SlideShare a Scribd company logo
1 of 6
http://informationaction.blogspot.com
Tw: @Alan_D_Duncan
Information Strategy | Data Governance | Analytics | Better Business Outcomes
Example Data Specifications &
Information Requirements Framework
INFORMATION SOLUTION OUTLINE &
HIGH-LEVEL REQUIREMENTS
TEMPLATE (Project Mandate)
Alan D. Duncan
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Information Solution Outline & High-Level Requirements
http://informationaction.blogspot.com
Tw: @Alan_D_Duncan
Information Strategy | Data Governance | Analytics | Better Business Outcomes
1 Purpose
This is a document template for capturing the overall high-level business requirements and
expectations for business information solutions with a significant impact on or requirement for data.
(cf. the “Project Mandate” document in PRINCE2).
At this stage, expectations are captured in general terms and while additional detail is of value, the
main aims are to ensure that the overall expectations and goals of the solution are captured in terms
that are meaningful to the business community. For guidance, the information solution will be outlined
at a level of understanding sufficient to support the outline Business Case.
This template forms part of example data specification & information requirements framework. The
framework offers a set of outline principles, standards and guidelines to describe and clarify the
semantic meaning of data terms in support of an Information Requirements Management process.
(See the Framework Overview for further details.)
Information Solution Outline & High-Level Requirements
http://informationaction.blogspot.com
Tw: @Alan_D_Duncan
Information Strategy | Data Governance | Analytics | Better Business Outcomes
2 Information Solution Outline & High-Level Requirements
(Project Mandate)
SOLUTION
REQUIREMENT:
The area of information & data management under review and the overall
expectations for the solution.
SOLUTION
OBJECTIVES:
Purpose or outcome required.
Why is the change to information (or associated systems) needed? What
business outcomes are intended?
SOLUTION
DESCRIPTION:
What data or information is required? How is the solution intended to
function?
What is the scope of changes to the information within the solution?
Where does this apply?
What will happen to the data? What will change?
SOLUTION DATA
OWNER:
Who is requesting the information solution?
Who is accountable for the solutions completion and outcomes of the
requirements for any resulting changes to the business information?
DATA STEWARD(s): Who is responsible for the execution of the changes to the data?
SYSTEMS
IMPACTED
Which information systems or architectural components will be involved
when a change is being made to the definition or consumption of this data
e.g.:
 SOA
 DWH
 MDM
 Application
 Project
SOLUTION
DELIVERY
METHODS & DATA
UPDATE
PROCESSES
How is the solution to be implemented?
What methodologies are to be applied? (e.g. MIKE2.0, DMBOK, SCRUM)
What changes to the data will be applied?
What steps are involved?
EXCEPTIONS,
CONSTRAINTS &
EXCLUSIONS
Are there any exceptions or constraints that limit the scope of data delivery?
Is there are any data that explicitly won’t be included?
DATA VERIFICATION
MODE
How will the solution changes and impacts on the data be verified as
effective and complete?
 Audit: after-the-fact review for compliance.
 Self-Certification: team compiles the evidence to demonstrate that
the checkpoint has been applied
 Intervention: Requirement for Data Governance Unit participation
will be proactively flagged
SOLUTION
DOCUMENTATION
INPUTS
What information or deliverables will be used as an input to the solution
delivery process?
E,g. requirements documents, draft design documents, Business Case,
options papers etc. etc.
SOLUTION
DOCUMENTATION
OUTPUTS
What information or deliverables will be created as an output of the
process?
E,g. minutes, design documents, updates to metadata repository etc.
Information Solution Outline & High-Level Requirements
http://informationaction.blogspot.com
Tw: @Alan_D_Duncan
Information Strategy | Data Governance | Analytics | Better Business Outcomes
RELEVANT
CONTROL
ARTEFACTS
Which Data Governance and Information Management standards,
guidelines, methods and constraints need to be applied? E.g.
 Update the Enterprise Conceptual Model
 Update the Enterprise Logical Model
 Update the solution Physical Design
What other principles, guidelines and reference materials could be useful?
(e.g. legislation, regulation, policies and standards)
RESOURCES &
ROLES:
Who is to participate in making the required changes to the data and
systems?
In what capacity/role will they contribute?
SOLUTION DATA DEFINITIONS:
Capture any known business & technical metadata
Are any data lineage impacts identified?
Define any updates than need to be applied to Business Data Network & Business Glossary
IDENTIFIED DEPENDENCIES
Pre-requisites etc. that need to be satisfied before the solution can be implemented.
RELATED BUSINESS PROCESSES
Identify existing business processes impacted by the delivery of the new data solution.
OTHER INFORMATION & NOTES
Information Solution Outline & High-Level Requirements
http://informationaction.blogspot.com
Tw: @Alan_D_Duncan
Information Strategy | Data Governance | Analytics | Better Business Outcomes
About the author
Alan D. Duncan is an evangelist for information and analytics as
enablers of better business outcomes, and a member of the
Advisory Board for QFire Software.
An executive-level leader in the field of Information and Data
Management Strategy, Governance and Business Analytics, he
has over 20 years of international business experience, working
with blue-chip companies in a range of industry sectors. Alan
was named by Information-Management.com in their 2012 list of
“Top 12 Data Governance gurus you should be following on
Twitter”.
Twitter: @Alan_D_Duncan
Blog: http://informationaction.blogspot.com.au/
Information Solution Outline & High-Level Requirements
http://informationaction.blogspot.com
Tw: @Alan_D_Duncan
Information Strategy | Data Governance | Analytics | Better Business Outcomes
Intellectual curiosity
Skeptical scrutiny
Critical thinking
http://www.informationaction.blogspot.com.au/
@Alan_D_Duncan
http://www.linkedin.com/in/alandduncan

More Related Content

What's hot

Data Mesh in Azure using Cloud Scale Analytics (WAF)
Data Mesh in Azure using Cloud Scale Analytics (WAF)Data Mesh in Azure using Cloud Scale Analytics (WAF)
Data Mesh in Azure using Cloud Scale Analytics (WAF)Nathan Bijnens
 
DAS Slides: Master Data Management – Aligning Data, Process, and Governance
DAS Slides: Master Data Management – Aligning Data, Process, and GovernanceDAS Slides: Master Data Management – Aligning Data, Process, and Governance
DAS Slides: Master Data Management – Aligning Data, Process, and GovernanceDATAVERSITY
 
Activate Data Governance Using the Data Catalog
Activate Data Governance Using the Data CatalogActivate Data Governance Using the Data Catalog
Activate Data Governance Using the Data CatalogDATAVERSITY
 
Building an Effective Data Warehouse Architecture
Building an Effective Data Warehouse ArchitectureBuilding an Effective Data Warehouse Architecture
Building an Effective Data Warehouse ArchitectureJames Serra
 
State of Data Governance in 2021
State of Data Governance in 2021State of Data Governance in 2021
State of Data Governance in 2021DATAVERSITY
 
Logical Data Fabric: Architectural Components
Logical Data Fabric: Architectural ComponentsLogical Data Fabric: Architectural Components
Logical Data Fabric: Architectural ComponentsDenodo
 
Data Modeling Best Practices - Business & Technical Approaches
Data Modeling Best Practices - Business & Technical ApproachesData Modeling Best Practices - Business & Technical Approaches
Data Modeling Best Practices - Business & Technical ApproachesDATAVERSITY
 
Emerging Trends in Data Architecture – What’s the Next Big Thing?
Emerging Trends in Data Architecture – What’s the Next Big Thing?Emerging Trends in Data Architecture – What’s the Next Big Thing?
Emerging Trends in Data Architecture – What’s the Next Big Thing?DATAVERSITY
 
Data Engineer's Lunch #81: Reverse ETL Tools for Modern Data Platforms
Data Engineer's Lunch #81: Reverse ETL Tools for Modern Data PlatformsData Engineer's Lunch #81: Reverse ETL Tools for Modern Data Platforms
Data Engineer's Lunch #81: Reverse ETL Tools for Modern Data PlatformsAnant Corporation
 
LDM Webinar: Data Modeling & Business Intelligence
LDM Webinar: Data Modeling & Business IntelligenceLDM Webinar: Data Modeling & Business Intelligence
LDM Webinar: Data Modeling & Business IntelligenceDATAVERSITY
 
DAS Slides: Metadata Management From Technical Architecture & Business Techni...
DAS Slides: Metadata Management From Technical Architecture & Business Techni...DAS Slides: Metadata Management From Technical Architecture & Business Techni...
DAS Slides: Metadata Management From Technical Architecture & Business Techni...DATAVERSITY
 
Selecting Data Management Tools - A practical approach
Selecting Data Management Tools - A practical approachSelecting Data Management Tools - A practical approach
Selecting Data Management Tools - A practical approachChristopher Bradley
 
06. Transformation Logic Template (Source to Target)
06. Transformation Logic Template (Source to Target)06. Transformation Logic Template (Source to Target)
06. Transformation Logic Template (Source to Target)Alan D. Duncan
 
DataMinds 2022 Azure Purview Erwin de Kreuk
DataMinds 2022 Azure Purview Erwin de KreukDataMinds 2022 Azure Purview Erwin de Kreuk
DataMinds 2022 Azure Purview Erwin de KreukErwin de Kreuk
 
Power apps portals are now generally available
Power apps portals are now generally availablePower apps portals are now generally available
Power apps portals are now generally availableConcetto Labs
 
Glossaries, Dictionaries, and Catalogs Result in Data Governance
Glossaries, Dictionaries, and Catalogs Result in Data GovernanceGlossaries, Dictionaries, and Catalogs Result in Data Governance
Glossaries, Dictionaries, and Catalogs Result in Data GovernanceDATAVERSITY
 
Data-Ed: Data Architecture Requirements
Data-Ed: Data Architecture Requirements Data-Ed: Data Architecture Requirements
Data-Ed: Data Architecture Requirements Data Blueprint
 

What's hot (20)

Data Mesh in Azure using Cloud Scale Analytics (WAF)
Data Mesh in Azure using Cloud Scale Analytics (WAF)Data Mesh in Azure using Cloud Scale Analytics (WAF)
Data Mesh in Azure using Cloud Scale Analytics (WAF)
 
DAS Slides: Master Data Management – Aligning Data, Process, and Governance
DAS Slides: Master Data Management – Aligning Data, Process, and GovernanceDAS Slides: Master Data Management – Aligning Data, Process, and Governance
DAS Slides: Master Data Management – Aligning Data, Process, and Governance
 
Activate Data Governance Using the Data Catalog
Activate Data Governance Using the Data CatalogActivate Data Governance Using the Data Catalog
Activate Data Governance Using the Data Catalog
 
Building an Effective Data Warehouse Architecture
Building an Effective Data Warehouse ArchitectureBuilding an Effective Data Warehouse Architecture
Building an Effective Data Warehouse Architecture
 
State of Data Governance in 2021
State of Data Governance in 2021State of Data Governance in 2021
State of Data Governance in 2021
 
Training
TrainingTraining
Training
 
Oracle Analytics Cloud
Oracle Analytics CloudOracle Analytics Cloud
Oracle Analytics Cloud
 
Logical Data Fabric: Architectural Components
Logical Data Fabric: Architectural ComponentsLogical Data Fabric: Architectural Components
Logical Data Fabric: Architectural Components
 
Data Modeling Best Practices - Business & Technical Approaches
Data Modeling Best Practices - Business & Technical ApproachesData Modeling Best Practices - Business & Technical Approaches
Data Modeling Best Practices - Business & Technical Approaches
 
Emerging Trends in Data Architecture – What’s the Next Big Thing?
Emerging Trends in Data Architecture – What’s the Next Big Thing?Emerging Trends in Data Architecture – What’s the Next Big Thing?
Emerging Trends in Data Architecture – What’s the Next Big Thing?
 
Data Engineer's Lunch #81: Reverse ETL Tools for Modern Data Platforms
Data Engineer's Lunch #81: Reverse ETL Tools for Modern Data PlatformsData Engineer's Lunch #81: Reverse ETL Tools for Modern Data Platforms
Data Engineer's Lunch #81: Reverse ETL Tools for Modern Data Platforms
 
LDM Webinar: Data Modeling & Business Intelligence
LDM Webinar: Data Modeling & Business IntelligenceLDM Webinar: Data Modeling & Business Intelligence
LDM Webinar: Data Modeling & Business Intelligence
 
DAS Slides: Metadata Management From Technical Architecture & Business Techni...
DAS Slides: Metadata Management From Technical Architecture & Business Techni...DAS Slides: Metadata Management From Technical Architecture & Business Techni...
DAS Slides: Metadata Management From Technical Architecture & Business Techni...
 
BI Business Requirements - A Framework For Business Analysts
BI Business Requirements -  A Framework For Business AnalystsBI Business Requirements -  A Framework For Business Analysts
BI Business Requirements - A Framework For Business Analysts
 
Selecting Data Management Tools - A practical approach
Selecting Data Management Tools - A practical approachSelecting Data Management Tools - A practical approach
Selecting Data Management Tools - A practical approach
 
06. Transformation Logic Template (Source to Target)
06. Transformation Logic Template (Source to Target)06. Transformation Logic Template (Source to Target)
06. Transformation Logic Template (Source to Target)
 
DataMinds 2022 Azure Purview Erwin de Kreuk
DataMinds 2022 Azure Purview Erwin de KreukDataMinds 2022 Azure Purview Erwin de Kreuk
DataMinds 2022 Azure Purview Erwin de Kreuk
 
Power apps portals are now generally available
Power apps portals are now generally availablePower apps portals are now generally available
Power apps portals are now generally available
 
Glossaries, Dictionaries, and Catalogs Result in Data Governance
Glossaries, Dictionaries, and Catalogs Result in Data GovernanceGlossaries, Dictionaries, and Catalogs Result in Data Governance
Glossaries, Dictionaries, and Catalogs Result in Data Governance
 
Data-Ed: Data Architecture Requirements
Data-Ed: Data Architecture Requirements Data-Ed: Data Architecture Requirements
Data-Ed: Data Architecture Requirements
 

Viewers also liked

Example data specifications and info requirements framework OVERVIEW
Example data specifications and info requirements framework OVERVIEWExample data specifications and info requirements framework OVERVIEW
Example data specifications and info requirements framework OVERVIEWAlan D. Duncan
 
Gathering And Documenting Your Bi Business Requirements
Gathering And Documenting Your Bi Business RequirementsGathering And Documenting Your Bi Business Requirements
Gathering And Documenting Your Bi Business RequirementsWynyard Group
 
Igqie14 analytics and ethics 20141107
Igqie14   analytics and ethics 20141107Igqie14   analytics and ethics 20141107
Igqie14 analytics and ethics 20141107Alan D. Duncan
 
The one question you must never ask!" (Information Requirements Gathering for...
The one question you must never ask!" (Information Requirements Gathering for...The one question you must never ask!" (Information Requirements Gathering for...
The one question you must never ask!" (Information Requirements Gathering for...Alan D. Duncan
 
WHITE PAPER: Distributed Data Quality
WHITE PAPER: Distributed Data QualityWHITE PAPER: Distributed Data Quality
WHITE PAPER: Distributed Data QualityAlan D. Duncan
 
Managing for Effective Data Governance: workshop for DQ Asia Pacific Congress...
Managing for Effective Data Governance: workshop for DQ Asia Pacific Congress...Managing for Effective Data Governance: workshop for DQ Asia Pacific Congress...
Managing for Effective Data Governance: workshop for DQ Asia Pacific Congress...Alan D. Duncan
 
Data Quality in Data Warehouse and Business Intelligence Environments - Disc...
Data Quality in  Data Warehouse and Business Intelligence Environments - Disc...Data Quality in  Data Warehouse and Business Intelligence Environments - Disc...
Data Quality in Data Warehouse and Business Intelligence Environments - Disc...Alan D. Duncan
 
Angelo's case study
Angelo's case studyAngelo's case study
Angelo's case studylionsprey
 
An introduction to social network data
An introduction to social network dataAn introduction to social network data
An introduction to social network dataDavid Walker
 
Building a data warehouse of call data records
Building a data warehouse of call data recordsBuilding a data warehouse of call data records
Building a data warehouse of call data recordsDavid Walker
 
The ABC of Data Governance: driving Information Excellence
The ABC of Data Governance: driving Information ExcellenceThe ABC of Data Governance: driving Information Excellence
The ABC of Data Governance: driving Information ExcellenceAlan D. Duncan
 
Implementing Netezza Spatial
Implementing Netezza SpatialImplementing Netezza Spatial
Implementing Netezza SpatialDavid Walker
 
BI SaaS & Cloud Strategies for Telcos
BI SaaS & Cloud Strategies for TelcosBI SaaS & Cloud Strategies for Telcos
BI SaaS & Cloud Strategies for TelcosDavid Walker
 
Building an analytical platform
Building an analytical platformBuilding an analytical platform
Building an analytical platformDavid Walker
 
Data Driven Insurance Underwriting (Dutch Language Version)
Data Driven Insurance Underwriting (Dutch Language Version)Data Driven Insurance Underwriting (Dutch Language Version)
Data Driven Insurance Underwriting (Dutch Language Version)David Walker
 
LL Higher Ed BI 2014 Key BI Market Trends 20140513a
LL Higher Ed BI 2014 Key BI Market Trends 20140513aLL Higher Ed BI 2014 Key BI Market Trends 20140513a
LL Higher Ed BI 2014 Key BI Market Trends 20140513aAlan D. Duncan
 
Basics of Microsoft Business Intelligence and Data Integration Techniques
Basics of Microsoft Business Intelligence and Data Integration TechniquesBasics of Microsoft Business Intelligence and Data Integration Techniques
Basics of Microsoft Business Intelligence and Data Integration TechniquesValmik Potbhare
 
Data Driven Insurance Underwriting
Data Driven Insurance UnderwritingData Driven Insurance Underwriting
Data Driven Insurance UnderwritingDavid Walker
 
Data warehousing change in a challenging environment
Data warehousing change in a challenging environmentData warehousing change in a challenging environment
Data warehousing change in a challenging environmentDavid Walker
 

Viewers also liked (19)

Example data specifications and info requirements framework OVERVIEW
Example data specifications and info requirements framework OVERVIEWExample data specifications and info requirements framework OVERVIEW
Example data specifications and info requirements framework OVERVIEW
 
Gathering And Documenting Your Bi Business Requirements
Gathering And Documenting Your Bi Business RequirementsGathering And Documenting Your Bi Business Requirements
Gathering And Documenting Your Bi Business Requirements
 
Igqie14 analytics and ethics 20141107
Igqie14   analytics and ethics 20141107Igqie14   analytics and ethics 20141107
Igqie14 analytics and ethics 20141107
 
The one question you must never ask!" (Information Requirements Gathering for...
The one question you must never ask!" (Information Requirements Gathering for...The one question you must never ask!" (Information Requirements Gathering for...
The one question you must never ask!" (Information Requirements Gathering for...
 
WHITE PAPER: Distributed Data Quality
WHITE PAPER: Distributed Data QualityWHITE PAPER: Distributed Data Quality
WHITE PAPER: Distributed Data Quality
 
Managing for Effective Data Governance: workshop for DQ Asia Pacific Congress...
Managing for Effective Data Governance: workshop for DQ Asia Pacific Congress...Managing for Effective Data Governance: workshop for DQ Asia Pacific Congress...
Managing for Effective Data Governance: workshop for DQ Asia Pacific Congress...
 
Data Quality in Data Warehouse and Business Intelligence Environments - Disc...
Data Quality in  Data Warehouse and Business Intelligence Environments - Disc...Data Quality in  Data Warehouse and Business Intelligence Environments - Disc...
Data Quality in Data Warehouse and Business Intelligence Environments - Disc...
 
Angelo's case study
Angelo's case studyAngelo's case study
Angelo's case study
 
An introduction to social network data
An introduction to social network dataAn introduction to social network data
An introduction to social network data
 
Building a data warehouse of call data records
Building a data warehouse of call data recordsBuilding a data warehouse of call data records
Building a data warehouse of call data records
 
The ABC of Data Governance: driving Information Excellence
The ABC of Data Governance: driving Information ExcellenceThe ABC of Data Governance: driving Information Excellence
The ABC of Data Governance: driving Information Excellence
 
Implementing Netezza Spatial
Implementing Netezza SpatialImplementing Netezza Spatial
Implementing Netezza Spatial
 
BI SaaS & Cloud Strategies for Telcos
BI SaaS & Cloud Strategies for TelcosBI SaaS & Cloud Strategies for Telcos
BI SaaS & Cloud Strategies for Telcos
 
Building an analytical platform
Building an analytical platformBuilding an analytical platform
Building an analytical platform
 
Data Driven Insurance Underwriting (Dutch Language Version)
Data Driven Insurance Underwriting (Dutch Language Version)Data Driven Insurance Underwriting (Dutch Language Version)
Data Driven Insurance Underwriting (Dutch Language Version)
 
LL Higher Ed BI 2014 Key BI Market Trends 20140513a
LL Higher Ed BI 2014 Key BI Market Trends 20140513aLL Higher Ed BI 2014 Key BI Market Trends 20140513a
LL Higher Ed BI 2014 Key BI Market Trends 20140513a
 
Basics of Microsoft Business Intelligence and Data Integration Techniques
Basics of Microsoft Business Intelligence and Data Integration TechniquesBasics of Microsoft Business Intelligence and Data Integration Techniques
Basics of Microsoft Business Intelligence and Data Integration Techniques
 
Data Driven Insurance Underwriting
Data Driven Insurance UnderwritingData Driven Insurance Underwriting
Data Driven Insurance Underwriting
 
Data warehousing change in a challenging environment
Data warehousing change in a challenging environmentData warehousing change in a challenging environment
Data warehousing change in a challenging environment
 

Similar to 02. Information solution outline template

Workable Enteprise Data Governance
Workable Enteprise Data GovernanceWorkable Enteprise Data Governance
Workable Enteprise Data GovernanceBhavendra Chavan
 
Enterprise Data Governance for Financial Institutions
Enterprise Data Governance for Financial InstitutionsEnterprise Data Governance for Financial Institutions
Enterprise Data Governance for Financial InstitutionsSheldon McCarthy
 
Best Practices: Data Admin & Data Management
Best Practices: Data Admin & Data ManagementBest Practices: Data Admin & Data Management
Best Practices: Data Admin & Data ManagementEmpowered Holdings, LLC
 
Implementing Agile Data Governance
Implementing Agile Data GovernanceImplementing Agile Data Governance
Implementing Agile Data GovernanceTami Flowers
 
Data Quality Best Practices
Data Quality Best PracticesData Quality Best Practices
Data Quality Best PracticesDATAVERSITY
 
413451520-8-Steps-Successful-Enterprise-Data-Manag.pdf
413451520-8-Steps-Successful-Enterprise-Data-Manag.pdf413451520-8-Steps-Successful-Enterprise-Data-Manag.pdf
413451520-8-Steps-Successful-Enterprise-Data-Manag.pdfIsmailCassiem
 
DGIQ 2013 Learned and Applied Concepts
DGIQ 2013 Learned and Applied Concepts DGIQ 2013 Learned and Applied Concepts
DGIQ 2013 Learned and Applied Concepts Angela Boyd
 
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 CCG
 
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 CompanyChristopher Bradley
 
The Business Value of Metadata for Data Governance
The Business Value of Metadata for Data GovernanceThe Business Value of Metadata for Data Governance
The Business Value of Metadata for Data GovernanceRoland Bullivant
 
Business Analyst_PennonSoft
Business Analyst_PennonSoftBusiness Analyst_PennonSoft
Business Analyst_PennonSoftPennonSoft
 
Supply Chain and EA abridged
Supply Chain and EA abridgedSupply Chain and EA abridged
Supply Chain and EA abridgedRichard Freggi
 
AI&BigData Lab 2016. Сергей Шельпук: Методология Data Science проектов
AI&BigData Lab 2016. Сергей Шельпук: Методология Data Science проектовAI&BigData Lab 2016. Сергей Шельпук: Методология Data Science проектов
AI&BigData Lab 2016. Сергей Шельпук: Методология Data Science проектовGeeksLab Odessa
 
CRISP-DM: a data science project methodology
CRISP-DM: a data science project methodologyCRISP-DM: a data science project methodology
CRISP-DM: a data science project methodologySergey Shelpuk
 
Big Data for Project and Program Managers
Big Data for Project and Program ManagersBig Data for Project and Program Managers
Big Data for Project and Program ManagersTonex
 
Optimizing Solution Value – Dynamic Data Quality, Governance, and MDM
Optimizing Solution Value – Dynamic Data Quality, Governance, and MDMOptimizing Solution Value – Dynamic Data Quality, Governance, and MDM
Optimizing Solution Value – Dynamic Data Quality, Governance, and MDMPrecisely
 
Data Integrity: From speed dating to lifelong partnership
Data Integrity: From speed dating to lifelong partnershipData Integrity: From speed dating to lifelong partnership
Data Integrity: From speed dating to lifelong partnershipPrecisely
 

Similar to 02. Information solution outline template (20)

Transformação Digital de TI com EA
Transformação Digital de TI com EATransformação Digital de TI com EA
Transformação Digital de TI com EA
 
Workable Enteprise Data Governance
Workable Enteprise Data GovernanceWorkable Enteprise Data Governance
Workable Enteprise Data Governance
 
Enterprise Data Governance for Financial Institutions
Enterprise Data Governance for Financial InstitutionsEnterprise Data Governance for Financial Institutions
Enterprise Data Governance for Financial Institutions
 
Best Practices: Data Admin & Data Management
Best Practices: Data Admin & Data ManagementBest Practices: Data Admin & Data Management
Best Practices: Data Admin & Data Management
 
Implementing Agile Data Governance
Implementing Agile Data GovernanceImplementing Agile Data Governance
Implementing Agile Data Governance
 
Data Quality Best Practices
Data Quality Best PracticesData Quality Best Practices
Data Quality Best Practices
 
413451520-8-Steps-Successful-Enterprise-Data-Manag.pdf
413451520-8-Steps-Successful-Enterprise-Data-Manag.pdf413451520-8-Steps-Successful-Enterprise-Data-Manag.pdf
413451520-8-Steps-Successful-Enterprise-Data-Manag.pdf
 
DGIQ 2013 Learned and Applied Concepts
DGIQ 2013 Learned and Applied Concepts DGIQ 2013 Learned and Applied Concepts
DGIQ 2013 Learned and Applied Concepts
 
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
 
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
 
The Business Value of Metadata for Data Governance
The Business Value of Metadata for Data GovernanceThe Business Value of Metadata for Data Governance
The Business Value of Metadata for Data Governance
 
Planning Data Warehouse
Planning Data WarehousePlanning Data Warehouse
Planning Data Warehouse
 
Business Analyst_PennonSoft
Business Analyst_PennonSoftBusiness Analyst_PennonSoft
Business Analyst_PennonSoft
 
Supply Chain and EA abridged
Supply Chain and EA abridgedSupply Chain and EA abridged
Supply Chain and EA abridged
 
AI&BigData Lab 2016. Сергей Шельпук: Методология Data Science проектов
AI&BigData Lab 2016. Сергей Шельпук: Методология Data Science проектовAI&BigData Lab 2016. Сергей Шельпук: Методология Data Science проектов
AI&BigData Lab 2016. Сергей Шельпук: Методология Data Science проектов
 
CRISP-DM: a data science project methodology
CRISP-DM: a data science project methodologyCRISP-DM: a data science project methodology
CRISP-DM: a data science project methodology
 
Big Data for Project and Program Managers
Big Data for Project and Program ManagersBig Data for Project and Program Managers
Big Data for Project and Program Managers
 
Optimizing Solution Value – Dynamic Data Quality, Governance, and MDM
Optimizing Solution Value – Dynamic Data Quality, Governance, and MDMOptimizing Solution Value – Dynamic Data Quality, Governance, and MDM
Optimizing Solution Value – Dynamic Data Quality, Governance, and MDM
 
DG - general intro ENG
DG - general intro ENGDG - general intro ENG
DG - general intro ENG
 
Data Integrity: From speed dating to lifelong partnership
Data Integrity: From speed dating to lifelong partnershipData Integrity: From speed dating to lifelong partnership
Data Integrity: From speed dating to lifelong partnership
 

Recently uploaded

Consent & Privacy Signals on Google *Pixels* - MeasureCamp Amsterdam 2024
Consent & Privacy Signals on Google *Pixels* - MeasureCamp Amsterdam 2024Consent & Privacy Signals on Google *Pixels* - MeasureCamp Amsterdam 2024
Consent & Privacy Signals on Google *Pixels* - MeasureCamp Amsterdam 2024thyngster
 
Predictive Analysis for Loan Default Presentation : Data Analysis Project PPT
Predictive Analysis for Loan Default  Presentation : Data Analysis Project PPTPredictive Analysis for Loan Default  Presentation : Data Analysis Project PPT
Predictive Analysis for Loan Default Presentation : Data Analysis Project PPTBoston Institute of Analytics
 
DBA Basics: Getting Started with Performance Tuning.pdf
DBA Basics: Getting Started with Performance Tuning.pdfDBA Basics: Getting Started with Performance Tuning.pdf
DBA Basics: Getting Started with Performance Tuning.pdfJohn Sterrett
 
Biometric Authentication: The Evolution, Applications, Benefits and Challenge...
Biometric Authentication: The Evolution, Applications, Benefits and Challenge...Biometric Authentication: The Evolution, Applications, Benefits and Challenge...
Biometric Authentication: The Evolution, Applications, Benefits and Challenge...GQ Research
 
INTERNSHIP ON PURBASHA COMPOSITE TEX LTD
INTERNSHIP ON PURBASHA COMPOSITE TEX LTDINTERNSHIP ON PURBASHA COMPOSITE TEX LTD
INTERNSHIP ON PURBASHA COMPOSITE TEX LTDRafezzaman
 
Easter Eggs From Star Wars and in cars 1 and 2
Easter Eggs From Star Wars and in cars 1 and 2Easter Eggs From Star Wars and in cars 1 and 2
Easter Eggs From Star Wars and in cars 1 and 217djon017
 
Predicting Salary Using Data Science: A Comprehensive Analysis.pdf
Predicting Salary Using Data Science: A Comprehensive Analysis.pdfPredicting Salary Using Data Science: A Comprehensive Analysis.pdf
Predicting Salary Using Data Science: A Comprehensive Analysis.pdfBoston Institute of Analytics
 
Heart Disease Classification Report: A Data Analysis Project
Heart Disease Classification Report: A Data Analysis ProjectHeart Disease Classification Report: A Data Analysis Project
Heart Disease Classification Report: A Data Analysis ProjectBoston Institute of Analytics
 
Data Factory in Microsoft Fabric (MsBIP #82)
Data Factory in Microsoft Fabric (MsBIP #82)Data Factory in Microsoft Fabric (MsBIP #82)
Data Factory in Microsoft Fabric (MsBIP #82)Cathrine Wilhelmsen
 
Multiple time frame trading analysis -brianshannon.pdf
Multiple time frame trading analysis -brianshannon.pdfMultiple time frame trading analysis -brianshannon.pdf
Multiple time frame trading analysis -brianshannon.pdfchwongval
 
ASML's Taxonomy Adventure by Daniel Canter
ASML's Taxonomy Adventure by Daniel CanterASML's Taxonomy Adventure by Daniel Canter
ASML's Taxonomy Adventure by Daniel Cantervoginip
 
detection and classification of knee osteoarthritis.pptx
detection and classification of knee osteoarthritis.pptxdetection and classification of knee osteoarthritis.pptx
detection and classification of knee osteoarthritis.pptxAleenaJamil4
 
NO1 Certified Black Magic Specialist Expert Amil baba in Lahore Islamabad Raw...
NO1 Certified Black Magic Specialist Expert Amil baba in Lahore Islamabad Raw...NO1 Certified Black Magic Specialist Expert Amil baba in Lahore Islamabad Raw...
NO1 Certified Black Magic Specialist Expert Amil baba in Lahore Islamabad Raw...Amil Baba Dawood bangali
 
办美国阿肯色大学小石城分校毕业证成绩单pdf电子版制作修改#真实留信入库#永久存档#真实可查#diploma#degree
办美国阿肯色大学小石城分校毕业证成绩单pdf电子版制作修改#真实留信入库#永久存档#真实可查#diploma#degree办美国阿肯色大学小石城分校毕业证成绩单pdf电子版制作修改#真实留信入库#永久存档#真实可查#diploma#degree
办美国阿肯色大学小石城分校毕业证成绩单pdf电子版制作修改#真实留信入库#永久存档#真实可查#diploma#degreeyuu sss
 
Decoding the Heart: Student Presentation on Heart Attack Prediction with Data...
Decoding the Heart: Student Presentation on Heart Attack Prediction with Data...Decoding the Heart: Student Presentation on Heart Attack Prediction with Data...
Decoding the Heart: Student Presentation on Heart Attack Prediction with Data...Boston Institute of Analytics
 
专业一比一美国俄亥俄大学毕业证成绩单pdf电子版制作修改
专业一比一美国俄亥俄大学毕业证成绩单pdf电子版制作修改专业一比一美国俄亥俄大学毕业证成绩单pdf电子版制作修改
专业一比一美国俄亥俄大学毕业证成绩单pdf电子版制作修改yuu sss
 
RadioAdProWritingCinderellabyButleri.pdf
RadioAdProWritingCinderellabyButleri.pdfRadioAdProWritingCinderellabyButleri.pdf
RadioAdProWritingCinderellabyButleri.pdfgstagge
 
Minimizing AI Hallucinations/Confabulations and the Path towards AGI with Exa...
Minimizing AI Hallucinations/Confabulations and the Path towards AGI with Exa...Minimizing AI Hallucinations/Confabulations and the Path towards AGI with Exa...
Minimizing AI Hallucinations/Confabulations and the Path towards AGI with Exa...Thomas Poetter
 
Semantic Shed - Squashing and Squeezing.pptx
Semantic Shed - Squashing and Squeezing.pptxSemantic Shed - Squashing and Squeezing.pptx
Semantic Shed - Squashing and Squeezing.pptxMike Bennett
 

Recently uploaded (20)

Consent & Privacy Signals on Google *Pixels* - MeasureCamp Amsterdam 2024
Consent & Privacy Signals on Google *Pixels* - MeasureCamp Amsterdam 2024Consent & Privacy Signals on Google *Pixels* - MeasureCamp Amsterdam 2024
Consent & Privacy Signals on Google *Pixels* - MeasureCamp Amsterdam 2024
 
Predictive Analysis for Loan Default Presentation : Data Analysis Project PPT
Predictive Analysis for Loan Default  Presentation : Data Analysis Project PPTPredictive Analysis for Loan Default  Presentation : Data Analysis Project PPT
Predictive Analysis for Loan Default Presentation : Data Analysis Project PPT
 
DBA Basics: Getting Started with Performance Tuning.pdf
DBA Basics: Getting Started with Performance Tuning.pdfDBA Basics: Getting Started with Performance Tuning.pdf
DBA Basics: Getting Started with Performance Tuning.pdf
 
Biometric Authentication: The Evolution, Applications, Benefits and Challenge...
Biometric Authentication: The Evolution, Applications, Benefits and Challenge...Biometric Authentication: The Evolution, Applications, Benefits and Challenge...
Biometric Authentication: The Evolution, Applications, Benefits and Challenge...
 
INTERNSHIP ON PURBASHA COMPOSITE TEX LTD
INTERNSHIP ON PURBASHA COMPOSITE TEX LTDINTERNSHIP ON PURBASHA COMPOSITE TEX LTD
INTERNSHIP ON PURBASHA COMPOSITE TEX LTD
 
Easter Eggs From Star Wars and in cars 1 and 2
Easter Eggs From Star Wars and in cars 1 and 2Easter Eggs From Star Wars and in cars 1 and 2
Easter Eggs From Star Wars and in cars 1 and 2
 
Predicting Salary Using Data Science: A Comprehensive Analysis.pdf
Predicting Salary Using Data Science: A Comprehensive Analysis.pdfPredicting Salary Using Data Science: A Comprehensive Analysis.pdf
Predicting Salary Using Data Science: A Comprehensive Analysis.pdf
 
Heart Disease Classification Report: A Data Analysis Project
Heart Disease Classification Report: A Data Analysis ProjectHeart Disease Classification Report: A Data Analysis Project
Heart Disease Classification Report: A Data Analysis Project
 
Data Factory in Microsoft Fabric (MsBIP #82)
Data Factory in Microsoft Fabric (MsBIP #82)Data Factory in Microsoft Fabric (MsBIP #82)
Data Factory in Microsoft Fabric (MsBIP #82)
 
Multiple time frame trading analysis -brianshannon.pdf
Multiple time frame trading analysis -brianshannon.pdfMultiple time frame trading analysis -brianshannon.pdf
Multiple time frame trading analysis -brianshannon.pdf
 
ASML's Taxonomy Adventure by Daniel Canter
ASML's Taxonomy Adventure by Daniel CanterASML's Taxonomy Adventure by Daniel Canter
ASML's Taxonomy Adventure by Daniel Canter
 
Deep Generative Learning for All - The Gen AI Hype (Spring 2024)
Deep Generative Learning for All - The Gen AI Hype (Spring 2024)Deep Generative Learning for All - The Gen AI Hype (Spring 2024)
Deep Generative Learning for All - The Gen AI Hype (Spring 2024)
 
detection and classification of knee osteoarthritis.pptx
detection and classification of knee osteoarthritis.pptxdetection and classification of knee osteoarthritis.pptx
detection and classification of knee osteoarthritis.pptx
 
NO1 Certified Black Magic Specialist Expert Amil baba in Lahore Islamabad Raw...
NO1 Certified Black Magic Specialist Expert Amil baba in Lahore Islamabad Raw...NO1 Certified Black Magic Specialist Expert Amil baba in Lahore Islamabad Raw...
NO1 Certified Black Magic Specialist Expert Amil baba in Lahore Islamabad Raw...
 
办美国阿肯色大学小石城分校毕业证成绩单pdf电子版制作修改#真实留信入库#永久存档#真实可查#diploma#degree
办美国阿肯色大学小石城分校毕业证成绩单pdf电子版制作修改#真实留信入库#永久存档#真实可查#diploma#degree办美国阿肯色大学小石城分校毕业证成绩单pdf电子版制作修改#真实留信入库#永久存档#真实可查#diploma#degree
办美国阿肯色大学小石城分校毕业证成绩单pdf电子版制作修改#真实留信入库#永久存档#真实可查#diploma#degree
 
Decoding the Heart: Student Presentation on Heart Attack Prediction with Data...
Decoding the Heart: Student Presentation on Heart Attack Prediction with Data...Decoding the Heart: Student Presentation on Heart Attack Prediction with Data...
Decoding the Heart: Student Presentation on Heart Attack Prediction with Data...
 
专业一比一美国俄亥俄大学毕业证成绩单pdf电子版制作修改
专业一比一美国俄亥俄大学毕业证成绩单pdf电子版制作修改专业一比一美国俄亥俄大学毕业证成绩单pdf电子版制作修改
专业一比一美国俄亥俄大学毕业证成绩单pdf电子版制作修改
 
RadioAdProWritingCinderellabyButleri.pdf
RadioAdProWritingCinderellabyButleri.pdfRadioAdProWritingCinderellabyButleri.pdf
RadioAdProWritingCinderellabyButleri.pdf
 
Minimizing AI Hallucinations/Confabulations and the Path towards AGI with Exa...
Minimizing AI Hallucinations/Confabulations and the Path towards AGI with Exa...Minimizing AI Hallucinations/Confabulations and the Path towards AGI with Exa...
Minimizing AI Hallucinations/Confabulations and the Path towards AGI with Exa...
 
Semantic Shed - Squashing and Squeezing.pptx
Semantic Shed - Squashing and Squeezing.pptxSemantic Shed - Squashing and Squeezing.pptx
Semantic Shed - Squashing and Squeezing.pptx
 

02. Information solution outline template

  • 1. http://informationaction.blogspot.com Tw: @Alan_D_Duncan Information Strategy | Data Governance | Analytics | Better Business Outcomes Example Data Specifications & Information Requirements Framework INFORMATION SOLUTION OUTLINE & HIGH-LEVEL REQUIREMENTS TEMPLATE (Project Mandate) Alan D. Duncan This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
  • 2. Information Solution Outline & High-Level Requirements http://informationaction.blogspot.com Tw: @Alan_D_Duncan Information Strategy | Data Governance | Analytics | Better Business Outcomes 1 Purpose This is a document template for capturing the overall high-level business requirements and expectations for business information solutions with a significant impact on or requirement for data. (cf. the “Project Mandate” document in PRINCE2). At this stage, expectations are captured in general terms and while additional detail is of value, the main aims are to ensure that the overall expectations and goals of the solution are captured in terms that are meaningful to the business community. For guidance, the information solution will be outlined at a level of understanding sufficient to support the outline Business Case. This template forms part of example data specification & information requirements framework. The framework offers a set of outline principles, standards and guidelines to describe and clarify the semantic meaning of data terms in support of an Information Requirements Management process. (See the Framework Overview for further details.)
  • 3. Information Solution Outline & High-Level Requirements http://informationaction.blogspot.com Tw: @Alan_D_Duncan Information Strategy | Data Governance | Analytics | Better Business Outcomes 2 Information Solution Outline & High-Level Requirements (Project Mandate) SOLUTION REQUIREMENT: The area of information & data management under review and the overall expectations for the solution. SOLUTION OBJECTIVES: Purpose or outcome required. Why is the change to information (or associated systems) needed? What business outcomes are intended? SOLUTION DESCRIPTION: What data or information is required? How is the solution intended to function? What is the scope of changes to the information within the solution? Where does this apply? What will happen to the data? What will change? SOLUTION DATA OWNER: Who is requesting the information solution? Who is accountable for the solutions completion and outcomes of the requirements for any resulting changes to the business information? DATA STEWARD(s): Who is responsible for the execution of the changes to the data? SYSTEMS IMPACTED Which information systems or architectural components will be involved when a change is being made to the definition or consumption of this data e.g.:  SOA  DWH  MDM  Application  Project SOLUTION DELIVERY METHODS & DATA UPDATE PROCESSES How is the solution to be implemented? What methodologies are to be applied? (e.g. MIKE2.0, DMBOK, SCRUM) What changes to the data will be applied? What steps are involved? EXCEPTIONS, CONSTRAINTS & EXCLUSIONS Are there any exceptions or constraints that limit the scope of data delivery? Is there are any data that explicitly won’t be included? DATA VERIFICATION MODE How will the solution changes and impacts on the data be verified as effective and complete?  Audit: after-the-fact review for compliance.  Self-Certification: team compiles the evidence to demonstrate that the checkpoint has been applied  Intervention: Requirement for Data Governance Unit participation will be proactively flagged SOLUTION DOCUMENTATION INPUTS What information or deliverables will be used as an input to the solution delivery process? E,g. requirements documents, draft design documents, Business Case, options papers etc. etc. SOLUTION DOCUMENTATION OUTPUTS What information or deliverables will be created as an output of the process? E,g. minutes, design documents, updates to metadata repository etc.
  • 4. Information Solution Outline & High-Level Requirements http://informationaction.blogspot.com Tw: @Alan_D_Duncan Information Strategy | Data Governance | Analytics | Better Business Outcomes RELEVANT CONTROL ARTEFACTS Which Data Governance and Information Management standards, guidelines, methods and constraints need to be applied? E.g.  Update the Enterprise Conceptual Model  Update the Enterprise Logical Model  Update the solution Physical Design What other principles, guidelines and reference materials could be useful? (e.g. legislation, regulation, policies and standards) RESOURCES & ROLES: Who is to participate in making the required changes to the data and systems? In what capacity/role will they contribute? SOLUTION DATA DEFINITIONS: Capture any known business & technical metadata Are any data lineage impacts identified? Define any updates than need to be applied to Business Data Network & Business Glossary IDENTIFIED DEPENDENCIES Pre-requisites etc. that need to be satisfied before the solution can be implemented. RELATED BUSINESS PROCESSES Identify existing business processes impacted by the delivery of the new data solution. OTHER INFORMATION & NOTES
  • 5. Information Solution Outline & High-Level Requirements http://informationaction.blogspot.com Tw: @Alan_D_Duncan Information Strategy | Data Governance | Analytics | Better Business Outcomes About the author Alan D. Duncan is an evangelist for information and analytics as enablers of better business outcomes, and a member of the Advisory Board for QFire Software. An executive-level leader in the field of Information and Data Management Strategy, Governance and Business Analytics, he has over 20 years of international business experience, working with blue-chip companies in a range of industry sectors. Alan was named by Information-Management.com in their 2012 list of “Top 12 Data Governance gurus you should be following on Twitter”. Twitter: @Alan_D_Duncan Blog: http://informationaction.blogspot.com.au/
  • 6. Information Solution Outline & High-Level Requirements http://informationaction.blogspot.com Tw: @Alan_D_Duncan Information Strategy | Data Governance | Analytics | Better Business Outcomes Intellectual curiosity Skeptical scrutiny Critical thinking http://www.informationaction.blogspot.com.au/ @Alan_D_Duncan http://www.linkedin.com/in/alandduncan