TOOLS FOR DATA GOVERNANCE PASSIONATE BY DATA AND THE ACCURACY OF THE RESULTS

DOMAINDOMAIN REVER DBMS Data Access Processings Presentation Programs management (web server, transactional, jcl, …) Data DBMS Data Access Processings Presentation Programs management REVER Data are at the heart of the I.S. and are the elements allowing BUSINESS CONTINUITY Data are at the heart of the I.S. and are the elements allowing BUSINESS CONTINUITY

SOLUTIONS EVOLVE-EASY DEV-EASY DATA QUALITY S.E.A.L. DOC-EASY SHARED MASTERY SHARED MASTERY ÉVOLUTIONS WITHOUT RISK MEASURES CORRECTIONS DB-MAIN KNOWLEDGE MODELLING EXTRACTIONS ANONYMIZATIONS ACCESS LAYER DEVELOPMENT ACCELERATOR

ARCHITECTURE

CHARACTERISTICSCHARACTERISTICS  INDUSTRIAL  AUTOMATED  CONTROLS  Integrated in the proccesses  Applications independant REVER SOLUTIONS  ADAPTABLE  GÉNÉRIC  Methods  Tools  FLEXIBLE  PROGRAMMABLE  SERVICES  SUBCONTRACTING  SIDE BY SIDE  Training  Support  Follow-up

SOLUTIONS

1 OUT OF 2 COMPANY declares 1 OUT OF 4 COMPANY déclares « WE DO NOT KNOW WHICH ARE THE REAL USES OF OUR DATA » « THE DATA AT THE DISPOSAL OF DEVELOPERS WERE USED FOR OTHER PURPOSES » WHAT STUDIES TELL US* *Ponemon Institute

THE MANAGEMENT OF THE DATA USES 60 % OF THE TESTING TIME THE MANAGEMENT OF THE DATA USES 60 % OF THE TESTING TIME THE DATA RELATED TO A CONCEPT (customers, suppliers, products) ARE SPREAD IN VARIOUS TECHNICAL DATABASES THE DATA RELATED TO A CONCEPT (customers, suppliers, products) ARE SPREAD IN VARIOUS TECHNICAL DATABASES STUDIES (follow)

DEFINITIONS DATA Generic name covering the notions of:  « category» (name) designed by « column »  « value» (Smith) designed by « content » DATA Generic name covering the notions of:  « category» (name) designed by « column »  « value» (Smith) designed by « content » TABLE Collection of grouped columns to represent a concept TABLE Collection of grouped columns to represent a concept LINK All type of relations between columns There are numerous "types of link: dependency, referential, redundancy, … LINK All type of relations between columns There are numerous "types of link: dependency, referential, redundancy, …

REFERENTIAL LINK The column establishing a link between the content of 2 tables REFERENTIAL LINK The column establishing a link between the content of 2 tables REDONDANCY LINK The column which takes the content of another column at time T REDONDANCY LINK The column which takes the content of another column at time T ORDERS réf. Cli. Nbr Order NbrAmountDelivery ad ORD €PHOENIXCLI 001 ORD €NYCLI 001 DEFINITIONS ORDERS réf. Cli. Nbr Order NbrAmountDelivery ad ORD €PHOENIXCLI 001 ORD €NYCLI 001

DATABASE Technical « container » grouping a collection of tables DATABASE Technical « container » grouping a collection of tables COPY Reproduction of an "original" content for processing purposes COPY Reproduction of an "original" content for processing purposes PROCESS Term which denotes either manual processes, or automated processes, or any combination of manual and automated processes PROCESS Term which denotes either manual processes, or automated processes, or any combination of manual and automated processes DEFINITIONS

« clients » « payments» DOSSIER: tables collection linked directly or indirectly with a main table DOSSIER: tables collection linked directly or indirectly with a main table DEFINITIONS

CLIENTS BASE ORDERS BASE PAYMENTS BASE The notion of dossier is independent from the "technical" implementation and is mostly "transverse" in databases DEFINITIONS

S.E.A.L. : Select, Extract, Anonymize & Load

THE NEEDS

S.E.A.L

PRODUCTION DATABASES S.E.A.L. DATABASE. PRODUCTS ORDERS PAYMENTS CLIENTS « TECHNICAL » DESCRIPTION OF THE TABLES AND COLUMNS « TECHNICAL » DESCRIPTION OF THE TABLES AND COLUMNS Project manager DATABASES SELECTION DATABASES SELECTION ADDITION  REFERENTIAL LINKS  REDONDANCY LINKS ADDITION  REFERENTIAL LINKS  REDONDANCY LINKS « FUNCTIONAL » DESCRIPTION « FUNCTIONAL » DESCRIPTION THE DATA YOU HAVE

REDONDANCY LINKS REFERAL LINKS PRODUCTS ORDERS PAYMENTS CLIENTS THE DATA YOU HAVE

SELECT THE « NECESSARY AND SUFFICIENT » DATA FOR THE FORECASTED PROCESSINGS SELECT THE « NECESSARY AND SUFFICIENT » DATA FOR THE FORECASTED PROCESSINGS DEFINE THE DOSSIERS SELECT THE CONTENTS S.E.A.L. database « FUNCTIONAL » DESCRIPTION « FUNCTIONAL » DESCRIPTION TABLES LIST Ordered in THE ORDER OF THE PROCESSINGS TABLES LIST Ordered in THE ORDER OF THE PROCESSINGS COMBINATION OF THE SELECTION CRITERIA THE DATA « YOU WANT »

PROJECT M CAMPAIGN i CAMPAIGN j THE DATA « YOU WANT »

CAMPAIGN j SELECT the CONTENTS « Name clients = SMITH » SELECT the CONTENTS « Name clients = SMITH » THE DATA THAT « YOU WANT »

COPIES CONTROLS COLUMN NOT TO BE USED FOR SELECTING CONTENTS COLUMN YOU MAY NOT COPY LIMIT TO THE NUMBER OF DOSIERS TO BE COPIED e,g, minimum 100 dossiers LIMIT TO THE NUMBER OF DOSIERS TO BE COPIED e,g, minimum 100 dossiers THE PROTECTIONS

RULES MASKING LIST CALCULATION Specific functions ANONYMIZATION

COLUMNS RÉGLES PROJECTS/ CAMPAIGNS PROJECTS/ CAMPAIGNS Client name Client name Rule A (masking) Rule A (masking) PROJ M/ CAMP i PROJ M/ CAMP i PROJ M/ CAMP j PROJ M/ CAMP j Client name Client name Rule B (list) Rule B (list) Birth date Birth date Rule C (calculated) Rule C (calculated) PROJ M/ CAMP i PROJ M/ CAMP i PROJ M/ CAMP j PROJ M/ CAMP j Birth date Birth date Rule D (calculated) Rule D (calculated) ANONYMIZATION

EXTRACTION ENGINE ALLOWS THE EXTRACTION OF THE DOSSIERS EXTRACTION ENGINE ALLOWS THE EXTRACTION OF THE DOSSIERS GENERATION ENGINE ADD LINES AND 3POPULATE 3THE COLUMNS GENERATION ENGINE ADD LINES AND 3POPULATE 3THE COLUMNS ANONYMIZATION ENGINE ANONYMIZE THE CONTENTS ANONYMIZATION ENGINE ANONYMIZE THE CONTENTS STORAGE ENGINE GIVES THE RESULTING DOSSIERS STORAGE ENGINE GIVES THE RESULTING DOSSIERS REPORT ENGINE PRODUCES THE REPORTS AND STATISTICS REPORT ENGINE PRODUCES THE REPORTS AND STATISTICS THE ENGINES

THE ANONYMIZATION ENGINE

DATABASE FROM THE SOFTWARE PACKAGE (ERP,CRM,….) DATA TO BE PROCESSED ANONYMIZED DATA TO BE PROCESSED REAL CONTENTS FICTIVE CONTENTS CORRESPONDENCE REAL CONTENTS FICTIVE CONTENTS CORRESPONDENCE ANONYMIZATIONS EXPORT PROCESSED DATA PROCESSED ANONYMIZED DATA RE- IDENTIFICATION PROCESSING IMPORT PROCESSING EXAMPLE: SOFTWARE PACKAGES

YOU WANT TO COPY INTEGRATE ONE DOSSIER SEVERAL DOSSIERS SEVERAL DOSSIERS ALL THE CONTENTS from one or more databases S.E.A.L. FUNCTIONS In your applications or packages S.E.A.L. FUNCTIONS In your applications or packages THE S.E.A.L. PRODUCTS

COPY PART OF THE DATA COPY A COMPLETE DATABASE AN INDIVIDUAL DOSSIER SEVERAL DOSSIERS ONLY THE TABLES NEEDED FOR PROCESING BAN TO COPY CERTAIN COLUMNS OBLIGATION TO COPY A MINIMUM Nbr OF DOSSIERS CONTENTS ANONYMIZATION S.E.A.L. The products PROTECTING all COPIES of your DATA S.E.A.L. The products PROTECTING all COPIES of your DATA PROTECTIONS SUMMARY

FUNCTIONAL APPROACH INTUITIVE AND FRIENDLY INTERFACE RULES AND ANONYMIZATION DESCRIPTIONS RE-USE RULES AND ANONYMIZATION DESCRIPTIONS RE-USE PARTIAL COPIES DÉFINITIONS AND OPERATIONS STORED IN A SPECIALIZED DATABASE QUICK INSTALLATION AND CONFIGURATION MONO DATABASE MULTI DATABASES MONO DATABASE MULTI DATABASES RÉDUCTION OF THE TECHNICAL RESSOURCES MAINTAIN COHERENCE SIMPLE INCREASE OF PRODUCTIVITY COSTS DECREASE FUNCTIONALITIES TECHNICAL ADDED VALUE S.E.A.L. MAIN ADVANTAGES

THE MECHANISMS USED IN S.E.A.L. ARE INDEPENDENT FROM THE DATA "SEMANTICS" S.E.A.L. IS DIRECTLY USABLE BY EVERY TYPES OF"BUSINESSES" « AFFORDABLE » PRICING « AFFORDABLE » PRICING S.E.AL. ADVANTAGES (more)

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