Hi. Could it be that the way you perceive that topic of centralization vs decentralisation is somewhat coloured by the kind of organisations you have experience with? Personally I have seen centralization efforts at large companies across insane logistics, manufacturing (iot, maintenance, operations), planning&scheduling, marketing&sales, product development ++ with literally thousands of specialist systems and warehouses and so on. I at least suspect that the mesh thinking and centralization critique is more targeted towards such initiatives. And I understand the whole mesh thing more as a central data catalogue of data from many database houses where each warehouse is more domain centric (e.g logistics) run by teams that optimizes for usage (I.e product). Any reflections?
I appreciate you taking to the time to carefully wade through what amounts to self-interested obfuscation.
In my opinion, complicated and jargon-y frameworks are designed to differentiate service-based businesses that otherwise would have few recommendable qualities.
The jargon serves both as a barrier to entry for competitors as well as a marketing ploy for unwitting and uniformed customers.
This is unfortunate because curious potential customers, who are intellectually humble or simply new to the data space, will often assume that they simply don't understand the complications but trust that they are justified.
However, most helpful conversations seldom happen on the content pages of businesses, but in forums, and independent articles (like yours) interested in informing and evaluating claims, independent of any financial incentive.
Not to be too cynical, but I think the community of data-engineering as a whole could benefit from adopting a healthy skepticism of any framework claiming to be a panacea: a quality framework is one that clearly outlines the conditions under which it is applicable.
This is great Pedram! As you've said, the bigger challenges lie in making use of the data that is made available rather than the way it is made available. Also, I'd add that data literacy is a huge unsolved problem at organizations large and small, and unless that is fixed, companies will unable to utilise their data effectively..
Thanks for this. I thought I was being stupid for not understanding exactly what the point of a "data mesh" was. Now I'm pretty confident its not really something anybody knows
Hi. Could it be that the way you perceive that topic of centralization vs decentralisation is somewhat coloured by the kind of organisations you have experience with? Personally I have seen centralization efforts at large companies across insane logistics, manufacturing (iot, maintenance, operations), planning&scheduling, marketing&sales, product development ++ with literally thousands of specialist systems and warehouses and so on. I at least suspect that the mesh thinking and centralization critique is more targeted towards such initiatives. And I understand the whole mesh thing more as a central data catalogue of data from many database houses where each warehouse is more domain centric (e.g logistics) run by teams that optimizes for usage (I.e product). Any reflections?
I appreciate you taking to the time to carefully wade through what amounts to self-interested obfuscation.
In my opinion, complicated and jargon-y frameworks are designed to differentiate service-based businesses that otherwise would have few recommendable qualities.
The jargon serves both as a barrier to entry for competitors as well as a marketing ploy for unwitting and uniformed customers.
This is unfortunate because curious potential customers, who are intellectually humble or simply new to the data space, will often assume that they simply don't understand the complications but trust that they are justified.
However, most helpful conversations seldom happen on the content pages of businesses, but in forums, and independent articles (like yours) interested in informing and evaluating claims, independent of any financial incentive.
Not to be too cynical, but I think the community of data-engineering as a whole could benefit from adopting a healthy skepticism of any framework claiming to be a panacea: a quality framework is one that clearly outlines the conditions under which it is applicable.
This is great Pedram! As you've said, the bigger challenges lie in making use of the data that is made available rather than the way it is made available. Also, I'd add that data literacy is a huge unsolved problem at organizations large and small, and unless that is fixed, companies will unable to utilise their data effectively..
Thanks for this. I thought I was being stupid for not understanding exactly what the point of a "data mesh" was. Now I'm pretty confident its not really something anybody knows