Ignacio G. Terrizzano

LabBook: Metadata-driven social collaborative data analysis

Open data analysis platforms are being adopted to support collaboration in science and business. Studies suggest that analytic work in an enterprise occurs in a complex ecosystem of people, data, and software working in a coordinated manner. These studies also point to friction between the elements of this ecosystem that reduces user productivity and quality of work. LabBook is an open, social, and collaborative data analysis platform designed explicitly to reduce this friction and accelerate discovery. Its goal is to help users leverage each others knowledge and experience to find the data, tools and collaborators they need to integrate, visualize, and analyze data. The key insight is to collect and use more metadata about all elements of the analytic ecosystem by means of an architecture and user experience that reduce the cost of contributing such metadata. We demonstrate how metadata can be exploited to improve the collaborative user experience and facilitate collaborative data integration and recommendations. We describe a specific use case and discuss several design issues concerning the capture, representation, querying and use of metadata.

Industry Operations Architecture for Business Process Model Collections

The absence of a holistic industry-centric architecture for processes is an important BPM shortfall that impacts model collections. This paper introduces a Componentized Industry Business Architecture as a vehicle to address this gap and to make processes better integrated with other critical dimensions in organizational design. This architecture provides the foundation for a taxonomy of processes and enables process models to be created or potentially rationalized against a comprehensive framework.

Splash: Simulation optimization in complex systems of systems

Decision-makers increasingly need to bring together multiple models across a broad range of disciplines to guide investment and policy decisions around highly complex issues such as population health and safety. We discuss the use of the Smarter Planet Platform for Analysis Simulation of Health (Splash) for cross-disciplinary modeling, simulation, sensitivity analysis, and optimization in the setting of complex systems of systems. Splash is a prototype system that allows combination of existing heterogeneous simulation models and datasets to create composite simulation models of complex systems. Splash, built on a combination of data-integration, workflow management, and simulation technologies, facilitates loose coupling of models via data exchange. We describe the various components of Splash, with an emphasis on the experiment-management component. This latter component uses user-supplied metadata about models and datasets to provide, via an interactive GUI, a unified view over all of the parameters in all of the component models that make up a composite model, a mechanism for selecting the factors to vary, and a means for allowing users to easily specify experimental designs for the selected factors. The experiment manager also provides a mechanism for systematically varying the inputs to the composite models. We show how the experiment manager can be used to implement some simple stochastic-optimization functionality by implementing the Rinott procedure for selecting the best system. We also implement a sensitivity-analysis method based on a fractional-factorial experimental design. We demonstrate this technology via a composite model comprising a financial-rate model and a healthcare payer model.

Systematic Web Data Mining with Business Architecture to Enhance Business Assessment Services

Among the numerous challenges faced by enterprises today, two recent trends have significantly impacted the manner businesses are run and decisions are made. First, many enterprises have adopted Business Architecture concepts to structure, define, plan, measure and optimize their operations. Second, enterprises have leveraged the vast wealth of dynamic and unstructured web information to identify competitive advantages, recognize social media sentiment patterns or anomalies, and assuage potentially damaging client perceptions. These tasks are commonly performed independently of each other, thus missing several synergetic opportunities. By establishing a systematic relationship between these seemingly disjoint trends, enterprises and consulting service companies gain competitive, operational advantages, and recurring benefits. This paper describes a systematic approach whereby results from text mining analysis are integrated with the Business Architecture to empower business users to leverage social media data for increased decision making efficiencies. Applying design science, the proposed approach is explained by providing a conceptual model and a two-phased integration method. It is then buttressed by a sample scenario derived from the banking industry. We discuss the potential operational and competitive gains realized by adopting our approach, and the directions for future work.

Modeling Business Applications for Business Architecture

For several years the mantra has been “closing the Business – IT gap”, enabling business people to focus on strategic direction, while not being unnecessarily burdened by the complexity of the underlying support systems. The discipline of Business Architecture continues to evolve, attempting to bring rigor to the task of matching imprecise strategic needs to rigid IT systems. Increasingly literature acknowledges the need for precise connections between business needs and IT systems, however little usable guidance has been provided. This paper focuses narrowly but deeply at suggesting a precise way in which business applications and related elements of the business architecture can be modeled. It highlights the needs of business stakeholders and compares several existing models. The paper illustrates the inadequacy of these models in addressing our business questions, and proposes a model that offers some distinct advantages.