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Data Excellence
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Data Excellence
modifierData Excellence is a set of methods, techniques and tools for achieving a sustainable business excellence, created by Dr Walid El Abed in 2007 and integrated to his business' s strategy at Global Data Excellence (GDE).Nowadays, it is used in industrial sectors as well as by governments[1].
The Discipline
modifierData Excellence is an emerging discipline that maximize the sustainable business value of enterprise data. It emerged from the field of Data Governance whose goal is to produce continually high-quality data while lowering cost and complexity and supporting risk management and regulatory compliance[2]. Since 2007, the Data Excellence discipline has been introduced and taught at the Research Centre Lucien Tesnière in Natural Language Processing, Franche Comté Univesity, at the CNAM (Centre National des Arts et des Métiers),Paris Dauphine University and finally at the Fribourg University of Law.
The term " Data Excellence" originally comes from the vision to elevate data' s level of excellence and to emphasize a value-driven approach for enabling business excellence. Since then, Data Excellence has become a support to unlock enterprise potential and to enable sustainable value generation. Thus, data is made visible enabling it to become an asset for the enterprise to perform business[3]. The sustainable value generation is organized concretely by implementing an enterprise common framework, which enables a paradigm shift across the whole organization in order to strive for business success. According to a Gartner analyst (http://www.gartner.com/technology/analysts.jsp), the Data Excellence is qualified as "an alternative future".
Methodology
modifierThe Data Excellence methodology is based on four value pillars: agility, trust, intelligence, and transparency. These characteristics are fundamental value pillars to enable business excellence sustainability.
Generally speaking, the barriers to manage the data as a company asset, which is key for Data Excellence success, are:
- The insufficient alignment between business managers, the data management, and IT.
- The inability to demonstrate the business impact and the value of data.
- The unclear accountability and responsibility to govern data as an asset.
In order to lower those three barriers to manage data as a company asset and to maximize the business value of enterprise data, the methodology performs the three-step approach as follows:
- Aligning and linking business objectives to data management via Business Excellence Requirements (BERs) and data.
- Measuring and visualizing the data value and its business impact on each business context
- Organizing and executing sustainable governance based on the "govern by value" model proposed by the Data excellence discipline.
The Data Excellence measurement instruments
modifierThe Data Excellence Index ( DEI) and the Key Value Indicator (KVI) are key deliverables of the Data Excellence Framework. The DEI results are used to evaluate the value and the impact of data on business operations and transactions. The DEI is obtained by the contextual polarization, which is an innovative technique to visualize and organize systematically the DEI results ( components) according to the context and the level.
The KVI is measurement of the value and impact of the DEI on business operations. The KVI is a fundamental deliverable of the Data Excellence Framework.
The Data Excellence Process
A guiding principle for the implementation of the Data Excellence framework is to avoid designing a process that requirs organizational change but to try as much as possible that the Data Excellence process fit one's current organization. The continuous Data Excellence process must be organized according to each organization, which needs to evolve its culture and wishes to accelerate a methodology shift to achieve business excellence through Data Excellence.Thus, this methodology shift is achieved through five distinct steps called DMPFA ( Define,Measure,Publish,Fix and Analyze ).
The Software
modifierThe Data Excellence Management System (DEMS) is a collaborative system, which fully supports the implementation of Data Excellence Framework capabilities.
The DEMS system is based on 3-tier standard architecture:
- 1st tier: the presentation logic provided by a web browser/ web server
- 2 nd tier: the business process logic and data access provided by an application server
- 3 rd tier: the business data provided by a data server
The References
modifier- Rodolph Koller Rodolph (2010), « Steria Suisse consolide son offre pour la qualité des données », ICT journal, (consulté le )
- DATA GOVERNANCE, « A step toward value-driven compliance & risk management », Risk & Compliance Magazine,
- The Economist, « The Data Deluge », The economist,