Key Points

References

Key Concepts

Jim DG, EDM keys

My career is managing data anywhere:  goalie, janitor, whisperer, therapist

Role keys- alignment on IT architecture, plans, priorities, business partnerships internal & external

SDP - virtual teams > discovery > assessment > plan > design > test > train > rollout > support

From IT EA, EDM, Digital Transformation programs, create related program for EDG that is aligned, integrated w BUs, clients, vendor services to meet goals on EDM quality & services OKRs, DT, Compliance ( internal and external audits )

EDM - Enterprise Data Mgt - 4 R keys - data & services are: RIGHT, RELIABLE, RESPONSIVE, REACTIVE across the enterprise, clients and vendor services

DTCC DLT Architect part of EA services tower w other IT lines > governance support on DLT, data

EDM app services, infrastructure, support tools 

Collibra for MDM or ?

Couchbase for distributed NoSQL w JDBC tools, reporting

Pulsar & Solace on global events

DLT w Firefly, Fabric

MySQL, Postgres, Oracle, Snowflake, MongoDB, Aws Aurora

Messaging > Kafka, MQ, ActiveMQ, Artemis ? 

Tools - EA, SA, Archimate, Plantuml, MDM repo ( NOT commercial ones but maybe integrated w Oracle )

Other DG and EDM Concepts

Who should own Data Quality?

https://www.linkedin.com/posts/rajkgrover_dataquality-datamanagement-datagovernance-activity-7139936738956812288-eEwd/?utm_source=share&utm_medium=member_desktop

Ownership of data quality is a critical aspect of effective #datamanagement within an organization. While the specific team or role that owns data quality may vary depending on the organizational structure, size, and industry, there are several common approaches:

 

#DataGovernance Team:

 

Role: A dedicated Data Governance Team or Office is responsible for defining and enforcing data management policies, including data quality standards.

Responsibilities:

-Establishing data quality policies and procedures.

-Defining data quality metrics and benchmarks.

-Monitoring and enforcing data quality standards.

 

#DataStewardship Team:

Role: Data stewards are individuals or a team responsible for the management and oversight of specific sets of data.

Responsibilities:

-Ensuring data quality at the operational level.

-Resolving data quality issues and discrepancies.

-Collaborating with business units to improve data quality.

 

IT or Data Management Team:

Role: The IT or Data Management Team, including database administrators and data engineers, may be responsible for technical aspects of data quality.

Responsibilities:

-Implementing data quality tools and technologies.

-Monitoring and optimizing data quality processes.

-Collaborating with business units to understand data requirements.

 

#BusinessAnalysts or #DataAnalysts:

Role: Business analysts or data analysts who work closely with business units and understand data requirements can play a role in ensuring data quality.

Responsibilities:

-Profiling and analyzing data to identify quality issues.

-Collaborating with data stewards to address data quality concerns.

-Participating in the definition of data quality rules.

 

Quality Assurance (QA) Team:

Role: In organizations with a strong QA function, the QA team may be involved in ensuring data quality for systems and applications.

Responsibilities:

-Applying QA principles to data-related processes.

-Conducting data validation and testing.

-Collaborating with data owners and stewards.

 

Business Units and Data Owners:

Role: In a decentralized model, business units or data owners may have ownership of data quality for the data they generate and use.

Responsibilities:

-Defining and maintaining data quality requirements.

-Taking ownership of data quality improvement initiatives.

-Collaborating with data stewards and IT teams.

 

#ChiefDataOfficer (#CDO) or Chief Analytics Officer (CAO):

Role: The CDO or CAO may have a strategic role in setting the overall vision for data quality and ensuring alignment with business goals.

Responsibilities:

-Setting the strategic direction for data quality.

-Advocating for data quality best practices.

-Collaborating with executive leadership to prioritize data quality initiatives.

 

Image Source: Eckerson Group

Analytics COE Models for Enterprises

Sample Digital Transformation Project Roadmap Checkpoints

Potential Value Opportunities

Potential Challenges

Candidate Solutions

Step-by-step guide for Example

sample code block

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