Global Data Excellence

The Data Excellence Framework ©

Dr. Walid El AbedCEO & Founder [email protected]

Global Data Excellence Ltd www.globaldataexcellence.com

Copyright 2008, Global Data Excellence Ltd. All rights reserved

Enable the Enterprise to be trusted,intelligent and agile through highestinformation quality empowering its Business Partners, Employees and Stakeholders.

Vision “Elevate Data to Trusted Intelligence” ©

Data is the basis for any communication. Its quality is an imperative pillar for the trust in enterprise image, brands and services.


Data Excellence Framework ©

Mission

Page 3

“Value Generation”


Introduce new products to market in 30 days

Divest a company in 6 weeks

Merge a new company in 8 weeks

Reach operational excellence in 1 year

Transform the business in 2 years

“Reduce number of applications by 50%”

“Reduce number of ERP by 75%”

“Reduce number of helpdesks to 1”

“Reduce number of competency centers to 2”

“Reduce number of vendors by 60%”

Value generation for 20xy Data strategy is critical to achieve 5 years business plan



Value Generation - People First

TransparencySocial Responsibility

Collaboration…

TrustComplianceTraceability

…

IntelligenceReporting

Business Analysis…

AgilityBusiness Transformation

M&A…



Identity

“Intelligence Quality”


Data integration Platform (Standards, Best practices, Processes Defined and aligned)

Data Cleansing approach to Data Quality

Integrated, Shared Master Data Repository

Common approach to share Basic Data

No Common standards

Reactive Approach to Data Integration

Non integrated Master Data

No common approach to share data externally

Data Excellence Maturity Model © From Chaos to Data Excellence

Data Excellence Framework Executed (Business Rules Stewardship)

Data Excellence Center (Education and training)

Data Pollution prevention approach to Data Quality

Increased transparency thru data sharing

Data Excellence Framework defined (Data Quality KPIs levels Visibility)

Business Rules Captured in a repository

Data Management services

Common platform to share data externally

Data Excellence at Design (Governance)

Data Excellence embedded in the Enterprise culture

Full Transparency and Information Sharing

Trusted, Agile and Intelligent Enterprise

©


Scope: - All vehicles (e.g. 1 million)

Rule:- If l then ”Stop”- else If l then, “Prepare to Stop” - else If l then “Go” KVI © Results

- 93.4% for consistency

Business Impact- 6.6% of indecision (inconsistent with the rule), may

lead to crashes i.e. 66,000 instances Business Value

- Cost of a each crash is15K. Preventing 66,000 instances may save 990 millions


Example: Business Rule, Impact & Value



Example: Impact, Value & KVI‟s



Allergen Data Completeness

KSI’s Results• 94.7% Finished Products are consistent with allergens regulationsBusiness Impact - Value• Any potential consumer of the product, judicial consequences, fines, business

closure• Consumer's trust on company brand and image

Case: Distribution

Business Rule:• All food or medical products must

consistently (across all processes and databases) include data on allergens contained in its constituents and raw materials as per US-FDA regulations


Sample of Operational Reporting - KVI‟s



Executive reporting - Overall Data Quality KVI

Aggregation in one KVI of all published Business Rules

an overall view of the Company data Quality

Analysis by Data Quality Dimension of ~1.0 Mio records according to set of Business Rules



Dimensions in Data Quality Reporting

Data Quality Score byQuality Dimension per Region

Data Quality KVI’s byData Object Per Region



KVI Calculation And Aggregation Model (CAAM)Master Data Quality KPI%

Completeness KPI% Accuracy KPI% Non Duplicate KPI% Non Obsolete KPI% Consistency

100%

Records

records Incomplete1 100%

Records Sample

records Inaccurate1 100%

Records

records Duplicate1 100%

Records

records Obsolete1 100%

Records

recordsnt Inconsiste1

100%Records DataMaster

recordsnt Inconsiste Obsolete duplicater Incomplete-Records DataMaster recordsnt Inconsiste Obsolete duplicater Incomplete1

RatioInaccuracyororoororo

Material Completeness KPI%

100%

Records

records Incomplete1

Customer Completeness KPI%

100%

Records

records Incomplete1

Vendor Completeness KPI%

100%

Records

records Incomplete1

Employee Completeness KPI%

100%

Records

records Incomplete1

Customer Accuracy KPI%

100%

Records Sample

records Inaccurate1

Vendor Accuracy KPI%

100%

Records Sample

records Inaccurate1

Material Accuracy KPI%

100%

Records Sample

records Inaccurate1

Employee Accuracy KPI%

100%

Records Sample

records Inaccurate1

Material Non Duplicate KPI%

100%

Records

records Duplicate1

Employee Non Duplicate KPI%

100%

Records

records Duplicate1

Vendor Non Duplicate KPI%

100%

Records

records Duplicate1

Customer Non Duplicate KPI%

100%

Records

records Duplicate1

Material Non Obsolete KPI%

100%

Records

records Obsolete1

Employee Non Obsolete KPI%

100%

Records

records Obsolete1

Vendor Non Obsolete KPI%

100%

Records

records Obsolete1

Customer Non Obsolete KPI%

100%

Records

records Obsolete1

Material Consistency KPI%

100%

Records


Employee Consistency KPI%

100%

Records


Vendor Consistency KPI%

100%

Records


Customer Consistency KPI%

100%

Records


100%Records DataMaster

recordsnt Inconsiste Obsolete duplicater Incomplete-Records DataMaster recordsnt Inconsiste Obsolete duplicater Incomplete1

RatioInaccuracyororoororo

Material Data Quality KPI% Customer Data Quality KPI% Vendor Data Quality KPI% Employee Data Quality KPI%



Data Quality Dimensions to Support the thresholds and KVIs

Completeness All records and relationships exist for a business process

(Non-) Accuracy Values must reflect the intended usage and the state in the real world

(Non-) duplicate Every business object instance represents one unique world event

(Non-) Obsolete Every business record is current and up to date

Consistency Data values of a business attribute are consistent when they are duplicated

Accessibility Information is accessible

Timeliness Information is available on time

Clarity Information includes all rules and its usage is understood without ambiguity

Data KVIs

Time Mgt.

Problem Mgt.



Data Excellence Process

Calculate the KVI‟s

Populate the Semantic Meta

Model

IT

DataSteward

Capture the Business rule

Processowner

Monitor KVI‟s and Exception

ReportsDataSteward

Root Cause Analysis

Processowner

Publish the KVI‟s

DataSteward

Processowner

Intranet

Data Quality Semantic

Rules

Data

DQ KVI‟s



Data Excellence Process (Actions and Activities)

Root Cause

IssueResolution

KVI Publication

KVI ©Calculation

BusinessRules

Define

Validate

Implement

Maintain

Define

Develop

maintain

Pull Fix Analyse

Define Business rules to support data quality dimensions per Business object

Measure by defining and implementing global data quality KVIs and exception reports based on predefined Business rules

Publish, providing visibility of data quality to the whole organization at all levels

Fix by monitoring the KVIsand the exception reports to take corrective action

Analyse and address root cause and improve Business processes

Action

Activity



Roles to support the process

Data Governance executed through Data Stewardship which include following roles:

- Business rule accountable.- Business Rule steward.- Data responsible.

Supported by - Data excellence group

Enabled by- Data Excellence framework. Data Excellence Framework

Data Excellence Group

Business rule

Accountable

Business rule

Steward

Data

Responsible

Stewardship


Data Excellence Framework ©The cultural change (From To)



Conclusion

Data Excellence can be achieved progressively (non invasive)

Data Excellence must be a value added driven program

Communication is key to Data Excellence, make it visible and understood by the whole organization at all levels

GO for Data Excellence !


ExQI – Association Française pour la promotion de la culture d„Excellence en matière de Qualité des données et de l'Information


ExQI - Excellence Qualité Information

Association Loi 1901

Créée le 11 mars 2009

Publiée le 25 avril 2009 au JO

7 Membres Fondateurs:- Sylvaine Nugier, EDF

- Brigitte Laboisse, A.I.D.

- Delphine Clément, Hewlett Packard

- Dr. Walid el Abed, Global Data Excellence

- Marc Daverat, Dataflux

- Virginie Goasdoué-Thion, Paris Dauphine

- Laure Berti Equille, Rennes 1 IRISA

Conseil d’Experts: opérationnels et scientifiques


ExQI – Nos objectifs

Excellence Qualité Information

• Propager la culture qualité des données et de l’information à tous les niveaux des organisations, des entreprises et de la Société de l’Information

• Définir et normaliser des méthodes et des démarches qualité de données, réaliser des benchmarks et développer des échanges de bonnes pratiques dans le domaine de la qualité des données et de l’information

• Créer des groupes d'intérêt et d’étude entre les industriels, praticiens, institutionnels et chercheurs sur les différents aspects de la qualité des données et de l’information

• Etre force de proposition et de recommandation dans le domaine de la qualité des données et de l’information auprès des diverses institutions publiques et privées (notamment gouvernementales, universitaires et de normalisation)

• Organiser des événements sur le thème de la qualité et de la gouvernance des données

« ExQI se veut une plate-forme coordonnée d’échanges et de partage de bonnes pratiques entre différents acteurs experts du marché – entreprise, éditeur de solutions, société de conseil, universitaire. »


ExQI – Les groupes de travail

ExQIMoLien entre acteurs académiques et industriels

La vocation d’ExQIMo est de promouvoir, faciliter etconcrétiser les échanges d’idées, de compétences et letransfert technologique entre les acteurs du mondeacadémique et les acteurs industriels dans le domaine de laqualité et de la gouvernance des données.

ExQIValenceMesure comparative de la Qualité des Données

ExQIValence a pour objectif de réaliser un benchmark qualité de données. Pour ce faire, le groupe de travail propose de réunir industriels, éditeurs de solutions, fournisseurs de données, prestataires de services, statisticiens autour de la thématique des Indicateurs Clés (KQI) à mettre en place et du calcul du ROI de la qualité des données.

ExQIGouvQualité et Gouvernance des Données

ExQIGouv replace les problématiques de Qualité desdonnées et de l’information dans le débat plus large de la« Gouvernance des Données », avec pour objectifd’analyser et clarifier les facteurs clefs de succès de ce typede démarche alliant organisation, processus ettechnologie.

Les projets ExQIFocus opérationnel sur des sujets pointus

ExQI, c’est également des projets autour deproblématiques du domaine telles que la synchronisationdes horloges, ou encore le multi-source sur lesquelsl’association apporte un regard d’expert.


ExQI bientôt sur http://www.exqi.asso.fr (en cours de validation)

Rejoignez le groupe ExQI sur LinkedIn

ExQI: 4 rue Henri le Sidaner – 78000 Versailles

Information contact – Email : [email protected]

Prendre des nouvelles d’ExQI…

http://www.exqi.asso.fr/

mailto:[email protected]

Thank you for your attentionCopyright 2008, Global Data Excellence Ltd. All rights reserved

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