Don’t panic: data management vs data strategy vs data governance
“The Hitchhiker’s Guide has already supplanted the great Encyclopaedia Galactica as the standard repository of all knowledge and wisdom, for though it has many omissions . . . it scores over the older, more pedestrian work in two important respects. First, it is slightly cheaper; and secondly it has the words DON’T PANIC inscribed in large friendly letters on its cover.”
— The foreword to The Hitchhiker’s Guide to the Galaxy, 1979
‘Don’t panic’, I was telling myself when I just started in the data domain. For as long as I can remember, I always struggled to initiate a new topic because until I understand how components fit into a larger picture, I cannot fully function. A good case in point is data strategy vs data management (DM) vs data governance (DG). “Eka, there are about 5,900,000 results for the exact keyword “data strategy”, 204,000,000 results for “data management” and 14,700,000 results for “data governance”. You are right, there was no shortage of materials on the topics, but I could not find/come up with the conceptual framework on how IT strategy, business strategy, data strategy, data management strategy, data governance, and data management fit together, or simply, I got stuck. The “Oh, I see” moment happened when I assigned the 5 W’s (and 1 H) — Who? What? When? Where? Why? How? — questions to each component, and then, it was a matter of finding a suitable methodology.
The objective of this article is to share the overall framework I adapted to piece together data strategy, data governance, and data management parts; and share some resources, aka my guidebook throughout the Data Universe.
You might argue with the framework below as IT and Data strategy can be a part of the functional strategies that operate below the business-level strategy. For the purpose of this article, I will place them on the same level as the business strategy.
The 5 W’s (and 1H) questions refer to the six basic questions techniques to ask when gathering information to understand an event, situation, or phenomena. The most frequently quoted source of the term origin is English rhetorician Thomas Wilson (1524–1581), who introduced the method in his discussion of the “seven circumstances”:
Who, what, and where, by what helpe, and by whose,
Why, how and when, doe many things disclose.
— The Arte of Rhetorique, 1560
Data strategy or Why of data?
According to DAMA’s Data Management Body of Knowledge (DMBoK), a “strategy is a set of choices and decisions that together chart a high-level course of action to achieve high-level goals. A data strategy should include business plans to use the information to competitive advantage.” Here’s what the Guide has to say about data strategy. It says that data strategy supports an organizational strategy by identifying business objectives and key drivers. Essentially, data strategy allows answering the question, ‘Why data matters? and ‘How business can benefit from data?’.
The strategy discussion could easily turn into “blah blah blah” without any tangible outcomes if not facilitated properly, and many of those 5,900,000 hits at Google belong to the “blah blah blah” category.
Resources to build the foundations:
- Bernard Marr with Key Business Questions (KBQs) framework at https://bernardmarr.com/
- Donna Burbank, a Managing Director of Global Data Strategy, regularly publishes and participates in webinars at https://www.dataversity.net
- Stacey Barr, a Performance Measure & KPI Specialist at https://www.staceybarr.com/
- Peter Aiken, an acknowledged Data Management (DM) authority, regularly publishes and participates in webinars at https://www.dataversity.net
Data governance or WWWW (Who, What, When, Where) of data?
Data governance is “a collection of practices and processes which help to ensure the formal management of data assets within an organization” (Dataversity). The Guide defines data governance as “a collection of practices, processes, roles, policies, standards, and metrics which help to ensure the formal management of data assets within an organization.” DG defines the roles of those (WHO) involved in data (WHAT data assets) and ensures that the right people (WHO) have access to the right information (WHAT data assets) at the right time (WHEN & WHERE) and in the right format.
So, yes, rules are the essence of data governance but they are also the essence of bureaucracy. As I work mostly with small and medium-sized companies, excessive rules and regulations could be a killer. While researching frameworks for small- and medium-sized companies, I came across the Minimal Data Governance by Nicola Askham, “a minimum approach to proactively managing data quality”. As per this framework, a DG framework consists of policies to mandate data governance; processes that produce consistent results; roles & responsibilities to ensure that responsibility and accountability are agreed upon and shared in the organization; and deliverables — any output that’s a result of work done during the data governance implementation.
Resources to build the foundations:
- Nicola Askham, a Data Governance consultant, https://www.nicolaaskham.com/getstarted
Data management or How of data?
Data management is “a collection of practices, concepts, and processes dedicated to leveraging data assets for business success and compliance with data regulations” (Dataversity). The Guide states that data management manages the full data lifecycle needs of an enterprise by executing and enabling rules and policies described in data governance, hence, it answers the question, ‘How to manage data?’
In my opinion, there are two ways to research data governance and data management (DM) frameworks. Firstly, you could look at formally defined DM frameworks such as the DAMA DMBOK Framework. The DAMA DMBOK Framework “provides insight that can be used to clarify strategy, develop roadmaps, organize teams, and align functions”. It contains (1) context diagrams that give an overview of the process’s input and output, (2) the DAMA Wheel (see above) that defines the data management knowledge domain, and (3) the Environmental Factors Hexagon that shows the relationship between people, process and technology.
As data management frameworks usually encompass multiple tools such as Data Management Maturity Models, your second option to review DM frameworks is through data management maturity models that offer a theoretical-practical framework to data management. Some data management maturity models are DAMA-DMBOK2, DMM, DCAM Maturity assessment from Enterprise Data Management Council.
Resources to build the foundations:
- Maturity models comparison — a shameless promotion — https://medium.com/@eponkratova/everybody-goes-through-phases-and-all-dont-they-64cfee942d7
- Irina Steenbeek, a data management practitioner has a series of articles, white papers, and webinars on data management and data maturity frameworks at https://datacrossroads.nl/free-resources/ Below is one of her widely-circulated comparative analyses of data management maturity models.
As a disclaimer, the article describes my ‘aha moment’ and my way of seeing what the data universe includes and how different components in the universe interact. Nevertheless, I hope it helped you to frame your worldview or directed you to different online resources.
What is next?
If you want to know more or just talk to me, you could reach me at https://www.linkedin.com/in/eponkratova/