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Thursday, August 09th, 2012 | Author:

On 9 August 2012, Rudo Denneman defended his MSc thesis on a requirements analysis for business intelligence of CRM processes of municipalities. The MSc project was carried out at Exxellence.
“Management information requirements for customer relationship management in municipalities”[download]
This research project looks into the management information requirements of municipalities in the Netherlands, related to their customer relationship program. Information requirements engineering methodologies for data warehouses are reviewed and a method is proposed based on its perceived suitability for the municipality context. The used methodology by Winter and Strauch matches information requirements elicitation with analyses of the data sources to get an overview of requirements and whether they are attainable. Results are a list of management information requirement, representation requirements and an advice to Exxellence Group on how they can foresee in this demand.
The resulting list of management information requirements seems to indicate that the management of client contact centres would like to see more management information than what it currently prescribed by the Antwoord© concept on which they have based their management information needs for the most part. The list was sent back to municipalities to allow them to comment and rate the information needs on their usefulness. Also, the COPC standard on which the Antwoord© indicators are based and the Antwoord© indicators themselves were compared to the results. The results seem to cover almost all of the COPC metrics except for several process areas that are not as relevant in the municipality context. Also potentially interesting additions to the results that could be made from the COPC standard have been identified. The indicators from the Antwoord© concept score relatively high in the ranking of information needs and are a solid basis for measurements.
Overall, the information needs voiced by municipalities are on an operational level to measure performance of departments and individual employees over time. To satisfy the information needs, Exxellence group will have to combine data from several back-office source systems along with other information from other sources such as customer satisfaction surveys. These sources will have to be identified per municipality due to the large variance in the types of back-office systems that are used in different municipalities. A data warehouse schema should be created that matches the information needs. The sources of information used to fill the data warehouse can then be identified per municipality.
In addition municipalities will have to access their processes and the training level of their personnel to see whether they are able to correctly capture all the information required to satisfy the information needs.

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Thursday, March 08th, 2012 | Author:

A journal paper with my vision on data interoperability and a basis formalization has been accepted for a special issue of the Journal of IT volume 54, issue 3.
Managing Uncertainty: The Road Towards Better Data Interoperability.
Maurice van Keulen
Data interoperability encompasses the many data management activities needed for effective information management in anyone´s or any organization´s everyday work such as data cleaning, coupling, fusion, mapping, and information extraction. It is our conviction that a significant amount of money and time in IT that is devoted to these activities, is about dealing with one problem: “semantic uncertainty”. Sometimes data is subjective, incomplete, not current, or incorrect, sometimes it can be interpreted in different ways, etc. In our opinion, clean correct data is only a special case, hence data management technology should treat data quality problems as a fact of life, not as something to be repaired afterwards. Recent approaches treat uncertainty as an additional source of information which should be preserved to reduce its impact. We believe that the road towards better data interoperability, is to be found in teaching our data processing tools and systems about all forms of doubt and how to live with them. In this paper, we show for several data interoperability use cases (deduplication, data coupling/fusion, and information extraction) how to formally model the associated data quality problems as semantic uncertainty. Furthermore, we provide an argument why our approach leads to better data interoperability in terms of natural problem exposure and risk assessment, more robustness and automation, reduced development costs, and potential for natural and effective feedback loops leveraging human attention.