Data Mesh TV: Leveraging International Data Management Standards In Your Data Mesh

Last Updated: May 15, 2023

Long before big data, data management was seen as a formidable opponent and we continue to strive for better ways of having a better command of it in our digital and AI-first era. The good news is that data management has been made easier by the work of the Data Management Association International(DAMA). Founded in 1980 by a collective of technical and business professionals, they recognized the importance of having international data management standards with regional chapters to huddle and share experiences. That culminated into the frequently referenced book: DMBoK, the Data Management Body of Knowledge. 

Fortunately or unfortunately, the rate of data continues to grow and accelerate at an unprecedented speed with businesses yielding mixed business results. Meanwhile, along comes Data Mesh, which hinges on shortening the path between data sources and the value created by that data. The DAMA DMBoK and Data Mesh are not mutually exclusive,to the contrary, they can certainly work really well together.  In this episode of Data Mesh TV, we explore how to leverage international data management standards with Data Mesh.

Leverage DAMA standards: guiding principles, framework, and common vocabulary

As we strive to understand the promise of Data Mesh and how it works together with DAMA, I’d first liken the parallels to a grocery store. From a domain perspective, the vegetable aisle is a domain, the bakery is a domain, the dairy aisle is a domain, etc — all the domains represent the larger organization. 

When you build data products, it’s similar to having a deli in the supermarket, there’s flour, butter, yeast, and you can make a sandwich. There’s a data product owner, but you have responsibilities. If your data consumer (patron) is using your data (eating your sandwich) and he’s not happy so he wants to contact you about the quality of the data (sandwich). Or I want more of this data (ingredient) or more of that data (ingredient).

Meanwhile, data management is more like a label on a food package. The label details data lineage, data quality, data profiling, data ownership, and what you can use the data for. Labels on a food package are there for a reason. There is an expectation that you have as a consumer for your groceries to have a certain level of quality because it’s gone through some level of process and inspection. In the data world, those are governance processes in a grocery store. The activities, the policies, the strategy for data architecture do not change and you don’t have to reinvent the wheel. The bottom line is that you can leverage existing DAMA standards with Data Mesh. 

Data Stewards are responsible for Data Governance outcomes

Data Governance is just one part of the overall discipline of data management, albeit an important one. In any data governance model, the stewardship and ownership of data assets is critically vital and now with Data Mesh, we have to consider the roles and responsibilities of data stewardship within domains as they have a certain level of autonomy. 

Currently, organizations may have a data steward for certain processes, but no organizational alignment on the concept of domains. For instance, IT and the business as a whole struggle with domain alignment. IT could have domains and data owners but those domains may not have meaning within the business, except for IT. However, with Data Mesh, you can organize the roles much more naturally and the data stewards can also align with the organization. Overall, data stewardship could benefit from a different approach, following Data Mesh principles.

Data Mesh can improve the success of your Data Architecture, Data Integration, and Data Quality efforts

data governance

With the Data Governance wheel, it implies that you should consider all the pillars that work together for data governance to be effective. However, in the DAMA DMBoK, we highlight the various governance models, from centralized to federated and hybrid models in between. That’s why it is no surprise that Data Mesh fits perfectly as a viable approach to Data Governance.

The many years of experience, working in data management all over the world, in all segments of the markets, has taught us that there is no one-size-fits-all approach. In fact, the DAMA DMBoK capitalizes on that in providing schemas, inputs, deliverables, and stakeholders that can be positioned exactly how your organization needs it (at that moment, because things change). Data Mesh is a wonderful implementation of ownership, capitalizing on data work already done, scaling up and using knowledge and capabilities already present in various parts of your organization. At DAMA, we fully support this to achieve your data governance and data management goals.

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