Library systems are becoming increasingly open, providing API’s that can deliver data in real-time. ETSU Library staff wanted a way to collect metrics from various systems and present it in one centralized dashboard. This presentation will cover our experiences with the project, including successes, challenges, and future goals.

Since there is limited research on the use of data dashboards in the evaluation field, this study explores the integration of a data dashboard in a knowledge network, the Centre for Research on Educational and Community Services (CRECS) as part of its program evaluation activities. The study used three phases of data collection and analysis. It investigates the process of designing a dashboard for a knowledge network and the different uses of a data dashboard in a program evaluation context through interviews and focus group discussions. Four members of the CRECS team participated in one focus group; two other members participated in individual interviews. Data were analyzed for thematic patterns. Results indicate that the process of designing a data dashboard consists of five steps that indicate the iterative process of design and the need for sufficient consultations with stakeholders. Moreover, the data dashboard has the potential to be used internally, within CRECS, and externally with other stakeholders. The data dashboard is also believed to be beneficial in program evaluation context as a monitoring tool, for evaluability assessment, and for evaluation capacity building. In addition, it can be used externally for accountability, reporting, and communication. The study sheds light on the potentials of data dashboards in organizations, yet prolonged and broader studies should take place to confirm these uses and their sustainability.

Innovation Management Software contains complex data with many different variables. This data is usually presented in tabular form or with isolated graphs that visualize a single independent aspect of a dataset. However, displaying this data with interconnected, interactive charts provide much more flexibility and opportunities for working with and understanding the data. Charts that show multiple aspects of the data at once can help in uncovering hidden relationships between different aspects of the data and in finding new insights that might be difficult to see with the traditional way of displaying data. The size and complexity of the available data also invites analyzing it with machine learning techniques. In this thesis it is first explored how machine learning techniques can be used to gain additional insight from the data and then the results of this investigation are used together with the original data in order to build a prototypical dashboard for exploratory visual data analysis. This dashboard is then evaluated by means of ICE-T heuristics and the results and findings are discussed.

The aim of this thesis is to create data acquisition application. The software module is designed for the industrial panel PC. Power Panel 520 from Bernecker + Reiner and the data acquisition board NI USB-6215 from National Instruments represent the fundamental hardware components for this project. Data acquisition and visualization tasks are controlled by a standalone application made in LabVIEW 2011. OPC, industrial standard in communication, is responsible for mutual interconnection between control (PLC) and data acquisition part. Network shared variables and Apple iPad 2 allow remote surveillance for operator. Finally, the creation of user-friendly interface and integration of technologies from different vendors, are main goals of this project.