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| Business Intelligence Series |
I watched today on YouTube Power BI Tips' "One Person to Do Everything" episode I missed last week. The main topic is based on Christopher Laubenthal's article "Why one person can't do everything in the data space". Author's arguments are based on an analogy between the various data areas and a college's functional structure. Reading the article, I must say that it takes a poorly chosen analogy to mess messy things more!
One of the most confusing things is that there are so many data-related context-dependent roles with considerable overlapping, that it becomes more and more difficult to understand what they cover. The author considers the roles of Data Architect, Data Engineer, Database Administrator (DBA), Data Analyst, Information Designer and Data Scientist. However, to the every aspect of a data architecture there are also developers on the database (backend) and reporting side (front-end). Conversely, there are other data professionals on the management side for the various knowledge areas of Data Management: Data Governance, Data Strategy, Data Security, Data Operations, etc. There are also roles at the border between the business and the technical side like Data Stewards, Business Analysts, Data Citizen, etc.
There are two main aspects here. According to the historical perspective, many of these roles appeared when a new set of requirements or a new layer appeared in the architecture. Firstly, it was maybe the DBA, who was supposed to primarily administer the database. Being a keeper of the data and having some knowledge of the data entities, it was easy for him/her to export data for the various reporting needs. In time such activities were taken over by a second category of data professionals. Then the data were moved to Decision Support Systems and later to Data Warehouses and Data Lakes/Lakehoses, this evolution requiring other professionals to address the challenges of each layer. Every activity performed on the data requires a certain type of knowledge that can result in the end in a new denomination.
The second perspective results from the management of data and the knowledge areas associated with it. If in small organizations with one or two systems in place one doesn't need to talk about Data Operations, in big organizations, where a data center or something similar is maybe in place, Data Operations can easily become a topic on its own, a management structure needing to be in place for its "effective and efficient" management. And the same can happen in the other knowledge areas and their interaction with the business. It's an inherent tendency of answering to complexity with complexity, which on the long term can be in the detriment of any business. In extremis, organizations tend to have a whole team in each area, which can further increase the overall complexity by a small to not that small magnitude.
Fortunately, one of the benefits of technological advancement is that much of the complexity can be moved somewhere else, and these are the areas where the cloud brings the most advantages. Parts or all architecture can be deployed into the cloud, being managed by cloud providers and third-parties on an on-demand basis at stable costs. Moreover, with the increasing maturity and integration of the various layers, the impact of the various roles in the overall picture is reduced considerably as areas like governance, security or operations are built-in as services, requiring thus less resources.
With Microsoft Fabric, all the data needed for reporting becomes in theory easily available in the OneLake. Unfortunately, there is another type of complexity that is dumped on other professionals' shoulders and these aspects need to be furthered considered.
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Resources:
[1] Christopher Laubenthal (2024) "Why One Person Can’t Do Everything In Data" (link)
[2] Power BI tips (2024) Ep.292: One Person to Do Everything (link)
