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Comment: The article's tone is not encyclopedic; we can't publish it like this since it's overly promotional and needs a complete rewrite. I'm also not convinced that this topic is notable. I can see that there are a lot of references but they don't count towards notability if they don't mention the topic of this article ("Data Excellence") specifically. — Jeraphine (talk) 15:10, 16 December 2015 (UTC)
Data Excellence is a set of methods, techniques and tools for achieving a sustainable business excellence. It was developed by Dr Walid El Abed in 2007 and is central to his business's strategy at Global Data Excellence (GDE). Nowadays, it is used in industrial sectors as well as by governments..
Data Excellence is an emerging discipline created to maximize the sustainable business value of enterprise data.Indeed, Data Excellence arises from the natural evolution of organizations and society in the information age where data is the key resource. 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. Since 2007, the Data Excellence discipline has been introduced and taught at the Research Centre Lucien Tesnière in Natural Language Processing, Franche-Comté University, 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 an imperative 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. 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.
Other early adopters of the Data Excellence include the Swiss Government (Canton du Valais), Givaudan, Groupe Mutuel Assurance, Schneider Electric, Alcatel Lucent, Michelin. Furthermore according to a Gartner analyst, the Data Excellence is qualified as "an alternative future"  .
The Data Excellence maturity model
The Data Excellence maturity model helps organizations worldwide to move successfully from the early stage of Data Excellence described as “chaotic” to the most mature stage described as “predictive”. At the later stage, data is utilized as a core enterprise asset. The Data Excellence maturity model is often used to aid understanding the right projects and the initiatives for introducing the discipline and methodology of Data Excellence. It aims at measuring and estimating the compliance level of organizations to Data Excellence best practices.
The 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 and support economical growth. In the world of big data, this methodology represents an opportunity to enable growth, profit, and flawless execution
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 backbone
The Business excellence requirement (BER) represents the backbone of the Data excellence framework. It is a prerequisite, business rule, standard, policy or best practice that business processes, transactions and data should comply with in order to have the business goals flawlessly executed to generate the value. Examples of BERs in the Banking domain are:
- BER 1: “a customer record of default must be provided for any loan application if the amount is over or equal to 10000€.” This record states whether the customer had defaulted on a loan repayment in the past.
- BER 2: “no loan is granted to minor customers (e.g., under 18).”
- BER 3: “each application must be accompanied with a pay slip.”
The Data Excellence measurement instruments
The Data Excellence Index (DEI) and the Key Value Indicator (KVI) are key deliverables of the Data Excellence Framework. The DEI is the instrument used to measure the compliance of data records with the BERs. The DEI results are used to evaluate the value and the impact of data on business operations and transactions. The BER concept makes the DEI actionable at the record level allowing the finest root cause analysis and surgical data governance.
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 a measurement of the value and impact of the DEI on business operations. A fundamental KVI represents the key business value and the impact affected by a specific BER. The KVI for any organizational level can be obtained by a contextual polarization together with the DEI. The KVI is a fundamental deliverable of the Data Excellence Framework. Through it, the framework enables multifocal governance linking business management and data management. The business transaction is therefore considered as a key component that enables managing data as a company asset. The DEI becomes the pivot that links the BER, the KVI, and the data elements.
The Data Excellence process
A guiding principle for a successful implementation of the Data Excellence framework is to avoid designing a process that requires 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 Data Excellence governance model
Most companies still invest in the core business functions focusing on business process optimization and cost rationalizations based on past transactions while the Data Excellence proposes a global, systemic and predicative data-driven "govern by value" concept.
The Data Excellence Framework involves the absolute “professionalization” of the Data excellence governance functions, which is operationalized through collaborative networks where the Data Excellence stewardship plays a pivotal role between business, data management and IT. The Data Excellence governance model establishes the accountability and the responsibility at each organizational level and according to the organization’s geography in order to maximize the business value of enterprise data.
- Accountability: the willingness and commitment of business executives to be accountable for the definition and management of the BERs of their area and related KVIs targets.
- Responsibility: The willingness and commitment of data managers to be responsible for individual data records assuring the compliance with BERs
The Data Excellence Management System (DEMS) is a collaborative system, which fully supports the implementation of Data Excellence Framework capabilities (namely the three-step approach presented in the Methodology's section).
The DEMS system is based on 3-tier standard architecture:
- 1st tier: the presentation logic provided by a web browser / web server
- 2nd tier: the business process logic and data access provided by an application server
- 3rd tier: the business data provided by a data server
- Reference data
- Data management
- Data steward
- Customer data integration
- Data integration
- Identity resolution
- Enterprise information integration
- Linked data
- Semantic Web
- Data governance
- Operational data store
- Big data
- Data Quality
- ↑ 1.0 1.1 Koller, Rodolph (2010). "Steria Suisse consolide son offre pour la qualité des données" (web). ICT Journal. Business Intelligence (Data Excellence). http://www.ictjournal.ch/fr-CH/News/2010/09/03/Steria-Suisse-consolide-son-offre-pour-la-qualite-des-donnees.aspx.
- ↑ DATA GOVERNANCE: A STEP TOWARD VALUE-DRIVEN COMPLIANCE & RISK MANAGEMENT. Risk & Compliance Magazine (2015). Retrieved on 2015-12-10.
- ↑ Centre Tesniere - Membres. Franche-Comté University (2015). Retrieved on 2015-12-10.
- ↑ Walid el.Abed's scientific biographie (2015). Retrieved on 2015-12-10.
- ↑ TEACHING STAFF | Master of Laws in Cross-Cultural Business Practice. Fribourg University of law (2015). Retrieved on 2015-12-10.
- ↑ The Economist - The Data Deluge (2010). Retrieved on 2015-12-10.
- ↑ Tucker &Topi, Taylor and Francis Group (2014). Computing Handbook, Third Edition: Information Systems and Information Technology. Retrieved on 2015-12-10.
- ↑ A Glimpse into the Future with Global Data Excellence? - Andrew White (en-US) (July 15, 2015). Retrieved on 2015-12-10.
- ↑ 9.0 9.1 9.2 9.3 Laure Berti-Equille (2012). La qualité et la gouvernance des données. (fr-FR). Retrieved on 2015-12-10.
- ↑ The Data Excellence Framework to Improve Global Safety and Security. 94-99. International Review Bulag, PUFC (2009). Retrieved on 2015-12-10.
- ↑ Khaled Crawoui in Senhajji Youtube Channel: https://www.youtube.com/watch?v=UXCmYsWJ_Gw+(2012), Walid el abed's interview on France 24 by Khaled Crawoui, http://www.france24.com/ar/20151204-الأسبوع-الاقتصادي-ج1-مؤتمر-المناخ-اتفاق-خفض-انبعاثات-غازات-الدول-النامية-الغنية, retrieved 2015-12-10
- ↑ "Domain Specific Language Based on the SBVR Standard for Expressing Business Rules". Enterprise Distributed Object Computing Conference Workshops (EDOCW), 2013 17th IEEE International (the IEEE Computer Society): 31-38. 2013-09-01. DOI:10.1109/EDOCW.2013.11. ISBN 978-0-7695-5085-5. http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6690531.
- ↑ Scopus. Retrieved on 2015-12-10.
- ↑ Davis, Brian, ed. (2014-08-20). "A Controlled Natural Language for Business Rule Specifications" (in en). Lecture Notes in Computer Science. Springer International Publishing. pp. 66-77. ISBN 978-3-319-10222-1. http://link.springer.com/chapter/10.1007/978-3-319-10223-8_7.
- ↑ "From natural language business requirements to executable models via SBVR". 2012 International Conference on Systems and Informatics (ICSAI) (IEEE Publications): 2453-2457. 2012-05-01. DOI:10.1109/ICSAI.2012.6223550. http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6223550.
- ↑ Selway, Matt; Grossmann, Georg; Mayer, Wolfgang; Stumptner, Markus (2015-12-01). "Formalising natural language specifications using a cognitive linguistic/configuration based approach". Information Systems 54: 191-208. DOI:10.1016/j.is.2015.04.003. http://www.sciencedirect.com/science/article/pii/S0306437915000630.
- ↑ Kampeera, Wannachai (2011-01-01). "La gouvernance des données dans un contexte de sécurité globale". WISG 2011, Workshop Interdisciplinaire sur la Sécurité Globale (Troyes, France): 7 pages, CD-ROM. https://hal.archives-ouvertes.fr/hal-00561173.
- ↑ Paratte, Nicolas (2011). ICTjournal. ICT Journal. Retrieved on 2015-12-10.
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