SlideShare una empresa de Scribd logo
1 de 34
Best Practices:  Data Administration and Quality Daniel Linstedt, all rights reserved, http://LearnDataVault.com
Introduction and Expectations ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Agenda ,[object Object],[object Object],[object Object],[object Object]
Defining Data Administration Issues
What is Data Administration? “ What do we mean by that in the case of data administration? We mean that DA must get out of the design review committee mentality and substitute something more value-added and flexible. It must recognize that systems tend to grow organically, and be a part of that process, rather than an instiller of order upon it.”  Eric Rawlins, 1995 Originally Published by: Database Research Group, Inc http://www.well.com/user/woodman/organic.html
The Role of Data Administration ,[object Object],[object Object],[object Object]
Cross-Organization Roles and Responsibilities Business ( Owner View) Data Steward Discipline Authority Business Process  Manager Data Usage Contact Data Manager Data Modeler DA is a  ROLE  and typically involves more than one person in order to achieve success. Logical (Designer  View) Data Administrator Physical ( Builder View) Database Administrator
Data Administrator Responsibilities ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Top 10 Data Administration Issues ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Defining Data Administration Issues Top 4 Examples
Defining Master Metadata ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Defining Master Data Management ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Assessing Logical Model Viability ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Defining Business Process Models ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Applying Best Practices
Revealing the DA Best Practices ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
DA: MDM and Master Metadata ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Many times we see a cross-role responsibility of data management and data administration.  The cross-role is responsible for the following:
Work Breakdown Structure ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Organizational Breakdown Structure ,[object Object],[object Object],[object Object],[object Object]
DA: Architecting Data Governance Business Rules & IQ EDW Source Systems Non Compliant Data Marts Business Rules & IQ EDW Source Systems Data Marts Compliant Hard Business Rules Soft Business Rules & IQ  Shift  to process AFTER  the EDW Hard Business Rules Still process  Before the EDW
Establishing Auditable Sources Sync  Routines Data 2 nd  Source System Staging EDW Data Warehouse Source System Data Export Sync  Routines OLTP Oper Reports DW Exports ,[object Object],[object Object],[object Object]
DA – Defining Data Errors and Models ,[object Object],[object Object],[object Object],[object Object],B.I. Tool Database Wrtr xform Rdr ETL Load Process Source System Staging Area Data Warehouse Data Marts **Error Stage **Error Warehouse Error Marts ** Not usually implemented
DA Example –  Classifications of Errors ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Business Owns the Error I.T. Owns the Error
DA: Tracking Errors – KPIs at Work
Metadata and Data Administration ,[object Object],[object Object],[object Object],[object Object],[object Object]
Metadata Administration Lifecycle Identify New  Metadata Integrate With Master Metadata  Repository Edit and Manage Master Metadata (Provide Business Users  with Web Interface) Stitch  Master Metadata Together Compare Master Metadata With Business Process And Objectives Export Master Metadata or Deploy via SOA With Master Data Set Derived from Meta Integration Metadata Lifecycle
Monitoring DA Efforts ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Establish KPIs for Each of the Following Areas
Case Study for DA Results ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],After Implementing DA Best Practices
Conclusions and Q&A
Revealing the DA Best Practices (Recap) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
The Experts Say… “ The Data Vault is the optimal choice for modeling the EDW in the DW 2.0 framework.”  Bill Inmon ,[object Object],Stephen Brobst “ The Data Vault is a technique which some industry experts have predicted may spark a revolution as the next big thing in data modeling for enterprise warehousing....”  Doug Laney
More Notables… ,[object Object],Scott Ambler
Where To Learn More ,[object Object],[object Object],[object Object],[object Object]
Thank you Contact us today: Dan Linstedt [email_address] http://LearnDataVault.com

Más contenido relacionado

La actualidad más candente

Enterprise Data Management
Enterprise Data ManagementEnterprise Data Management
Enterprise Data Management
Bhavendra Chavan
 
Data warehouse architecture
Data warehouse architectureData warehouse architecture
Data warehouse architecture
pcherukumalla
 
Introduction to Data Warehousing
Introduction to Data WarehousingIntroduction to Data Warehousing
Introduction to Data Warehousing
Eyad Manna
 

La actualidad más candente (20)

Data Warehouse 101
Data Warehouse 101Data Warehouse 101
Data Warehouse 101
 
Data Warehousing and Data Mining
Data Warehousing and Data MiningData Warehousing and Data Mining
Data Warehousing and Data Mining
 
Database Performance Tuning
Database Performance Tuning Database Performance Tuning
Database Performance Tuning
 
Data warehousing
Data warehousingData warehousing
Data warehousing
 
Basic Introduction of Data Warehousing from Adiva Consulting
Basic Introduction of  Data Warehousing from Adiva ConsultingBasic Introduction of  Data Warehousing from Adiva Consulting
Basic Introduction of Data Warehousing from Adiva Consulting
 
Master Data Management
Master Data ManagementMaster Data Management
Master Data Management
 
Enterprise Data Management
Enterprise Data ManagementEnterprise Data Management
Enterprise Data Management
 
Data warehouse proposal
Data warehouse proposalData warehouse proposal
Data warehouse proposal
 
Project Presentation on Data WareHouse
Project Presentation on Data WareHouseProject Presentation on Data WareHouse
Project Presentation on Data WareHouse
 
Data warehouse architecture
Data warehouse architectureData warehouse architecture
Data warehouse architecture
 
Architecture of integration services
Architecture of integration servicesArchitecture of integration services
Architecture of integration services
 
5 Level of MDM Maturity
5 Level of MDM Maturity5 Level of MDM Maturity
5 Level of MDM Maturity
 
DBMS Bascis
DBMS BascisDBMS Bascis
DBMS Bascis
 
Introduction to Data Warehousing
Introduction to Data WarehousingIntroduction to Data Warehousing
Introduction to Data Warehousing
 
The what, why, and how of master data management
The what, why, and how of master data managementThe what, why, and how of master data management
The what, why, and how of master data management
 
Data warehouse presentaion
Data warehouse presentaionData warehouse presentaion
Data warehouse presentaion
 
Masterclass Live: Amazon EMR
Masterclass Live: Amazon EMRMasterclass Live: Amazon EMR
Masterclass Live: Amazon EMR
 
Data warehouse architecture
Data warehouse architecture Data warehouse architecture
Data warehouse architecture
 
ETL VS ELT.pdf
ETL VS ELT.pdfETL VS ELT.pdf
ETL VS ELT.pdf
 
Why shift from ETL to ELT?
Why shift from ETL to ELT?Why shift from ETL to ELT?
Why shift from ETL to ELT?
 

Destacado

Data Quality Best Practices Nbk Auto May 06 2010
Data Quality Best Practices  Nbk Auto May 06 2010Data Quality Best Practices  Nbk Auto May 06 2010
Data Quality Best Practices Nbk Auto May 06 2010
Rami Mansour
 

Destacado (8)

NLP Data Cleansing Based on Linguistic Ontology Constraints
NLP Data Cleansing Based on Linguistic Ontology ConstraintsNLP Data Cleansing Based on Linguistic Ontology Constraints
NLP Data Cleansing Based on Linguistic Ontology Constraints
 
Data Cleansing introduction (for BigClean Prague 2011)
Data Cleansing introduction (for BigClean Prague 2011)Data Cleansing introduction (for BigClean Prague 2011)
Data Cleansing introduction (for BigClean Prague 2011)
 
Scaling Big Data Cleansing
Scaling Big Data CleansingScaling Big Data Cleansing
Scaling Big Data Cleansing
 
Data Cleaning Process
Data Cleaning ProcessData Cleaning Process
Data Cleaning Process
 
Data Quality Best Practices Nbk Auto May 06 2010
Data Quality Best Practices  Nbk Auto May 06 2010Data Quality Best Practices  Nbk Auto May 06 2010
Data Quality Best Practices Nbk Auto May 06 2010
 
Data-Ed: Best Practices with the Data Management Maturity Model
Data-Ed: Best Practices with the Data Management Maturity ModelData-Ed: Best Practices with the Data Management Maturity Model
Data-Ed: Best Practices with the Data Management Maturity Model
 
Applying Data Quality Best Practices at Big Data Scale
Applying Data Quality Best Practices at Big Data ScaleApplying Data Quality Best Practices at Big Data Scale
Applying Data Quality Best Practices at Big Data Scale
 
Data cleansing
Data cleansingData cleansing
Data cleansing
 

Similar a Best Practices: Data Admin & Data Management

Enterprise Data Governance for Financial Institutions
Enterprise Data Governance for Financial InstitutionsEnterprise Data Governance for Financial Institutions
Enterprise Data Governance for Financial Institutions
Sheldon McCarthy
 
How JCI Prepared a Data Governance Program for Big Data & MDG on HANA
How JCI Prepared a Data Governance Program for Big Data & MDG on HANAHow JCI Prepared a Data Governance Program for Big Data & MDG on HANA
How JCI Prepared a Data Governance Program for Big Data & MDG on HANA
DATUM LLC
 
EDMC_DCAM_-_WORKING_DRAFT_VERSION_0.7.pdf
EDMC_DCAM_-_WORKING_DRAFT_VERSION_0.7.pdfEDMC_DCAM_-_WORKING_DRAFT_VERSION_0.7.pdf
EDMC_DCAM_-_WORKING_DRAFT_VERSION_0.7.pdf
Abhinav195887
 

Similar a Best Practices: Data Admin & Data Management (20)

Planning Data Warehouse
Planning Data WarehousePlanning Data Warehouse
Planning Data Warehouse
 
Data Governance challenges in a major Energy Company
Data Governance challenges in a major Energy CompanyData Governance challenges in a major Energy Company
Data Governance challenges in a major Energy Company
 
Enterprise Data Governance for Financial Institutions
Enterprise Data Governance for Financial InstitutionsEnterprise Data Governance for Financial Institutions
Enterprise Data Governance for Financial Institutions
 
MDM & BI Strategy For Large Enterprises
MDM & BI Strategy For Large EnterprisesMDM & BI Strategy For Large Enterprises
MDM & BI Strategy For Large Enterprises
 
Metadata Strategies
Metadata StrategiesMetadata Strategies
Metadata Strategies
 
These Are The Data You Are Looking For
These Are The Data You Are Looking ForThese Are The Data You Are Looking For
These Are The Data You Are Looking For
 
How JCI Prepared a Data Governance Program for Big Data & MDG on HANA
How JCI Prepared a Data Governance Program for Big Data & MDG on HANAHow JCI Prepared a Data Governance Program for Big Data & MDG on HANA
How JCI Prepared a Data Governance Program for Big Data & MDG on HANA
 
Implementing Agile Data Governance
Implementing Agile Data GovernanceImplementing Agile Data Governance
Implementing Agile Data Governance
 
Database 2 External Schema
Database 2   External SchemaDatabase 2   External Schema
Database 2 External Schema
 
Business intelligence
Business intelligenceBusiness intelligence
Business intelligence
 
11626 Bitt I 2008 Lec 2
11626 Bitt I 2008 Lec 211626 Bitt I 2008 Lec 2
11626 Bitt I 2008 Lec 2
 
A Step-by-Step Guide to Metadata Management
A Step-by-Step Guide to Metadata ManagementA Step-by-Step Guide to Metadata Management
A Step-by-Step Guide to Metadata Management
 
Adopting a Process-Driven Approach to Master Data Management
Adopting a Process-Driven Approach to Master Data ManagementAdopting a Process-Driven Approach to Master Data Management
Adopting a Process-Driven Approach to Master Data Management
 
Master Data Management's Place in the Data Governance Landscape
Master Data Management's Place in the Data Governance Landscape Master Data Management's Place in the Data Governance Landscape
Master Data Management's Place in the Data Governance Landscape
 
AnalytiX DS - Master Deck
AnalytiX DS - Master DeckAnalytiX DS - Master Deck
AnalytiX DS - Master Deck
 
3._DWH_Architecture__Components.ppt
3._DWH_Architecture__Components.ppt3._DWH_Architecture__Components.ppt
3._DWH_Architecture__Components.ppt
 
EDMC_DCAM_-_WORKING_DRAFT_VERSION_0.7.pdf
EDMC_DCAM_-_WORKING_DRAFT_VERSION_0.7.pdfEDMC_DCAM_-_WORKING_DRAFT_VERSION_0.7.pdf
EDMC_DCAM_-_WORKING_DRAFT_VERSION_0.7.pdf
 
The Key Reason Why Your DG Program is Failing
The Key Reason Why Your DG Program is FailingThe Key Reason Why Your DG Program is Failing
The Key Reason Why Your DG Program is Failing
 
Introduction to Master Data Services in SQL Server 2012
Introduction to Master Data Services in SQL Server 2012Introduction to Master Data Services in SQL Server 2012
Introduction to Master Data Services in SQL Server 2012
 
Metadata Repositories in Health Care - Master Data Management Approach to Met...
Metadata Repositories in Health Care - Master Data Management Approach to Met...Metadata Repositories in Health Care - Master Data Management Approach to Met...
Metadata Repositories in Health Care - Master Data Management Approach to Met...
 

Más de Empowered Holdings, LLC

Más de Empowered Holdings, LLC (8)

Introduction To Data Vault - DAMA Oregon 2012
Introduction To Data Vault - DAMA Oregon 2012Introduction To Data Vault - DAMA Oregon 2012
Introduction To Data Vault - DAMA Oregon 2012
 
Présentation data vault et bi v20120508
Présentation data vault et bi v20120508Présentation data vault et bi v20120508
Présentation data vault et bi v20120508
 
IRM UK - 2009: DV Modeling And Methodology
IRM UK - 2009: DV Modeling And MethodologyIRM UK - 2009: DV Modeling And Methodology
IRM UK - 2009: DV Modeling And Methodology
 
Data Vault and DW2.0
Data Vault and DW2.0Data Vault and DW2.0
Data Vault and DW2.0
 
Data vault what's Next: Part 2
Data vault what's Next: Part 2Data vault what's Next: Part 2
Data vault what's Next: Part 2
 
Data vault: What's Next
Data vault: What's NextData vault: What's Next
Data vault: What's Next
 
Operational Data Vault
Operational Data VaultOperational Data Vault
Operational Data Vault
 
Data Vault Overview
Data Vault OverviewData Vault Overview
Data Vault Overview
 

Último

Why Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire businessWhy Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire business
panagenda
 
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers:  A Deep Dive into Serverless Spatial Data and FMECloud Frontiers:  A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Safe Software
 
Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024
Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024
Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024
Victor Rentea
 

Último (20)

Exploring Multimodal Embeddings with Milvus
Exploring Multimodal Embeddings with MilvusExploring Multimodal Embeddings with Milvus
Exploring Multimodal Embeddings with Milvus
 
Elevate Developer Efficiency & build GenAI Application with Amazon Q​
Elevate Developer Efficiency & build GenAI Application with Amazon Q​Elevate Developer Efficiency & build GenAI Application with Amazon Q​
Elevate Developer Efficiency & build GenAI Application with Amazon Q​
 
MS Copilot expands with MS Graph connectors
MS Copilot expands with MS Graph connectorsMS Copilot expands with MS Graph connectors
MS Copilot expands with MS Graph connectors
 
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
 
Why Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire businessWhy Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire business
 
Corporate and higher education May webinar.pptx
Corporate and higher education May webinar.pptxCorporate and higher education May webinar.pptx
Corporate and higher education May webinar.pptx
 
Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...
Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...
Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...
 
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers:  A Deep Dive into Serverless Spatial Data and FMECloud Frontiers:  A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
 
Apidays New York 2024 - APIs in 2030: The Risk of Technological Sleepwalk by ...
Apidays New York 2024 - APIs in 2030: The Risk of Technological Sleepwalk by ...Apidays New York 2024 - APIs in 2030: The Risk of Technological Sleepwalk by ...
Apidays New York 2024 - APIs in 2030: The Risk of Technological Sleepwalk by ...
 
Apidays New York 2024 - The value of a flexible API Management solution for O...
Apidays New York 2024 - The value of a flexible API Management solution for O...Apidays New York 2024 - The value of a flexible API Management solution for O...
Apidays New York 2024 - The value of a flexible API Management solution for O...
 
Understanding the FAA Part 107 License ..
Understanding the FAA Part 107 License ..Understanding the FAA Part 107 License ..
Understanding the FAA Part 107 License ..
 
Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024
Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024
Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024
 
presentation ICT roal in 21st century education
presentation ICT roal in 21st century educationpresentation ICT roal in 21st century education
presentation ICT roal in 21st century education
 
DBX First Quarter 2024 Investor Presentation
DBX First Quarter 2024 Investor PresentationDBX First Quarter 2024 Investor Presentation
DBX First Quarter 2024 Investor Presentation
 
Strategies for Landing an Oracle DBA Job as a Fresher
Strategies for Landing an Oracle DBA Job as a FresherStrategies for Landing an Oracle DBA Job as a Fresher
Strategies for Landing an Oracle DBA Job as a Fresher
 
Vector Search -An Introduction in Oracle Database 23ai.pptx
Vector Search -An Introduction in Oracle Database 23ai.pptxVector Search -An Introduction in Oracle Database 23ai.pptx
Vector Search -An Introduction in Oracle Database 23ai.pptx
 
Six Myths about Ontologies: The Basics of Formal Ontology
Six Myths about Ontologies: The Basics of Formal OntologySix Myths about Ontologies: The Basics of Formal Ontology
Six Myths about Ontologies: The Basics of Formal Ontology
 
CNIC Information System with Pakdata Cf In Pakistan
CNIC Information System with Pakdata Cf In PakistanCNIC Information System with Pakdata Cf In Pakistan
CNIC Information System with Pakdata Cf In Pakistan
 
ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke
ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemkeProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke
ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke
 
Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...
Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...
Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...
 

Best Practices: Data Admin & Data Management

  • 1. Best Practices: Data Administration and Quality Daniel Linstedt, all rights reserved, http://LearnDataVault.com
  • 2.
  • 3.
  • 5. What is Data Administration? “ What do we mean by that in the case of data administration? We mean that DA must get out of the design review committee mentality and substitute something more value-added and flexible. It must recognize that systems tend to grow organically, and be a part of that process, rather than an instiller of order upon it.”  Eric Rawlins, 1995 Originally Published by: Database Research Group, Inc http://www.well.com/user/woodman/organic.html
  • 6.
  • 7. Cross-Organization Roles and Responsibilities Business ( Owner View) Data Steward Discipline Authority Business Process Manager Data Usage Contact Data Manager Data Modeler DA is a ROLE and typically involves more than one person in order to achieve success. Logical (Designer View) Data Administrator Physical ( Builder View) Database Administrator
  • 8.
  • 9.
  • 10. Defining Data Administration Issues Top 4 Examples
  • 11.
  • 12.
  • 13.
  • 14.
  • 16.
  • 17.
  • 18.
  • 19.
  • 20. DA: Architecting Data Governance Business Rules & IQ EDW Source Systems Non Compliant Data Marts Business Rules & IQ EDW Source Systems Data Marts Compliant Hard Business Rules Soft Business Rules & IQ Shift to process AFTER the EDW Hard Business Rules Still process Before the EDW
  • 21.
  • 22.
  • 23.
  • 24. DA: Tracking Errors – KPIs at Work
  • 25.
  • 26. Metadata Administration Lifecycle Identify New Metadata Integrate With Master Metadata Repository Edit and Manage Master Metadata (Provide Business Users with Web Interface) Stitch Master Metadata Together Compare Master Metadata With Business Process And Objectives Export Master Metadata or Deploy via SOA With Master Data Set Derived from Meta Integration Metadata Lifecycle
  • 27.
  • 28.
  • 30.
  • 31.
  • 32.
  • 33.
  • 34. Thank you Contact us today: Dan Linstedt [email_address] http://LearnDataVault.com

Notas del editor

  1. The purpose of this slide show is to present and discuss the role of data administration in the data integration world. Here we define some of the business and technical problems that DA’s face on a daily basis, then we move on to discuss the types of activities that a DA will under-take in an enterprise level initiative. Please bear in mind, that the DA is a role, and may not end-up being just a single individual, but rather a group of individuals, some of whom are directly responsible for Data Management as well.
  2. In this section we define different DA roles, issues, and conceptual notions. We discuss the DA role from a 20,000 foot level where the enterprise “see’s” data administrators, and begins to understand what they do. The role of the DA ranges from monitoring business user meetings to over-seeing the design of data flow through business processes. Business Process flow has a large impact on the world of the DA and what they need to be capable of achieving. They need to work across multiple groups in order to achieve an enterprise vision of the data assets and models that will serve the enterprise.
  3. http://www.cio.gov.bc.ca/other/daf/DMRolesRespV1.pdf
  4. http://www.cio.gov.bc.ca/other/daf/DMRolesRespV1.pdf
  5. http://www.educause.edu/ir/library/text/CEM9047.txt
  6. Data Must Be: Auditable, Traceable, Stored in the granular format it arrived in, A “statement-of-fact” Business Rules must move to the output side of the equation. Data can be integrated by the same semantic grain, but cannot be altered.
  7. The Data Administrator is responsible for identifying auditable or audited sources of data. The DA will be responsible for ensuring which data sets can and should be utilized to load enterprise data warehouses. The DA will set policies and procedures for measuring, auditing, and assessing the quality of information flowing to and from the source systems.
  8. The Data Administrator is responsible for assigning or classifying different groups of errors, what will make the data set or break the data set. They are also responsible for the integrity of the data set, and ensuring that the data set matches the requirements set forth by the business users.
  9. The Data Administrator might use a live chart like this one to examine the errors and the occurrences of errors over time. The DA will be responsible for the quality of the data, as it relates to the business metrics put forward. The DA will be responsible for maintaining the logical models, and the business processes – and if the error count is too high for a specific area of expertise, then the Data Manager must be notified, and corrective action must be taken.
  10. Organic Data Administration, http://www.well.com/user/woodman/organic.html