Choosing Data Management IT Tools: Data Governance Solutions
Dr. Irina Steenbeek
Data Management Practitioner & Coach | Data Management and Governance Frameworks | DM Maturity Assessment | Data Lineage | Metadata | Keynote Speaker | Author: The O.R.A.N.G.E. Data Management Framework & 4 books
The previous article?of the series “Choosing Data Management IT Tools”??reviewed specifics in selecting data lineage solutions and their functionalities. In this article, we will discuss the following:
Data Governance: Definition, Content, and Associated Challenges
The term “data governance” has multiple definitions in the data management context. I’ve discussed this issue in several articles, “Data Management and Data Governance in a Nutshell,”?“Data Management and Governance 101,” and “DAMA-DMBOK2 vs. DCAM 2.2: Mapping between Frameworks.”
In this article, I will only summarize the key challenges that this issue brings to selecting a data governance tool.
Challenge 1: Data governance’s definition, role, and deliverables differ between leading industry guidelines.
DAMA-DMBOK2 and DCAM are two leading industry guidelines/frameworks that have different viewpoints on the content of data governance. Let’s start by comparing their definitions.
DAMA-DMBOK2?says, “Data governance is the?exercise of authority, control, and shared-decision making?(planning,?monitoring, and enforcement) over the management of data.”
DCAM?has its interpretation of a data governance function: “The function that defines and implements the standards, controls and best practices of the data management initiative in alignment with strategy.”
Figure 1 demonstrates differences in these definitions.
DAMA-DMBOK2 defines data governance as a knowledge area, while DCAM does it as a function. In my viewpoint, the “data governance knowledge area” provides the theoretical foundation, while the “data governance function” is about the practical application of that knowledge.
The key challenge is that from the DAMA-DMBOK2 viewpoint, data governance only plans, monitors, enforces, and controls what data management does. DCAM delegates to data governance the “implementation” power. However, it isn’t easy to interpret the actual meanings of these definitions.
The more significant challenge is that these frameworks define data governance deliverables differently.
DAMA-DMBOK2 limited data governance deliverables to data strategy, policies, processes, roles, plans, scorecards, etc.
DCAM is a closed society, making it hard to understand its approach and methodology. Some earlier publications available to the public demonstrated deliverables that DCAM assigned to data governance. Some of them, like data domains, models, glossaries, and classifications, could hardly be considered data governance deliverables. They are artifacts of data modeling and architecture. I don’t have access to the current version of DCAM and don’t know whether they changed their viewpoint.
Challenge 2: Leading authorities in data management have quite different understandings of data governance.
This statement is easy to prove. Below are several definitions I got by searching for “data governance definition” in Google.
“Data governance (DG) is the process of managing the availability, usability, integrity and security of the?data?in enterprise systems, based on internal data standards and policies that also control data usage.”
“Data governance is a collection of processes, roles, policies, standards, and metrics that ensure the effective and efficient use of information in enabling an organization to achieve its goals.”
“Data governance is a set of principles, standards, and practices that ensures your data is reliable and consistent. It also helps ensure that your data can be trusted to drive business initiatives, inform decisions and power digital transformations.”
“Data governance promotes the availability, quality, and security of an organization’s data through different policies and standards.”
领英推荐
“A set of processes that ensures that data assets are formally managed throughout the enterprise. A data governance model establishes authority and management and decision-making parameters related to the data produced or managed by the enterprise.”
Figure 2 summarizes these definitions by answering two questions: WHAT is data governance, and WHY does a company need it?
Even at first sight, you can see that the definitions have similarities and differences. You can find similarities in defining “what” data governance. A process, role, etc., are all components of a data management framework—the differences and challenges you can see in answers to the “why” question. I believe it is a data quality capability accountable for data reliability, quality, trustfulness, and consistency, not data governance. Data governance should only coordinate the development of a data quality framework. A data quality capability is an independent data management capability. The proposed role of data governance leads to the situation when data governance and data quality capabilities are both accountable for data quality which can’t be per definition. What do you think?
Challenge 3: Data Management and Governance are two different concepts, while “management” and “governance” are synonyms from the linguistic viewpoint.
I don’t know the history of introducing and differentiating the concepts of “management” and “governance” in the data management community. Recently, I discovered that several most recognizable linguistic dictionaries,?Merriam-Webster?and?Thesaurus, consider “management” and “governance” synonymous. In daily life, it often leads to the situation that the words “management” and “governance” are used interchangeably. Data-related professionals, who are unfamiliar with the leading industry guidelines, can use these concepts quite freely, giving different meanings and content to these concepts. I will demonstrate the background of this conclusion later in this article by showing the functionalities of “data governance” tools.
All discussed above leads us to a simple conclusion: each company must define its definition of a data governance concept.
Business Needs and Requirements for a Data Governance Tool
The needs and requirements for a data governance tool depend entirely on a company’s internal definition and understanding of data governance.
A few requirements can be expected like the following:
For example, different types of owners like data-, business process-, and system owners are examples.
An example of a use case is the ability to link data requirements and data elements to various legislative documents.
Strangely enough, a business glossary is often considered an artifact of data governance. In my practice, I assign it to data modeling as this capability must describe data elements at various abstraction levels.
A company must formulate the requirements for a data governance tool based on the company’s understanding of data governance and its deliverables.
Overview of COTS Data Governance Tools
In this article, I will discuss several challenges IT and data management professionals must know while selecting a data governance tool.
Read further: https://datacrossroads.nl/2023/07/19/choosing-data-management-it-tools-data-governance-solutions/
About the author:
Dr. Irina Steenbeek is a well-known expert in implementing Data Management (DM) Frameworks and Data Lineage and assessing DM maturity. Her 12 years of data management experience have led her to develop the "Orange" Data Management Framework, which several large international companies successfully implemented.?
Irina is a celebrated international speaker and author of several books, multiple white papers, and blogs. She has shared her approach and implementation experience by publishing?The "Orange" Data Management Framework,?The Data Management Toolkit,?The Data Management Cookbook, and Data Lineage from a Business Perspective.
Irina is also the founder of Data Crossroads, a coaching, training, and consulting services enterprise in data management.?
To inquire about Irina's training, coaching, or participating in your company webinar or event, please, email to?[email protected]?or book a free 30-min session at https://datacrossroads.nl/free-strategy-session/
Co-Founder and Managing Director at Nephos Technologies Ltd
1 年Really interesting post. In my experience, one of the biggest issues is that people buy a tool without purpose. They buy a tool but they've not thought about the output or outcome that they're trying to achieve. I'd be interested to get your thoughts on buying a service as opposed to a tool?
Establish and drive implementation of data management capabilities such as data governance, data quality etc
1 年Thank you for your valuable reflections. In my view you have captured some fundamental challenges, that leads to confusion for alot of people, cheers.