- Enterprise Data Warehouse - to provide a standards based, flexible and integrated platform for business intelligence and management information
- User Experience Services - to improve the digital experience by focusing on user-centred design through consistent standards, user experience services and training
- User Centred Portal and Notifications - to develop a new user-centred interface for the University Enterprise Portal (MyEd)
- Engagement - to drive engagement with Digital Transformation across the University and provide a governance framework for projects that are not part of other programmes
A further tranche of programmes and projects will come on stream later in 2017 with a the portfolio of work to be completed by Summer 2020.
Your Roadmap for An Enterprise Graph StrategyNeo4j
This document provides a roadmap for developing an enterprise graph strategy with the following key steps:
1. Design and build a proof-of-concept graph using a small local dataset to demonstrate graph capabilities.
2. Present use cases and example queries to business stakeholders to validate the graph model and gather feedback.
3. Design the production graph schema and build APIs/services to integrate data from multiple sources.
4. Deploy the graph in the cloud and develop applications and reports to mobilize enterprise data using the graph.
As the author of “Big Data in Healthcare Hype and Hope,” Dr. Feldman has interviewed over 180 emerging tech and healthcare companies, always asking, “How can your new approach help patients?” Her research shows that data, as an enabling tool, has the power to give us critical new insights into not only what causes disease, but what comprises normal. Despite this promise, few patients have reaped the benefits of personalized medicine. A panel of leading big data innovators will discuss the evolving health data ecosystem and how big data is being leveraged for research, discovery, clinical trials, genomics, and cancer care. Case studies and real-life examples of what’s working, what’s not working, and how we can help speed up progress to get patients the right care at the right time will be explored and debated.
• Bonnie Feldman, DDS, MBA - Chief Growth Officer, @DrBonnie360
• Colin Hill - CEO, GNS Healthcare
• Jonathan Hirsch - Founder & President, Syapse
• Andrew Kasarskis, PhD - Co-Director, Icahn Institute for Genomics & Multiscale Biology; Associate Professor, Genetics & Genomic Studies, Icaahn School of Medicine at Mt. Sinai
• William King - CEO, Zephyr Health
New York eHealth Collaborative Digital Health Conference
November 18, 2014
The document discusses how graph databases are well-suited for telecommunications networks due to their ability to model complex relationships. It provides examples of large telecom companies using Neo4j to power applications involving network topology analysis, digital twins, and other use cases. Key benefits highlighted include stronger support for dynamic connectivity modeling, improved path calculation, and more flexible approaches compared to relational databases. The presentation outlines Neo4j's adoption among top telecom firms and leadership in the graph database market.
NeoDash - Building Neo4j Dashboards In MinutesNeo4j
NeoDash is an open-source, low-code dashboard builder for Neo4j that allows users to build interactive dashboards with graphs, tables, charts and other visualizations using Cypher queries. It has over 500 active users across 50 countries. Dashboards created in NeoDash can be customized and published to predefined users. The presentation demonstrates how to use NeoDash to build dashboards from Neo4j data in minutes without extensive programming knowledge. Support options for NeoDash include training, extensions and help with installation from Neo4j professional services.
House Temperature Monitoring using AWS IoT And Raspberry PiRoberto Falconi
Brief description and useful links:
Developed smart home automation project to measure your house's temperature and send it on your smartphone.
LinkedIn profile: https://www.linkedin.com/in/roberto-falconi
GitHub repository: https://github.com/RobertoFalconi/HouseTemperatureMonitoring
Hackster full description: https://www.hackster.io/Falkons/house-temperature-monitoring-using-aws-iot-and-raspberry-pi-3b6410
SlideShare presentation: https://www.slideshare.net/RobertoFalconi4/house-temperature-monitoring-using-aws-iot-and-raspberry-pi
YouTube video: https://www.youtube.com/watch?v=gQxOSbcN79s
Bi Architecture And Conceptual FrameworkSlava Kokaev
This document discusses business intelligence architecture and concepts. It covers topics like analysis services, SQL Server, data mining, integration services, and enterprise BI strategy and vision. It provides overviews of Microsoft's BI platform, conceptual frameworks, dimensional modeling, ETL processes, and data visualization systems. The goal is to improve organizational processes by providing critical business information to employees.
Digital Transformation at the University of EdinburghMark Ritchie
This slide set provides an overview of the Digital Transformation portfolio of projects being led by Information Services at the University of Edinburgh. The slide set provides the current position on 30th April 2017. We'll update the slideset as work progresses.
Digital Transformation is about more than just technology. Our goal is to adopt new "digital first" ways of working which leverage technology, further our mission and provide a world class experience for our students and staff. Digital Transformation is a major, multi-year portfolio of programmes and projects. The current programmes are:
- Enterprise APIs - to develop a flexible and secure API framework to support development and deployment of user focused solutions more quickly and at lower cost
- Enterprise Data Warehouse - to provide a standards based, flexible and integrated platform for business intelligence and management information
- User Experience Services - to improve the digital experience by focusing on user-centred design through consistent standards, user experience services and training
- User Centred Portal and Notifications - to develop a new user-centred interface for the University Enterprise Portal (MyEd)
- Engagement - to drive engagement with Digital Transformation across the University and provide a governance framework for projects that are not part of other programmes
A further tranche of programmes and projects will come on stream later in 2017 with a the portfolio of work to be completed by Summer 2020.
Your Roadmap for An Enterprise Graph StrategyNeo4j
This document provides a roadmap for developing an enterprise graph strategy with the following key steps:
1. Design and build a proof-of-concept graph using a small local dataset to demonstrate graph capabilities.
2. Present use cases and example queries to business stakeholders to validate the graph model and gather feedback.
3. Design the production graph schema and build APIs/services to integrate data from multiple sources.
4. Deploy the graph in the cloud and develop applications and reports to mobilize enterprise data using the graph.
As the author of “Big Data in Healthcare Hype and Hope,” Dr. Feldman has interviewed over 180 emerging tech and healthcare companies, always asking, “How can your new approach help patients?” Her research shows that data, as an enabling tool, has the power to give us critical new insights into not only what causes disease, but what comprises normal. Despite this promise, few patients have reaped the benefits of personalized medicine. A panel of leading big data innovators will discuss the evolving health data ecosystem and how big data is being leveraged for research, discovery, clinical trials, genomics, and cancer care. Case studies and real-life examples of what’s working, what’s not working, and how we can help speed up progress to get patients the right care at the right time will be explored and debated.
• Bonnie Feldman, DDS, MBA - Chief Growth Officer, @DrBonnie360
• Colin Hill - CEO, GNS Healthcare
• Jonathan Hirsch - Founder & President, Syapse
• Andrew Kasarskis, PhD - Co-Director, Icahn Institute for Genomics & Multiscale Biology; Associate Professor, Genetics & Genomic Studies, Icaahn School of Medicine at Mt. Sinai
• William King - CEO, Zephyr Health
New York eHealth Collaborative Digital Health Conference
November 18, 2014
The document discusses how graph databases are well-suited for telecommunications networks due to their ability to model complex relationships. It provides examples of large telecom companies using Neo4j to power applications involving network topology analysis, digital twins, and other use cases. Key benefits highlighted include stronger support for dynamic connectivity modeling, improved path calculation, and more flexible approaches compared to relational databases. The presentation outlines Neo4j's adoption among top telecom firms and leadership in the graph database market.
This document provides case studies on how several companies leverage big data, including Google, GE, Cornerstone, and Microsoft. The Google case study describes how Google processes billions of search queries daily and uses this data to continuously improve its search algorithms. The GE case study outlines how GE collects vast amounts of sensor data from power turbines, jet engines, and other industrial equipment to optimize operations and efficiency. The Cornerstone case study examines how Cornerstone uses employee data to help clients predict retention and performance. Finally, the Microsoft case study discusses how Microsoft has positioned itself as a major player in big data and offers data hosting and analytics services.
Large amounts of heterogeneous medical data have become available in various healthcare organizations (payers, providers, pharmaceuticals). Those data could be an enabling resource for deriving insights for improving care delivery and reducing waste. The enormity and complexity of these datasets present great challenges in analyses and subsequent applications to a practical clinical environment. More details are available here http://dmkd.cs.wayne.edu/TUTORIAL/Healthcare/
CRM for Insurance Companies – Features You Must Never Compromise OnDamco Solutions
CRM for Insurance Companies is a sophisticated digital tool that complements business functionalities. The ideal CRM has multiple features that make the work easy. Check the key features here. https://www.damcogroup.com/blogs/best-crm-for-insurance-companies-features-and-advantages-to-check-for/
White paper making an-operational_data_store_(ods)_the_center_of_your_data_...Eric Javier Espino Man
The document discusses implementing an operational data store (ODS) to centralize data from multiple source systems. An ODS integrates disparate data for reporting and analytics while insulating operational systems. The document recommends selling an ODS internally by highlighting benefits like reduced workload for ETL developers and improved access to real-time data for business users. It also provides best practices like using automation tools that simplify ODS creation and maintenance.
Digital Transformation Toolkit from ProductStackRahul Mohan
ProductStack's Digital Transformation Toolkit is a conceptual model that provides:
1) A structure for assessing an organization’s digital strategy and digital maturity (Digital Transformation Framework)
2) An indicator or an organization's capabilities to leverage technology to transform their business. (Digital Maturity Model)
3) A high-level approach for an organization to create a roadmap that aligns digital initiatives with its long term vision and business goals. (Digital Strategy)
ProductStack’s Digital Strategy Workshop is a short (2-3 days), collaborative engagement where ProductStack consultants run facilitated workshops with your team members to help you build your digital strategy and an execution roadmap.
Learn more about Digital Transformation at https://productstack.com
The document outlines how to develop an effective digital strategy. It discusses that companies must adopt new technologies or face competitive obsolescence. A digital strategy should address rapid technological change, changing user behaviors, and organizational impacts. It recommends a minimum viable digital strategy that is high-level, user-centered, rapidly developed through collaboration, and focused on outcomes. The process involves discovery, vision, planning, and measurement phases to iteratively improve the strategy.
Business Intelligence - A Management Perspectivevinaya.hs
The document discusses the evolution and importance of business intelligence (BI) systems. It describes how BI systems have evolved from executive information systems to encompass more integrated enterprise systems that pull data from across an organization and external sources to provide a single version of the truth. The document emphasizes that BI is important for making effective decisions, gaining insights from large amounts of data, and empowering all employees with analytics and feedback mechanisms.
Big data is large amounts of unstructured data that require new techniques and tools to analyze. Key drivers of big data growth are increased storage capacity, processing power, and data availability. Big data analytics can uncover hidden patterns to provide competitive advantages and better business decisions. Applications include healthcare, homeland security, finance, manufacturing, and retail. The global big data market is expected to grow significantly, with India's market projected to reach $1 billion by 2015. This growth will increase demand for data scientists and analysts to support big data solutions and technologies like Hadoop and NoSQL databases.
The document discusses a session on future jobs and the changing nature of work. It explores how technological advances will transform careers and the skills needed in the future, which will include creativity, complex problem solving, and the ability to generate new ideas. The session emphasizes educating students to develop skills like curiosity, passion, synthesis, and seeing from multiple perspectives to prepare for jobs that may not even exist yet. Attendees are challenged to rethink learning and teaching models to best support students for an uncertain future.
The document discusses big data and big data analytics in banking. It defines big data as large, complex datasets that are difficult to process and store using traditional databases. Sources of big data include social media, sensors, transportation services, online shopping, and mobile apps. Characteristics of big data include volume, velocity, and variety. Hadoop is presented as an open source framework for analyzing big data using HDFS for storage and MapReduce for processing. The benefits of big data analytics in banking include fraud detection, risk management, customer segmentation, churn analysis, and sentiment analysis to improve customer experience.
Knowledge graph construction for research & medicinePaul Groth
1) Elsevier aims to build knowledge graphs to help address challenges in research and medicine like high drug development costs and medical errors.
2) Knowledge graphs link entities like people, concepts, and events to provide answers by going beyond traditional bibliographic descriptions.
3) Elsevier constructs knowledge graphs using techniques like information extraction from text, integrating data sources, and predictive modeling of large patient datasets to identify statistical correlations.
El proyecto atomico nazi revisionismo holocausto cesar vidal pasajes de la ...Silvia Quezada
Este documento resume la historia del posible programa nuclear nazi durante la Segunda Guerra Mundial. Se desarrollaron al menos tres proyectos de bomba atómica de forma paralela y sin colaboración, dirigidos por Heisenberg, Ohnesorge y Kammler respectivamente. A medida que avanzaba la guerra, las SS asumieron el control de los proyectos secretos, incluido el nuclear. No hay pruebas concluyentes de que Alemania completara una bomba atómica, aunque sus científicos estaban avanzados en el des
Digital transformation provides opportunities for new revenue streams and cost optimization. It differs from traditional IT transformation in several key ways: digital transformation is customer-facing, top-down, and driven by market forces rather than IT upgrade cycles. Over time, the triggers driving digital transformation have evolved from early revenue generation to include new business models and industry disruption. Attributes of successful digital transformation include simplicity, ubiquity, affordability, speed, usability, and empowerment. Digital disruption poses low barriers to entry and forces constant innovation.
This document outlines an agenda and presentation on digital transformation at Geisinger Health. The presentation discusses Geisinger's digital strategy, two case studies on portal consolidation and intelligent automation, assessing digital maturity, and best practices for digital transformation roadmaps. The speakers are Karen Murphy, Chief Innovations Officer at Geisinger, and Paddy Padmanabhan, CEO of Damo Consulting. The presentation provides an overview of Geisinger's multi-year digital transformation efforts to enhance care, grow revenue, and improve efficiency through technology.
AI Security : Machine Learning, Deep Learning and Computer Vision SecurityCihan Özhan
This document discusses technologies related to machine learning, deep learning, computer vision, and artificial intelligence. It covers topics such as ML/DL algorithms, applications, data objects, cloud computing services, distributed systems, security issues, model lifecycles, publishing ML projects, and adversarial attacks against various AI systems including image, speech, NLP, remote sensing, autonomous vehicles, and industrial applications. It also provides links to the founder's online profiles and contact information.
The document discusses business intelligence and analytics programs and careers. It provides information on topics like data mining, dashboards, enterprise resource planning systems, online analytical processing, and multidimensional data models. It also lists relevant course descriptions and curriculum from technical schools and colleges to prepare for careers in fields like business intelligence specialist, business intelligence developer, and business intelligence report developer.
Towards Digital Twin standards following an open source approachFIWARE
Digital Twins are gaining momentum when designing smart solutions in different application domains. However, there is a lack of open standards that warrant interoperability and portability of solutions, avoiding vendor lock-in.
During the presentation, we will review major developments in this area, focused on the adoption of a standard API for accessing Digital Twin Data and Smart Data Models. We will review how a Digital Twin approach enables data integration at different levels: architecting vertical smart solutions, within smart organizations and across organizations. At all levels interfacing with IoT, BigData, AI/ML, Blockchain, or Robotics technologies.
This document provides an introduction to data science. It discusses what data science is, the data life cycle, key domains that benefit from data science and why Python is well-suited for data science. It also summarizes several important Python libraries for data science - Pandas for data analysis, NumPy for scientific computing, Matplotlib and Seaborn for data visualization, and introduces machine learning concepts like supervised and unsupervised learning. Example algorithms like linear regression and K-means clustering are also covered.
Industrial Data Space: Digital Sovereignty for Industry 4.0 and Smart ServicesBoris Otto
The document discusses the Industrial Data Space initiative, which aims to establish a trusted network for industrial data exchange. It outlines the role of data in Industry 4.0 and smart services, and describes the Industrial Data Space architecture, which focuses on digital sovereignty, security, and a decentralized federated approach. The Industrial Data Space is being developed through a research project and chartered association, with upcoming activities including further use cases, positioning in Europe, and joint preparation of usage and operating models.
This document provides case studies on how several companies leverage big data, including Google, GE, Cornerstone, and Microsoft. The Google case study describes how Google processes billions of search queries daily and uses this data to continuously improve its search algorithms. The GE case study outlines how GE collects vast amounts of sensor data from power turbines, jet engines, and other industrial equipment to optimize operations and efficiency. The Cornerstone case study examines how Cornerstone uses employee data to help clients predict retention and performance. Finally, the Microsoft case study discusses how Microsoft has positioned itself as a major player in big data and offers data hosting and analytics services.
Large amounts of heterogeneous medical data have become available in various healthcare organizations (payers, providers, pharmaceuticals). Those data could be an enabling resource for deriving insights for improving care delivery and reducing waste. The enormity and complexity of these datasets present great challenges in analyses and subsequent applications to a practical clinical environment. More details are available here http://dmkd.cs.wayne.edu/TUTORIAL/Healthcare/
CRM for Insurance Companies – Features You Must Never Compromise OnDamco Solutions
CRM for Insurance Companies is a sophisticated digital tool that complements business functionalities. The ideal CRM has multiple features that make the work easy. Check the key features here. https://www.damcogroup.com/blogs/best-crm-for-insurance-companies-features-and-advantages-to-check-for/
White paper making an-operational_data_store_(ods)_the_center_of_your_data_...Eric Javier Espino Man
The document discusses implementing an operational data store (ODS) to centralize data from multiple source systems. An ODS integrates disparate data for reporting and analytics while insulating operational systems. The document recommends selling an ODS internally by highlighting benefits like reduced workload for ETL developers and improved access to real-time data for business users. It also provides best practices like using automation tools that simplify ODS creation and maintenance.
Digital Transformation Toolkit from ProductStackRahul Mohan
ProductStack's Digital Transformation Toolkit is a conceptual model that provides:
1) A structure for assessing an organization’s digital strategy and digital maturity (Digital Transformation Framework)
2) An indicator or an organization's capabilities to leverage technology to transform their business. (Digital Maturity Model)
3) A high-level approach for an organization to create a roadmap that aligns digital initiatives with its long term vision and business goals. (Digital Strategy)
ProductStack’s Digital Strategy Workshop is a short (2-3 days), collaborative engagement where ProductStack consultants run facilitated workshops with your team members to help you build your digital strategy and an execution roadmap.
Learn more about Digital Transformation at https://productstack.com
The document outlines how to develop an effective digital strategy. It discusses that companies must adopt new technologies or face competitive obsolescence. A digital strategy should address rapid technological change, changing user behaviors, and organizational impacts. It recommends a minimum viable digital strategy that is high-level, user-centered, rapidly developed through collaboration, and focused on outcomes. The process involves discovery, vision, planning, and measurement phases to iteratively improve the strategy.
Business Intelligence - A Management Perspectivevinaya.hs
The document discusses the evolution and importance of business intelligence (BI) systems. It describes how BI systems have evolved from executive information systems to encompass more integrated enterprise systems that pull data from across an organization and external sources to provide a single version of the truth. The document emphasizes that BI is important for making effective decisions, gaining insights from large amounts of data, and empowering all employees with analytics and feedback mechanisms.
Big data is large amounts of unstructured data that require new techniques and tools to analyze. Key drivers of big data growth are increased storage capacity, processing power, and data availability. Big data analytics can uncover hidden patterns to provide competitive advantages and better business decisions. Applications include healthcare, homeland security, finance, manufacturing, and retail. The global big data market is expected to grow significantly, with India's market projected to reach $1 billion by 2015. This growth will increase demand for data scientists and analysts to support big data solutions and technologies like Hadoop and NoSQL databases.
The document discusses a session on future jobs and the changing nature of work. It explores how technological advances will transform careers and the skills needed in the future, which will include creativity, complex problem solving, and the ability to generate new ideas. The session emphasizes educating students to develop skills like curiosity, passion, synthesis, and seeing from multiple perspectives to prepare for jobs that may not even exist yet. Attendees are challenged to rethink learning and teaching models to best support students for an uncertain future.
The document discusses big data and big data analytics in banking. It defines big data as large, complex datasets that are difficult to process and store using traditional databases. Sources of big data include social media, sensors, transportation services, online shopping, and mobile apps. Characteristics of big data include volume, velocity, and variety. Hadoop is presented as an open source framework for analyzing big data using HDFS for storage and MapReduce for processing. The benefits of big data analytics in banking include fraud detection, risk management, customer segmentation, churn analysis, and sentiment analysis to improve customer experience.
Knowledge graph construction for research & medicinePaul Groth
1) Elsevier aims to build knowledge graphs to help address challenges in research and medicine like high drug development costs and medical errors.
2) Knowledge graphs link entities like people, concepts, and events to provide answers by going beyond traditional bibliographic descriptions.
3) Elsevier constructs knowledge graphs using techniques like information extraction from text, integrating data sources, and predictive modeling of large patient datasets to identify statistical correlations.
El proyecto atomico nazi revisionismo holocausto cesar vidal pasajes de la ...Silvia Quezada
Este documento resume la historia del posible programa nuclear nazi durante la Segunda Guerra Mundial. Se desarrollaron al menos tres proyectos de bomba atómica de forma paralela y sin colaboración, dirigidos por Heisenberg, Ohnesorge y Kammler respectivamente. A medida que avanzaba la guerra, las SS asumieron el control de los proyectos secretos, incluido el nuclear. No hay pruebas concluyentes de que Alemania completara una bomba atómica, aunque sus científicos estaban avanzados en el des
Digital transformation provides opportunities for new revenue streams and cost optimization. It differs from traditional IT transformation in several key ways: digital transformation is customer-facing, top-down, and driven by market forces rather than IT upgrade cycles. Over time, the triggers driving digital transformation have evolved from early revenue generation to include new business models and industry disruption. Attributes of successful digital transformation include simplicity, ubiquity, affordability, speed, usability, and empowerment. Digital disruption poses low barriers to entry and forces constant innovation.
This document outlines an agenda and presentation on digital transformation at Geisinger Health. The presentation discusses Geisinger's digital strategy, two case studies on portal consolidation and intelligent automation, assessing digital maturity, and best practices for digital transformation roadmaps. The speakers are Karen Murphy, Chief Innovations Officer at Geisinger, and Paddy Padmanabhan, CEO of Damo Consulting. The presentation provides an overview of Geisinger's multi-year digital transformation efforts to enhance care, grow revenue, and improve efficiency through technology.
AI Security : Machine Learning, Deep Learning and Computer Vision SecurityCihan Özhan
This document discusses technologies related to machine learning, deep learning, computer vision, and artificial intelligence. It covers topics such as ML/DL algorithms, applications, data objects, cloud computing services, distributed systems, security issues, model lifecycles, publishing ML projects, and adversarial attacks against various AI systems including image, speech, NLP, remote sensing, autonomous vehicles, and industrial applications. It also provides links to the founder's online profiles and contact information.
The document discusses business intelligence and analytics programs and careers. It provides information on topics like data mining, dashboards, enterprise resource planning systems, online analytical processing, and multidimensional data models. It also lists relevant course descriptions and curriculum from technical schools and colleges to prepare for careers in fields like business intelligence specialist, business intelligence developer, and business intelligence report developer.
Towards Digital Twin standards following an open source approachFIWARE
Digital Twins are gaining momentum when designing smart solutions in different application domains. However, there is a lack of open standards that warrant interoperability and portability of solutions, avoiding vendor lock-in.
During the presentation, we will review major developments in this area, focused on the adoption of a standard API for accessing Digital Twin Data and Smart Data Models. We will review how a Digital Twin approach enables data integration at different levels: architecting vertical smart solutions, within smart organizations and across organizations. At all levels interfacing with IoT, BigData, AI/ML, Blockchain, or Robotics technologies.
This document provides an introduction to data science. It discusses what data science is, the data life cycle, key domains that benefit from data science and why Python is well-suited for data science. It also summarizes several important Python libraries for data science - Pandas for data analysis, NumPy for scientific computing, Matplotlib and Seaborn for data visualization, and introduces machine learning concepts like supervised and unsupervised learning. Example algorithms like linear regression and K-means clustering are also covered.
Industrial Data Space: Digital Sovereignty for Industry 4.0 and Smart ServicesBoris Otto
The document discusses the Industrial Data Space initiative, which aims to establish a trusted network for industrial data exchange. It outlines the role of data in Industry 4.0 and smart services, and describes the Industrial Data Space architecture, which focuses on digital sovereignty, security, and a decentralized federated approach. The Industrial Data Space is being developed through a research project and chartered association, with upcoming activities including further use cases, positioning in Europe, and joint preparation of usage and operating models.
Logistik in der digitalen Wirtschaft: Daten als strategische RessourceBoris Otto
This presentation (in German) was given at the GS1 Switzerland Big Data in Supply Chain Forum held on March 18th, 2015, in Baden, Switzerland. The presentation introduces digitization and "Industrie 4.0" in logistics and supply chain management using a set of case studies. Furthermore, it points to the increasing importance of data in these cases and argues that data must be treated as a strategic resource in firms. The presentation closes with a brief overview about the Fraunhofer Data Innovation Lab in Dortmund, Germany, and a selection of current projects.
Der Vortrag leitet am Beispiel der Automobilindustrie in die wesentlichen Entwicklungen zur Digitalisierung von Industriebetrieben ein und stellt dabei die besondere Rolle der Daten und eines wirksamen Datenmanagements heraus. Abschließend gibt der Vortrag Empfehlungen zum Management der Digitalen Transformation.
Digitalisierung: Datenzentrierte GeschäftsinnovationBoris Otto
Der Beitrag greift den übergreifenden Trend der Digitalisierung auf und untersucht seine Implikation auf die Innovation von Geschäftsmodellen. Aktuelle Beispiele illustrieren Erfolgsfaktoren und zeigen Handlungsempfehlungen auf.
Daten sind die strategische Ressource im digitalen Zeitalter. Der Industrial Data Space zielt darauf ab, den sicheren Austausch und die einfache Kombination von Daten für Unternehmen zu ermöglichen. Dadurch lassen sich smarte Dienstleistungen einfacher verwirklichen. Fraunhofer erarbeitet in einem vom Bundesministerium für Bildung und Forschung geförderten Projekt die Basis dazu und entwickelt ein Referenzarchitekturmodell für den Industrial Data Space, das in ausgewählten Use Cases pilotiert wird.
Digital Business Engineering am Fraunhofer ISSTBoris Otto
This presentation (in German) gives an overview about how Fraunhofer ISST supports digital transformation projects in various industries. It motivates Digital Business Engineering as a methodological framework and show-cases typical applications. The presentation was given at the Fraunhofer ISST 25th anniversary event at Zeche Zollern in Dortmund.
Industrie 4.0 in der Logistik: Stand der Umsetzung und AusblickBoris Otto
Diese Präsentation beschreibt aktuelle Fallstudien zur Industrie 4.0 in der Logistik, identifiziert Entwurfskriterien für Industrie-4.0-Anwendungsszenarien und schließt mit einem Ausblick auf die Änderungen, die Industrie 4.0 für Unternehmen generell und das Logistik-Management im Speziellen mit sich bringt. Die Präsentation wurde gehalten anlässlich der Jahresabschlussveranstaltung der Audi-Logistik am Standort Ingolstadt.
Industrial Data Space: Referenzarchitektur für Data Supply ChainsBoris Otto
Dieser Vortrag stellt den Industrial Data Space als Referenz-Architektur für Data Supply Chains vor. Data Supply Chains sind vernetzte, unternehmensübergreifende Datenflüsse. Data Supply Chains sind Voraussetzung um hybride Leistungsangebote (Smart Services) einerseits und digitalisierte Leistungserstellung (Industrie 4.0) andererseits zu verbinden. Durch die effektive und effiziente Bewirtschaftung von Data Supply Chains erhöhen Unternehmen ihre Wettbewerbsfähigkeit. Der Industrial Data Space liefert hierzu die Blaupause, als Referenzarchitektur für die Datenökonomie.
Industrial Data Space: Digitale Souveränität über DatenBoris Otto
Der Vortrag führt in Grundbegriffe der Datenökonomie ein und macht einen Vorschlag zur Definition des Begriffs der digitalen Souveränität. Zudem arbeitet der Vortrag heraus, welchen wichtigen Beitrag der Industrial Data Space zur Wahrung der digitalen Souveränität leistet.
Data Sovereignty - Call for an International EffortBoris Otto
This presentation will be given at the Digitisting Manufacturing in the G20 Conference on March 16, 2017, in Berlin, in the context of the workshop "Data Sovereignty in Global Value Networks".
The Industrial Data Space is a strategic initiative driven by industry and supported by the German Federal Government. It aims at supporting the secure exchange and easy combination of data within ecosystems.
The presentation (in German) gives an overview about current digitization trends in logistics and their implications on the design of modern logistics systems. The presentation was given at the DB Schenker Science Day on September 8, 2014, in Frankfurt, Germany.
The Industrial Data Space aims at establishing a virtual data space in which partners in business ecosystems can securely exchange and easily link their data assets. The presentation puts the Industrial Data Space in the context of recent developments in the area of Smart Service Welt and Industrie 4.0 and sketches a reference architecture model and functional software components. Furthermore, the presentation introduces the Industrial Data Space Association which institutionalizes the user requirements and drives standardization. The presentation was given at the Industry 4.0 session at MACH 2016 on April 14, 2016, in Birmingham, UK.
Industrial Data Space: Referenzarchitekturmodell für die DigitalisierungBoris Otto
Diese Präsentation auf der VDI Industrie 4.0 Tagung am 25.1.2017 in Düsseldorf gibt ein Update der Entwicklungen des Industrial Data Space. Schwerpunkte sind Datensouveränität, der Industrial Data Space als Bindeglied zwischen IoT-Cloud-Plattformen sowie der Referenz-Use-Case Logistik.
This presentation was held at the 2nd Internet of Manufacturing Conference on February 7, 2017, in Munich, Germany. It addresses the need of a new kind of data management to cope with the requirements digital scenarios pose on the industrial enterprise. Motivated by examples, the talk outlines design principles for smart data management and concludes with two leading examples, namely the Industrial Data Space initiative and the Corporate Data League.
A Taxonomy of the Data Resource in the Networked IndustryBoris Otto
This presentation reports on the design of a taxonomy of the data resource in the networked industry. It was held on the 7th International Scientific Symposium on Logistics on June 6, 2014, in Cologne, Germany. The presentation motivates the topic, analyzes four networking industry cases and discusses a first version of the taxonomy. The presentation argues that for companies aiming at designing a future-proof data architecture leveraging the potentials of the industrial internet, collaborative forms of organizations etc. transparency about data sources, data ownership, criticality, compliance of standards of data, data quality are key for success. In addition, the presentation introduces a first sketch of a method supporting businesses in applying the taxonomy.
Enabling the Industry 4.0 vision: Hype? Real Opportunity!Boris Otto
These are the slides I used at my key note speech at the NASSCOM Engineering Summit on October 7, 2015, in Pune, India. The presentation sets Industry 4.0 in context of smart services and points to the key role data plays.
Lernen und Lehren sind nach meinem Verständnis unmittelbar miteinander verknüpft. Diese frühe persönliche Prägung motiviert mich, Lernprozesse als multipotente Entwicklungsprozesse für alle Beteiligten zu gestalten. Als Professorin an einer technisch orientierten Fachhochschule fühle ich mich der anwendungsnahen Forschung und Entwicklung verpflichtet und habe ein genuines Interesse, Studierende für meine Forschungsfragen zu sensibilisieren und für die Mitarbeit an Neuentwicklungen zu begeistern.
Wie die Swiss Alliance for Data-Intensive Services datenbasierte Mehrwerte sc...Thilo Stadelmann
Die Swiss Alliance for Data-Intensive Services (Data+Service) schafft Mehrwert durch Innovation. Innovation entsteht, wenn sich die richtigen Partner treffen. Wir schaffen inspirierende Begegnungsflächen in Konferenzen, Workshops und Expert Groups. Wir helfen bei der Konkretisierung von Ideen in Projektskizzen in Innovation Boot Camps. Und wir setzen Projektskizzen in innovativen Mehrwert um im Rahmen von Kooperationsprojekten.
Jedes Online-Projektresultat muss einfach und verständlich sein! Dieser Anspruch wird jedem Projektverantwortlichen übertragen und entscheidet tatsächlich über Erfolg und Misserfolg. Die professionell präsentierte "Hochglanz-Lösung" Ihrer Agentur ist natürlich überzeugend wie eh und je; trotzdem plagt Sie ein ungutes Gefühl und Sie fragen sich: "Wird unsere neue Lösung funktionieren?"
Thomas Link und Andri Stoffel zeigen, wie Sie diese "Bauchschmerzen" mit selbst durchgeführten Usability Tests aus der Welt schaffen können. Dabei lernen Sie, wie Sie dafür am besten vorgehen und welche Tools und Instrumente am besten geeignet sind.
• "Yes we can" – Warum ausgerechnet Sie die Tests durchführen
• Organisiertes und Ad-hoc-Testing
• Produkt-Roadmap entlang Usability-Meilensteinen
• So geht’s und das sollten Sie beachten - eine Checkliste
• Changing your Organisation
Ähnlich wie Konsortialforschung: Gestaltungsorientierte Wirtschaftsinformatikforschung in Kooperation mit der Praxis (20)
This document discusses the evolution of data spaces from closed ecosystems to open ecosystems to federations of ecosystems. It defines key concepts of data spaces including their technological, business, and legal aspects. The document outlines an example data space in the mobility domain and describes the fundamentals of data spaces including roles, interactions, and activities. It analyzes how characteristics such as interoperability, sovereignty, and trust/security change as data spaces evolve from closed to open to federations. Finally, it poses questions about who will take on the federator role to coordinate ecosystems and what business models and regulatory implications this role may have.
Shared Digital Twins: Collaboration in EcosystemsBoris Otto
This presentation introduces the concept of shared digital Twins from a cusiness perspective and outlines recent technological developments for shared digital twin management.
Deutschland auf dem Weg in die DatenökonomieBoris Otto
Der Vortrag greift aktuelle Diskussionsstränge zwischen Wirtschaft, Wissenschaft und Politik auf und thematisiert u.a. die betriebswirtschaftliche, volkswirtschaftliche, informationstechnische und ethische Dimension der Datenökonomie.
International Data Spaces: Data Sovereignty for Business Model InnovationBoris Otto
This presentation given at the European Big Data Value Forum on November 13, 2018, in Vienna introduces International Data Spaces (IDS) as a reference architecture and implementation for data sovereignty. The IDS archiecture rests on usage control technologies and trusted computing environments and, thus, forms a strategic enabler for a fair data economy which respects the interests of the data owners.
Business mit Daten? Deutschland auf dem Weg in die smarte DatenwirtschaftBoris Otto
This presentation (in German) given at the "Tage der digitalen Technologien" on May 15, 2019, in Berlin addresses data ecosystems as an innovative institutional format for creating value out of shared data. Furthermore, the talk points to selected challenges in setting up data ecosystems.
International Data Spaces: Data Sovereignty and Interoperability for Business...Boris Otto
This presentation was held in a workshop session on IoT Business Models and Data Interoperability at the Max Planck Institute for Innovation and Competition in Munich on 8 October 2018. The presenation introduces the concept of business ecosystems and the role of data within the latter, then outlines the state of the art in terms of interoperability and sovereignty and finally sketches the IDS contribution.
This presentation was part of the IDS Webinar on Data Governance. It gives a brief overview of the history on Data Governance, describes how governing data has to be further developed in the era of business and data ecosystems, and outlines the contribution of the International Data Spaces Association on the topic.
Data Resource Management: Good Practices to Make the Most out of a Hidden Tre...Boris Otto
Management of the data resource in the industrial enterprise becomes a strategic capability in the digital age. The talk motivates data resource management, presents proven practices and outlines principles of modern data management approaches.
Smart Data Engineering: Erfolgsfaktor für die digitale TransformationBoris Otto
Diese Präsentation wurde auf dem Strategieforum IoT auf Schloss Hohenkammer am 30.5.2018 vorgetragen und führt in die Herausforderungen im Datenmanagement im Internet der Dinge ein. Zudem werden Prinzipien des Smart Data Engineering erläutert.
IDS: Update on Reference Architecture and Ecosystem DesignBoris Otto
This presentation motivates the Industrial Data Space and gives an update on the IDS Reference Architecture Model as well as the related ecosystem. It sets data in the context of business model innovation and points out how the IDS Reference Architecture relates to alternative data architecture styles such as data lakes and blockchain technology, for example. The presentation was given at the IDSA Summit on March 22, 2018.
Datensouveränität in Produktions- und LogistiknetzwerkenBoris Otto
Dieser Vortrag motiviert Datensouveränität in Produktions- und Logistiknetzwerken. Datensouveränität ist die Fähigkeit zur Selbstbestimmung über das Wirtschaftsgut Daten - auch beim Austauschen der Daten in Unternehmensnetzwerken. Der Vortrag führt in die Architektur des Industrial Data Space ein, der einen virtuellen Datenraum für den souveränen Datenaustausch bildet. Der Vortrag schließt mit Anwendungsbeispielen und einer Diskussion des Beitrags für die Wissenschaft und die Praxis.
2. 2Prof. Dr. B. Otto, Prof. Dr. H. Österle | Osnabrück
4. März 2015
Workshop-Agenda
Konsortialforschung im Überblick
Beispiele zur Konsortialforschung
Weiterentwicklungspotentiale
Anhang: Quellenverzeichnis
3. 3Prof. Dr. B. Otto, Prof. Dr. H. Österle | Osnabrück
4. März 2015
Motivation für Konsortialforschung
Aho-Bericht der Europäischen Kommission
Die Verwertung von Forschungsergebnissen muss im Ecosystem erfolgen und
die Zusammenarbeit zwischen den verschiedenen Akteuren im
Forschungsprozess ist zu forcieren1
OECD Science, Technology, and Industry Outlook 2008
Die Intensität der Zusammenarbeit zwischen Wissenschaft und Forschung hat
einen direkten Einfluss auf die Effektivität und Effizienz von
Innovationssystemen2
1) [European Commission, 2008]
2) [OECD, 2008]
4. 4Prof. Dr. B. Otto, Prof. Dr. H. Österle | Osnabrück
4. März 2015
Merkmale der Konsortialforschung1
Forschung und Praxis definieren gemeinsam die Forschungsziele
Vertreter der Partnerunternehmen arbeiten im Projekt mit und gewähren
Zugang zum Unternehmenswissen
Forschungsergebnisse sind wissenschaftliche Artefakte mit praktischem
Nutzen für die Partnerunternehmen
Artefakte werden im Unternehmenskontext getestet und evaluiert
Partnerunternehmen stellen Finanzmittel und Personal bereit
Forschungsergebnisse werden veröffentlicht
1) [Österle & Otto, 2010]
5. 5Prof. Dr. B. Otto, Prof. Dr. H. Österle | Osnabrück
4. März 2015
Methodenüberblick der Konsortialforschung1
Domäne
Design
Evaluation
Diffusion
Wissenschaftliche
Publikation
Publikation in der
Praxis
Lehrmaterial
Review-
Workshops
Funktions-
tests
Experimente Simulationen Piloten
Verwertungsplan
GUI
Design
Software
Engineering
Method
Engineering
Referenz-
modellierung
. . .
Stand der
Forschung
• Implemen-
tierungen
• Modelle
• Methoden
• Theorien
• Konstrukte
Analyse
Forschungsskizze:
Bedarfe,
Lücke, Ziel
Wissensstand
(Modelle u.
Methoden)
Wissensstand
(Implemen-
tierungen)
Konsortial-
vereinbarung
Wissensstand
(Theorien u.
Konstrukte)
Forschungs-plan
Stand der Praxis
• Geschäftsmodelle
• Aufbau- und
Ablauforganisation
• Informations-
systeme
• IuK-Technologie
1) [Österle & Otto, 2010]
6. 6Prof. Dr. B. Otto, Prof. Dr. H. Österle | Osnabrück
4. März 2015
Wissensgenerierung1,2,3 in der Konsortialforschung gemäß dem
Modell von Nonaka & Takeuchi4
Sozialisierung Externalisierung
Aktionsforschung
Kreativitätstechniken (z. B.
Morphologische Analyse)
„Participatory Design“
…
Experteninterviews
Fallstudien
Fokusgruppeninterviews
„Reverse Engineering“
Umfragen
…
Kombination Internalisierung
Fallstudien
Inhaltsanalysen
Marktstudien
Prototypenbau
Referenzmodellierung
…
„In-House“-Schulungen
Gemeinsam Projektteams
…
1) [Österle & Otto, 2010]
2) [Otto & Österle, 2011]
3) [Österle & Otto, 2012]
4) [Nonaka & Takeuchi, 1995]
7. 7Prof. Dr. B. Otto, Prof. Dr. H. Österle | Osnabrück
4. März 2015
Workshop-Agenda
Konsortialforschung im Überblick
Beispiele zur Konsortialforschung
Kompetenzzentrum Corporate Data Quality (CC CDQ)
EFQM Reifegradmodell für Corporate Data Quality Management
Corporate Data League
Weiterentwicklungspotentiale
Anhang: Quellenverzeichnis
8. 8Prof. Dr. B. Otto, Prof. Dr. H. Österle | Osnabrück
4. März 2015
Beispiele zur Konsortialforschung
Kompetenzzentrum Corporate Data Quality (CC CDQ)
9. 9Prof. Dr. B. Otto, Prof. Dr. H. Österle | Osnabrück
4. März 2015
Eckdaten des CC CDQ1
Laufzeit
Seit November 2006
Konsortialworkshops
5 zweitägige Workshops p.a.
43 insgesamt seit 2006
Beispiel
Partnerunternehmen
29 (seit 2006, aktuell 18)
Wissenschaftliche Laufbahnen
1 Habilitation
15 Promotionen
1) [Otto & Österle, 2010]
10. 10Prof. Dr. B. Otto, Prof. Dr. H. Österle | Osnabrück
4. März 2015
CC CDQ: Referenzmodell des
Stammdatenqualitätsmanagement1,2
1) [Otto, 2011]
2) [Otto et al., 2011]
11. 11Prof. Dr. B. Otto, Prof. Dr. H. Österle | Osnabrück
4. März 2015
Aktuelles Konsortium des CC CDQ
ABB LTD. ASTRAZENECA PLC BAYER AG BEIERSDORF AG DB NETZ AG
DRÄGERWERK AG & CO.
KGAA
ERICSSON AB FESTO AG & CO. KG MERCK KGAA NESTLÉ SA
NOVARTIS PHARMA AG OSRAM GMBH ROBERT BOSCH GMBH SAP AG
SCHWEIZERISCHE
BUNDESBAHNEN SBB
SCHAEFFLER AG SWISSCOM IT SERVICES AG ZF FRIEDRICHSHAFEN AG
12. 12Prof. Dr. B. Otto, Prof. Dr. H. Österle | Osnabrück
4. März 2015
Beispiele zur Konsortialforschung
CDQM Reifegradmodell
13. 13Prof. Dr. B. Otto, Prof. Dr. H. Österle | Osnabrück
4. März 2015
CDQM Reifegradmodell: Motivation
2011 2012 2013 2014
CDQM
Reifegrad
Zeit
Messbarkeit der Leistung des
Corporate Data Quality Management
Vergleichswerte/Benchmarking
Maßnahmenplan für die
Weiterentwicklung
Ziele
„Best-in-Class“
Durchschnitt
„Best Practice
Gap“
14. 14Prof. Dr. B. Otto, Prof. Dr. H. Österle | Osnabrück
4. März 2015
CDQM-Reifegradmodell: Forschungsprozess1,2
2006 2007 2008 2009 2010 2011
Bedarf in Konsortium-
Workshop festgestellt
RM ausgewertet in
AR Projekt
Anforderungen in Konsortium-
Workshop spezifiziert
RM ausgewertet
in AR Projekt
RF bewertet
durch EFQM
Kooperationsvertrag
mit EFQM
RM in Konsortium-
Workshop bewertet
Fertigstellung web-basiertes
Bewertungs-Werkzeug
RM für die
Öffentlichkeit verfügbar
DE Iteration 1
DE Iteration 2
DE Iteration 3
DD1
RM ausgewertet
in AR Projekten
RM in Konsortium-
Workshop bewertet
DD2
DD3
DD4
Legende: RM – Reifegrad Model; DE – Design/Evaluate; DD – Design Decision.
RM durch Studie
bewertet
1) [Ofner et al., 2013]
2) [Ofner et al., 2009]
3) [Hüner et al., 2009]
15. 15Prof. Dr. B. Otto, Prof. Dr. H. Österle | Osnabrück
4. März 2015
CDQM-Reifegradmodell: Diffusion
Download: http://benchmarking.iwi.unisg.ch/Framework_for_CDQM.pdf
Unterzeichnet von:
16. 16Prof. Dr. B. Otto, Prof. Dr. H. Österle | Osnabrück
4. März 2015
CDQM Reifegradmodell: Software-Unterstützung
17. 17Prof. Dr. B. Otto, Prof. Dr. H. Österle | Osnabrück
4. März 2015
CDQM-Reifegradmodell: Konsortialforschungsmethode
Domäne
Design
Evaluation
Diffusion
Wissenschaftliche
Publikation
Publikation in der
Praxis
Lehrmaterial
Zielgruppen
Interviews
Projekte zur
Aktionsforschung
Umfrage
Web-basiertes
Bewertungs-
Werkzeug
Reife-
grad-
modell
Entwurf
Fallstudien
geordnete
Referenz-
modellierung
Stand der Forschung
• Theorie der
Reifegrad-
modellierung
• Reifegradmodell
Entwurf
• Fokus auf
Unternehemens-
fähigkeiten
Analyse
Bedürfnisse aller
Stakeholder-
Gruppen
Stand des
DQM und der
Reifegradmodelle
CC CDQ und
EFQM
Vereinbarung
Problemdefinition
durch das CC CDQ
Stand der Praxis
• Reifegradmodelle
• DQM Praktiken und
Indikatoren
Konferenzen &
Seminare
18. 18Prof. Dr. B. Otto, Prof. Dr. H. Österle | Osnabrück
4. März 2015
Beispiele zur Konsortialforschung
Corporate Data League
19. 19Prof. Dr. B. Otto, Prof. Dr. H. Österle | Osnabrück
4. März 2015
Corporate Data League: Problemstellung
Anzahl der Attribute Erhaltungsaufwand
Attribute der Geschäftspartner
Offizieller Name, Rechtsform
Adressen (offizielle, Rechnungs-, Versand- , Bestelladresse)
Hierarchien (rechtlich, organisatorisch, geographisch)
Steuernummern (z.B. Mehrwertsteuer)
Bankinformation (SWIFT-BIC, IBAN)
Zertifikate (z.B. SAS70, ISO 9000)
…
Geschäftliche Kontakte
Daten Zulieferer Klassifikation
Beschaffungs- und Versandkonditionen
Zahlungsbedingungen und Methoden
Partnerrollen
…
5%
30%
70%
95%
Öffentliche Daten , Potential für
unternehmensübergreifende Zusammenarbeit
…
Kreditlimit
Währungsinformationen
Mahnbedingungen
Buchhaltungsdaten
Versicherungsdaten
Preiskalkulation Daten
…
20. 20Prof. Dr. B. Otto, Prof. Dr. H. Österle | Osnabrück
4. März 2015
Corporate Data League: Lösungsansätze
Bayer, Merck, und Novartis haben den gleichen Zulieferer: Nestlé
Jedes der Unternehmen verwaltet Stammdaten von Nestlé
Dreifach redundanter Aufwand bei der Datenverwaltung und Kosten für die Bezugsdaten
Ist-Situation: Redundantes Daten-Management
Bayer, Merck und Novartis teilen sich einen Teil der Daten über Nestlé
Die Unternehmen verständigen sich auf unternehmensübergreifende Prozesse zum
Daten-Management
Reduzierter Aufwand für die Datenverwaltung bei geringeren Kosten
Erhöhte Datenqualität: Mehr Möglichkeiten um potentielle Datenfehler zu identifizieren
Lösungsansatz: Unternehmensübergreifendes Daten-Management
Verwaltung von Daten des Geschäftspartners Nestlé, z. B. Name, Rechtsform und Adresse
21. 21Prof. Dr. B. Otto, Prof. Dr. H. Österle | Osnabrück
4. März 2015
Corporate Date League: Forschungsfrage und Konsortium
Forschungsfrage
Wie ist eine Architektur für unternehmensübergreifende Zusammenarbeit im
Datenmanagement zu entwerfen?
Konsortium an Partnerunternehmen
Bayer
Nestlé
Novartis
Syngenta
Förderung
Kommission für Technologie und Innovation
22. 22Prof. Dr. B. Otto, Prof. Dr. H. Österle | Osnabrück
4. März 2015
Corporate Data League: Stand der Arbeiten
23. 23Prof. Dr. B. Otto, Prof. Dr. H. Österle | Osnabrück
4. März 2015
Domäne
Design
Evaluation
Diffusion
Wissenschaftliche
Publikation
Publikation in der
Praxis
Markteinführung
CTI
Workshops
Testen der
Software
Fokus Gruppe,
Experten
Interviews
Pilot
Anwendung
Prototyp
Vorstellung
Stand der Forschung
• Referenzmodelle
• Semantische
Interoperationen
• Organisations-
Theorie
• Methoden
Analyse
Schwachpunkt:
geringe Daten-
Qualität
SotA:
Modelle &
Methoden
Bereitsteller der
Daten, DQ
Validierungs-
Werkzeuge, BRMS
CC CDQ
Konsortium
SotA:
Theorien &
Konstrukte
CTI Projekt
Konsortium
Stand der Praxis
• Business Models
• Data governance
• DM Prozesse
• BRM and BRMS
• Cloud Anwendungen
• TOGAF
• Zachman Framework
Überlappende
Analyse
Software
Engineering
Referenz-
modellierung
Geschäfts-
modellierung
Methoden-Design
Fallstudien –
Forschung
Ontologie-
entwurf
Entwicklung von
Prototyp
Corporate Data League: Konsortialforschungsmethodik
24. 24Prof. Dr. B. Otto, Prof. Dr. H. Österle | Osnabrück
4. März 2015
Workshop-Agenda
Konsortialforschung im Überblick
Beispiele zur Konsortialforschung
Weiterentwicklungspotentiale
Anhang: Quellenverzeichnis
25. 25Prof. Dr. B. Otto, Prof. Dr. H. Österle | Osnabrück
4. März 2015
Weiterentwicklung
1Potentiale
Was sind Einsatzfelder der Konsortialforschung? Können Sie sich
einen Einsatz in Ihrem Umfeld vorstellen?
3Methodik
Was methodische Stärken und Schwächen? Was muss verbessert
werden?
2Barrieren
Was sind Limitationen der Konsortialforschung bzw. Barrieren?
Unter welchen Bedingungen funktioniert Konsortialforschung nicht?
26. 26Prof. Dr. B. Otto, Prof. Dr. H. Österle | Osnabrück
4. März 2015
Ihr Ansprechpartner
Univ.-Prof. Dr.-Ing. habil. Boris Otto
Technische Universität Dortmund Fraunhofer IML & Fraunhofer ISST
Audi-Stiftungslehrstuhl Leitung
Supply Net Order Management Data Innovation Lab
LogistikCampus
Joseph-v.-Fraunhofer-Straße 2-4
D-44227 Dortmund
Boris.Otto@tu-dortmund.de Boris.Otto@iml.fraunhofer.de
+49-231/755-5959 +49-231-9743-655
27. 27Prof. Dr. B. Otto, Prof. Dr. H. Österle | Osnabrück
4. März 2015
Workshop-Agenda
Konsortialforschung im Überblick
Beispiele zur Konsortialforschung
Weiterentwicklungspotentiale
Anhang: Quellenverzeichnis
28. 28Prof. Dr. B. Otto, Prof. Dr. H. Österle | Osnabrück
4. März 2015
Quellenverzeichnis (I/II)
[European Commission, 2008]
European Commission (2008) Information Society Research and Innovation: Delivering results with sustained impact (Evaluation of the
effectiveness of Information Society Research in the 6th Framework Programme 2003-2006). European Commission, DG Information Society
and Media.
[Hüner et al., 2009]
Hüner K, Ofner M, Otto B (2009) Towards a Maturity Model for Corporate Data Quality Management. In: Proceedings of the 2009 ACM
Symposium on Applied Computing, Honolulu, HI.
[OECD, 2008]
OECD (2008) OECD Science, Technology and Industry Outlook. Organisation for Economic Co-operation and Development, Paris.
[Ofner et al., 2009]
Ofner MH, Hüner KM, Otto B Dealing with Complexity: A Method to Adapt and Implement a Maturity Model for Corporate Data Quality
Management. In: Proceedings of the 15th Americas Conference on Information Systems (AMCIS 2009), San Francisco, CA.
[Ofner et al., 2013]
Ofner, M, Otto, B, Österle, H (2013) A Maturity Model for Enterprise Data Quality Management. In: Enterprise Modelling and Information
Systems Architectures: An International Electronic Journal 8, Nr. 2, S. 4-24.
[Österle & Otto, 2010]
Österle H, Otto B (2010) Konsortialforschung: Eine Methode für die Zusammenarbeit von Forschung und Praxis in der gestaltungsorientierten
Wirtschaftsinformatikforschung. WIRTSCHAFTSINFORMATIK 52 (5):273-285.
[Otto, 2011]
Otto B (2011) Quality Management of Corporate Data Assets. In: Praeg C-P, Spath D (eds) Quality Management for IT Services: Perspectives
on Business and Process Performance. IGI Global, Hershey, PA, pp 193-209.
[Otto et al., 2011]
Otto B, Kokemüller J, Weisbecker A, Gizanis D (2011) Stammdatenmanagement: Datenqualität für Geschäftsprozesse. HMD - Praxis der
Wirtschaftsinformatik 48 (279):5-16.
29. 29Prof. Dr. B. Otto, Prof. Dr. H. Österle | Osnabrück
4. März 2015
Quellenverzeichnis (II/II)
[Otto & Österle, 2010]
Otto B, Österle H (2010) Relevance Through Consortium Research? A Case Study. In: Proceedings of the 18th European Conference on
Information Systems (ECIS 2010), Pretoria, 2010-06-06.
[Otto & Österle, 2011]
Otto B, Österle H (2011) Toward a Knowledge Creation Perspective on Design Science Research. In: Proceedings of the 17th Americas
Conferece on Information Systems (AMCIS 2011), Detroit, MI, August 6, 2011.
[Otto & Österle, 2012]
Otto, B, Österle, H (2012) Principles for Knowledge Creation in Collaborative Design Science Research. In: Proceedings of the 33rd
International Conference on Information Systems (ICIS 2012), Orlando, FL
[Nonaka & Takeuchi, 1995]
Nonaka I, Takeuchi H (1995) The Knowledge-Creating Company: How Japanese Companies Create the Dynamics of Innovation. Oxford
University Press, Oxford, UK