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EA Data Collection

To create an EA model, data from different sources needs to be collected and integrated [PTS10]. This is a non-trivial process.

EA Data Collection Foundations

Data necessary as input for the EA model of an organization is spread throughout the enterprise [PTS10]. This data needs to be collected and integrated, which is usually done using an EAM repository [PTS10]. The data to be collected is defined in the EA information model of the organization [Bus+12]. Based on this information, the sources of the data need to be identified [PTS10]. Project management tools, enterprise resource planning systems as well as network monitoring utilities can be such data sources [PTS10]. Manual integration of data into the EAM tool is error prone, expensive, and time consuming, therefore as much as possible of the data collection and integration should be automated [Bus+12].

Moreover, it is not sufficient to collect information on the current status of the EA and use it as a basis for future decision making [Buc+08a]. Instead, the data collection process should be designed in a manner, that ensures the availability of up-to-date data [Buc+08a]. Therefore, appropriate rules enforcing data collection need to be set up [Buc+08a].


Challenges of Data Collection

The documentation of EA information constitutes as major challenge for many organizations [Far+13]. First of all, it is difficult to determine which information is really needed by the EA stakeholders and what quality it needs to have [Bus+12]. Further, the complexity of the EA and its frequent changes make it cumbersome to keep the information basis up-to-date [Far+13]. Even though it is widely agreed that automation of data collection could solve this problem, only few enterprises really have automated data integration processes between information systems and their EAM tool in place [Far+13]. The main reasons for this deficit are the data granularity mismatch between data source and the EA information model, the high implementation cost and the low data quality of source data [Far+13]. Therefore, data collection remains a mainly manual and event-driven process, which is very time consuming and error prone [Far+13].



M. Farwick, M. Hauder, S. Roth, F. Matthes, and R. Breu. “Enterprise Architecture Documentation: Empirical Analysis of Information Sources for Automation.” In: Hawaii International Conference on System Sciences (HICSS 46). Maui, Hawaii, 2013.


M. Postina, J. Trefke, and U. Steffens. “An ea-approach to develop soa viewpoints.” In: Enterprise Distributed Object Computing Conference (EDOC), 2010 14th IEEE International. 2010, pp. 37–46.


S. Buckl, A. Ernst, J. Lankes, and F. Matthes. Enterprise Architecture Management Pattern Catalog (Version 1.0, February 2008). Tech. rep. TB0801. Chair for Informatics 19 (sebis), Technische Universität München., 2008.