Creating a Data Management Strategy

This article looks at how different practice areas within the DAMA DMBOK interact and support each other in managing and leveraging data assets within an organization.

Why or three-legged stool?

I want to start with data practice in my company but where do I start?
In the webinar ‘Data Management Best Practices’, Peter Aiken suggested using the 3-legged stool approach to ensure successful data usage and data management adoption, stating that the organization cannot succeed with any data management without every ‘leg’ of the stool working.

All sounds good but how do I pick three areas to focus on? One approach to tackle the data practice implementation complexity is to look at the interdependency of Data Management practice areas where each practice area informs and supports another area(s) and focus on those areas that are interdependent or directly related.

What came first, the chicken or the egg?

In this article, I characterize relationships as either supporting or interacting.

Supporting relationships indicate that a data management practice area provides actual physical resources, such as infrastructure or guidelines to enable another data management area to function effectively. For example, Data quality supports Data governance by providing the measures and mechanisms necessary for enforcing governance policies related to data accuracy, completeness, consistency, and timeliness.

Interacting relationships assume that areas collaborate, influence, or impact each other’s processes, decisions, or outcomes. For example, Data storage interacts with data integration by providing the storage infrastructure necessary for integrating data from various sources.

Arguably, this mapping raises the question, ‘what came first, the chicken or the egg?’ For example, is it data quality that supports Data governance by providing the measures and mechanisms necessary for enforcing governance policies related to data accuracy, completeness, consistency, and timeliness? Or is it Data Governance that provides policies, standards, and guidelines to support Data Quality efforts?

The current version of the areas’ interdependences looks like:

Metadata Management supports all practice areas by capturing and managing metadata to describe, manage, and govern data assets.
Data architecture & Data governance. Data architecture supports data governance by providing the technical framework and infrastructure to implement the policies, standards, and controls defined by data governance.
Data modeling & Data governance. Data modeling supports data governance by providing the structured representation of data assets according to governance policies and standards.
Data storage & Data governance. Data storage supports data governance by implementing storage solutions that comply with data governance policies and standards.
Data security & Data governance. Data security supports data governance by enforcing policies, standards, and controls related to data security and access management.
Data warehousing & Data governance. Data warehousing supports data governance by providing a centralized repository for integrated and curated data that adheres to governance policies and standards.
Reference & MDM & Data governance. Reference & MDM supports data governance by providing a framework for establishing and enforcing policies, standards, and controls related to reference and master data.
Data storage & Data architecture. Data storage supports data architecture by providing the infrastructure for storing and organizing data assets according to architectural principles and standards.
Data architecture & Data modeling. Data architecture interacts with data modeling to implement the architectural principles and standards defined by data modeling.
Data modeling & Data storage. Data storage supports data modeling by providing the physical storage structures and configurations that reflect the data models designed by data modelers.
Data governance & Data integration. Data governance provides the policies, standards, and guidelines that govern the integration of data across the organization.
Data architecture & Data integration. Data architecture supports data integration by providing the architectural framework for integrating data from various sources.
Data modeling & Data integration. Data modeling supports Data integration by providing a structured representation of data assets.
Data architecture & Data warehousing. Data architecture supports data warehousing and business intelligence by providing the architectural foundation for designing and implementing data warehouse structures, dimensional models, and reporting environments.
Data modeling & Data warehousing. Data modeling supports data warehousing and business intelligence by providing the dimensional models, schemas, and data structures necessary for designing data warehouse repositories and BI reporting environments.
Data integration & Data warehousing. Data integration interacts with data warehousing/business intelligence to populate data warehouses with integrated and cleansed data from various sources.
Reference Data & Data quality. Reference & MDM supports data quality by ensuring the accuracy, completeness, and consistency of reference and master data assets.
Data Architecture & Reference Data. Reference & MDM supports data architecture by providing the infrastructure for managing reference and master data.

If you want to get more interactive, although a more simplified version as only ‘supporting’ relationships are kept, you can check the dependency graph here (you need to download the file) to filter by a particular practice area (edge).

https://github.com/eponkratova/articles/tree/master/dama_dependencies >> dama_wheel.html

Is the mapping complete? It is far from being complete… it was just an attempt to identify the related areas to tackle when building a data practice but thanks for reading :)
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