Technologies for Happiness - and their Impact on Enterprise Application Architectures

Fabio Casati

University of Trento

Keynote: 07 September 2016


FabioCasatiTechnology has profoundly changed all aspects of our personal and professional lives, and is continuing to do so at an ever increasing pace. Most of these changes help us be more efficient, effective and flexible. What is still unclear is whether this change has made us happier, that is, if it has improved our quality of life or whether it has made it more hectic and stressful.Pursuing happiness is considered a goal worthy in its own right, and happiness has a wide range of "side" benefits as well: happier individuals are healthier, more social, more giving, more collaborative, and so on. It is therefore not surprising that research on happiness has intensified in recent years, with the rise of scientific fields such as positive psychology that studies how we can live more fulfilling lives. This talk is about positive technology, that is, technology that can directly contribute to people's happiness. I'll start by presenting what science today considers as important determinants of happiness, both as adults and as we age, and how it can be "measured". We'll then discuss how technology can affect these determinants and what are the potential and the key ingredients of positive technology as a science. Finally, we'll assess the impact positive technologies can have on enterprises, by enabling employees to become more effective at what they do and more capable of living in a constantly and sometimes "disruptively" changing environment.


Speakers's Bio

Fabio Casati is professor of social informatics and senator at the University of Trento. Until 2006, he was technical lead for the research program on business process intelligence in Hewlett-Packard USA, where he contributed to several HP commercial products in the area of web services and business process management. He then moved to academia, where he started a research line on technologies for happiness, delivering results that have a direct impact on people’s life. The research results are available at



The Role of Big Data and Data Science within Digitization at Allianz Group

Andreas Braun

Global Data & Analytics at Allianz SE

Keynote: 08 September 2016


AndreasBraunBy Big Data Analytics we understand new technologies and methods that go beyond how we previously handled data and analytics. One the data side, for instance, extremely large data sets can be stored and processed, even real-time, and at reasonable cost. This is largely applied also to unstructured data, for example internet and clickstreams, bank and credit card transactions, and GPS/ geospatial data. On the analytical side, methods are no longer limited to on hard-coded (business) rules or statistics, but leverage artificial intelligence (AI) and particularly Machine Learning (ML). Big Data platforms recognize recurring patterns and act context-aware to transform the data mentioned before into more meaningful actionable insights. In the aforementioned scenarios ML not only typically delivers better results than statistical approaches or rule-based systems in particular, they can also be implemented dynamically and adaptive; they are intelligent in a way. As a further key element, ML allows predictions based on what has been learnt so far—hence the term predictive analytics. The application of Big Data today is manifold and of growing importance. However, we believe that Big Data's most tangible and immediate impact in the domain of business is in customer and consumer analytics. This area has been summarized as the Digital Consumer Journey Analytics. Such journeys are constructed from people's movement and navigational patterns in both the virtual and physical world. While individual data points are at first not very expressive nor rich of content, and are seen for themselves also anonymous in a way, the picture created by continuous collection of ubiquitous data and their history allows to unveil almost any identity profile [7]. This is typically used for profiling, predictions, and segmentations. For example, web pathways can be used to derive a socio-economic customer profile to predict interest, purchasing intent, or churn. Comprehensive consumer profiles can be cataloged and used for marketing purposes. The creation of such insight became only possible through the use of Big Data technologies and analytics: first of all, because of the sheer amount of data and their history being used; secondly, because online ML allows for the continuous improvement and fine tuning of initial profiles and models so that they increasingly correlate ever better with reality and the real life situation of an actual person—eventually down to the segment of one. The broader context beyond a single individual, on the other side, allows for various marketing relevant predictions: What do people within a category typically buy? What are they interested to buy next? When do they go on holiday, and where to? What do they spend on the location they have travelled to? How to get in touch and address them? etc. The entrepreneurial and economic value of such analytics is beyond doubt and proven to be immense for businesses. Conveniently enough, various different use cases sit on the same data eco system. For example, while fraud analytics saves two-digit millions in fraudulent claims, the same data is used to “white-flag” uncritical claims to pay customers faster and identify unhappy clients. Retention models reduce churn by more than 20%—compared to the statistical models used previously. Meanwhile, the same data are used to improve the conversion rate in direct insurance by almost 25%. We argue here that the ability to improve the customer experience and innovate the customer journey is the most important change on the new data wave. Besides “white-flagging” claims to pay customers faster, web-pages can be arranged accordingly to customer interest in real-time to optimize usability and minimize navigation effort for the customer as customer-relevant information is prioritized. Big Data Analytics is used to make better and more relevant offers to customers, or to refrain offers in the wrong moment. Customers are understood increasingly well so that a customer need can be identified in real-time: rebates can be added to product-bundles for specifically price-sensitive customer profiles. The customer experience is continuously measured and fine-tuned in the background. Customers, in turn, will also use technology to protect themselves from unwanted advertisements and direct marketing. In the future products and services not only will be developed using Big data but also tailored to a customer's specific need. Staying in the relevant set of customers will be of utmost importance. Hence, we believe the notion of marketing will change and broaden and continuously blend into e.g., product service design and improvement. While information, IT and Cyber Security is discussed since decades the new Big Data-driven challenge will be data privacy and ethics. Basically, this means that legacy approached like anonymization and personally identifiable information or PII are not sufficient any more. The task for marketing hence is to make products relevant and trusted in the digital age/ In this paper, we illustrate a few successful Big Data Use Cases in Consumer Analytics and discuss Privacy by Design as an approach to be trusted.


Speakers's Bio

Andreas Braun graduated from TU-Munich in Computer Science and theoretical medicine and pursued an Accenture-funded doctorate focused on software architectures for artificial intelligence. Today, Andreas heads up Global Data & Analytics at Allianz SE. In this role, he is responsible for the Global Data & Analytics Competence Center at group level. This spans big data use cases, governance, data sciences and advanced analytics, and the respective technology and architecture. Previously, Andreas was Global Head, Business Applications and Technology in GfK SE, Germany’s largest market research firm, where he was responsible for all customer-facing business software development and new technologies, including e.g., Big Data, Hadoop etc. Earlier in his career, Andreas was overseeing operations, data analytics, and off and near-shoring at TNS Infratest in Germany and Central Eastern Europe; in the 1990s, Andreas co-founded a company focusing on image processing, which he sold in 2000.



Enterprise Computing in the Context of Networked Business Paradigms

Paul Grefen

Eindhoven University of Technology

Keynote: 09 September 2016


PaulGrefenIn recent years, we have seen the emergence of new business paradigms that highlight the importance of business network thinking. These business paradigms stress the idea that business thinking should not be based primarily on an intra-organizational focus, but rather on the relationships with business organizations, such as collaborators or customers. For example, the service-dominant paradigm is centered at networked co-creation of value for customers through services. The recent outcome economy paradigm revolves around facilitating measurable business results for customers. Combining these paradigms with the concept of agile business leads to dynamic business networks as a first order citizen in business engineering. These developments have a strong impact on the domain of enterprise computing: on the one hand, it requires an outside-in engineering to complement the traditional inside-out approach; on the other hand, it requires a decoupling of strategic resource-based design from tactic value-based design. In this presentation, networked business paradigms are illustrated and their impact on enterprise computing is explored.


Speakers's Bio

Paul Grefen is a full professor in the School of Industrial Engineering at Eindhoven University of Technology since 2003. He chaired the Information Systems subdepartment from 2006 to 2014. Currently, he is the research director of the School. He received his Ph.D. in 1992 from the University of Twente and held assistant and associate professor positions in the Computer Science Department. He was a visiting researcher at Stanford University in 1994. He has been involved in various European research projects as well as various projects within the Netherlands. He is an editor of the International Journal of Cooperative Information Systems. He is an editor and author of the books on the WIDE and CrossWork projects, and has authored books on workflow management, electronic business and service-dominant business engineering. He is a member of the Executive Board of the European Supply Chain Forum. His current research covers architectural design of business information systems, inter-organizational business process management, and service-oriented business design and support. He teaches at the MSc, PDEng and PhD levels at TU/e and at the executive level for TIAS business school.