Overview#

This is the second phase of the Data Product Creation lifecycle and the first stage from where AI Foundation assumes active responsibility of deliverables and Product Owner assuming accountability of the solution.

Success

Create a working project in a project management tool of choice
Document detailed requirements. The high level requirement document from previous phase acts as input to this phase
Data Product Registration
Solution Design
Risk Assessment

Post the design phase TDA needs to be done

Note

A typical team setup for development of a Use case, shall have the following key roles identified in the early stages of initiation.

Function	Role to be staffed from	How to request the role
Solution / Product Owner	LoB	LoB Responsibility, can take guidance from GD&AI vertical
Solution / Product Manager	Lob	LoB Responsibility, can take guidance from GD&AI vertical
System Manager	GD&AI	Reach out to GD&AI vertical
Technical Lead	GD&AI Vertical	Reach out to GD&AI vertical
IT QA	GD&AI	Reach out to GD&AI vertical
Validation Lead	LoB	LoB Responsibility, can take guidance from GD&AI horizontal
Project Manager	LoB	LoB Responsibility, can take guidance from GD&AI vertical
Scrum Master	GD&AI	LoB Responsibility, can take guidance from GD&AI vertical
Lead Architect	GD&AI	LoB Responsibility, can take guidance from GD&AI vertical
Tester	GD&AI	LoB Responsibility, can take guidance from GD&AI vertical
Developers	GD&AI	LoB Responsibility, can take guidance from GD&AI vertical

Data Governance during Design Phase#

Objective:#

Ensure data products meet FAIR Principles through comprehensive metadata capture.

During the design phase, Data Governance ensures that Data Products are functional, high-quality, and secure by capturing metadata for data product attributes to ensure they are Findable, Accessible, Interoperable, and Reusable.

Metadata Requirements for FAIR Data Products#

To ensure Data Products are Findable, Accessible, Interoperable, and Reusable, follow these guidelines: 1. Governance: Assign a Data Owner and Data Steward for every Data Product.

Risk Assessment: Conduct and document risk and classification assessments (e.g., Data Classification, PII, GxP). Currently, use the standard risk assessment format for IT. A simplified version of a risk assessment for data products is planned for November linked to the entity in ServiceNOW.
Data Product Attributes: Maintain complete metadata, including source and product IDs, descriptions, etc.
Source Tagging: Tag source data with classification and domain tags from enterprise data guardrails.
Lineage: Ensure data lineage from source to product is captured in NNDM, where all input and output ports are present, e.g., with linked sources, applications, and data contracts or consumer data products.
Semantically Consistent: Use defined business terms from the business glossary; link data models to the enterprise model where relevant.
Data Quality: Define and monitor data quality metrics (completeness, accuracy, timeliness). For more information and support, check the services and support from data quality here.
Usage Map: Map producing and consuming systems; document interface agreements. To be defined further
Implementation SOP: Ensure the implementation of data products is in alignment with the applicable SOPs, e.g., that data products are correctly classified according to Protecting and Handling Information SOP.
Retention Policy: Define and document retention and disposal procedures aligned with corporate policies.

Fair Data Product Requirements#

Data Product Attributes	Description	Required/ Optional	Rationale
1. Governance
Data Owner	Owner of Data Products	Required	To establish ownership and stewardship for Data Products critical for governance, maintenance, and enhancements of Data Products. LoBs can further customize the roles to required granularity, e.g., Data (product) Owner, Data (integrity) Owner. Key consideration is that Owner is ultimately accountable, while steward is responsible for operations and adherence to the requirement of CRUD operations. SME roles cover extended roles specific to an LoB (Data Modelers, ontologists, scientific stewards, researchers, etc.).
Data Steward	Data Steward responsible for maintaining the data product content and quality	Required
Subject Matter Expert	Responsible for questions regarding the data asset or data product	Optional
2. Risk Assessment
Business Impact	Risk Classification (High, Medium, Low) in alignment with IT Security Risk assessment Framework	Required	Helps assess and manage the risks associated with a data product to protect NOVO NORDISK data and ensure compliance with regulations.
Data Sensitivity Classification	Data sensitivity classification (e.g., public, Internal Use, Confidential, Strictly Confidential)	Required
Personal Identifiable Information (PII)	Specify if the data source contains Personally identifiable information (PII) (e.g., Yes or No)	Required
GxP	Specify if the data source contains GxP data as part of a GxP validated process	Required
3. Attributes
Data Product ID	Global Unique and persistent identifier from Purview and NOVO NORDISK DM	Required	Identification and brief description of Data Product for quick understanding.
Data Product Name	A user-oriented name of the data product. Use plain English and avoid abbreviations	Required
Data Product Description	A description of the data product in non-technical lingo, use plain English and do not use abbreviations that are more commonly used, e.g., PC, ATM, etc.	Required
Source ID	Unique ID of the source system	Required	Identification and brief description of the source system where actual data is hosted and its owner.
Source System Name	Name of the software used to perform a function	Required
IT-system owner	The owner of an IT system is a Line Manager who is responsible for one of the business processes supported by the IT system	Required
Source Type	Type of source (e.g., Oracle, Azure SQL, SQL Server, ADLS Gen2, Databricks, PostgreSQL, Snowflake, Power BI)	Optional	Identifies the origin of the data product, helping consumers understand the underlying technology.
Source Connection Details	Connection details like S3 Bucket URL	Optional	Defines how data consumers can technically connect to the data source, e.g., PostgreSQL: Server, Port, DB Name.
Service Account	Azure AD identity used for accessing the source (if Azure-hosted)	Optional	To enforce secure authentication and ensure compliance with Novo Nordisk's security guidelines.
Access Credentials	Credentials (username/password) for accessing the data source (if not using a service principal)	Optional
4. Source Tagging
Tag Name	Name of tags in Data Sources (E.g., Data Quality tags, Business or Domain tags such as Financial Data, Clinical Data, etc.)	Optional	Helps categorize the Data Products.
5. Lineage
Data Lineage	Lineage from source to data product in the data marketplace	Optional	Helps with auditing, troubleshooting, and understanding data flows.
6. Semantically Consistent
Business Glossary	Definition of business terms used in Data Product in the glossary	Optional	Ensures common understanding of business terminology, enhancing communication and reducing ambiguity across teams.
Data Model	Data Model for Data Assets & Products linked to enterprise data model	Required	Helps understand the underlying structure of a Data Product.
7. Data Quality
Data Quality Measures	Data should have data quality metrics and monitoring defined	Required	Ensures Data Products meet business and user needs.
8. Usage Map
Interface Agreements	Data map over producing and consuming systems	Optional	Interface Agreements act as the foundation for standardized, secure, and governed data exchanges, enabling seamless interoperability between systems while ensuring compliance, trust, and usability of Data Products.
9. Implementation SOP	Ensure Implementation of data product is in alignment with the applicable SoPs e.g., that data products are correctly classified according to Protecting and Handling Information SOP.	Optional	Ensure all compliance requirements are met.
10. Retention Policy
Retention Time	Retention time for the content of the data	Required	Ensure compliance with legal and regulatory requirements and optimal data storage management.

Data Governance Success Metrics & Checkpoints#

Checkpoint 1: Risk assessments completed and documented
Checkpoint 2: Metadata validated against FAIR Data Product requirements

Data Governance Common Challenges & Solutions#

Challenge: Incomplete metadata leading to poor discoverability - Solution: Implement a comprehensive checklist for required metadata attributes - Prevention: Conduct regular reviews of metadata against established standards