Does this not still beg the question of if our work is as valuable as we say it is though? For example, I want to believe things like "our opinions, shaped by our knowledge of the data discipline, are equally valuable" and "our instincts where there isn’t complete data, or any data at all, can be very valuable." But...why would it actually be true?
Other teams don't treat us that way; they treat us like question answering services. And it doesn't necessarily follow that because they use us that way, there's so much more we could do if they used us in other way. I'm not under-using a plumber by asking them to fix my pipes but not do my interior design.
Moreover, finance and marketing has expertise in a functional domain - they know how we businesses operate, and what customers thing, and so on. When working on problems about businesses and customers, those perspectives seem plainly useful, even if they're incomplete.
I'm not sure what our version of that is. We can tell you how to set up data stuff, but assumes it's useful in the first place. If the same plumber wanted to join an exec team, they couldn't just say "I know all about pipes!;" they'd have to first prove why pipe expertise extends to other things that matter too. We either are get really bad at convincing people of that, or we're (like the plumber) trying to convince people of something that may not be true. And I think we gotta more seriously grapple with the possibility of the latter.
I think if we focus too much of our attention as an industry and community on data engineering, then there is risk that we are just the plumber trying to persuade people that we know business. We've had to focus so much on DE in the past few years because it was so fundamentally broken. We're not in this place any more, I've spoken to many data leaders over the last few weeks who are starting roles this year knowing: "I can just implement this median data stack and it gets me to a place where I can operate and start to answer the businesses questions and model the world of the business to an acceptable extent." We can be thankful for the likes of dbt, Fivetran and Snowflake who invariably come up in these stacks.
I started out as an analyst. I never got into data to do engineering, I just realised I liked it along the way. I did well in data by doing this: "know how we businesses operate, and what customers think, and so on. When working on problems about businesses and customers, those perspectives seem plainly useful, even if they're incomplete." Maybe I'm a bit of an anomaly in being CPA too, but it just helped me understand how to frame data in the context of a business and how the data models related to business performance and customer behaviour. I always saw my opinions as very similar to those of people in Finance, their knowledge came from what they have seen in their journals/ledgers/P&L/balance sheet, mine came from my data models which in part reflected the same things but also in part gave insight into areas nothing else could. I knew the customer in some ways better than any marketer could, I knew in some ways why money flowed better than any accountant could.
On the first point, I think that's certainly possible. Maybe our problem was that we spent too much time on data infrastructure stuff, and we're just now getting to the point that we can really do the other stuff. But in that case, we can't say it's all valuable; we can just say we don't really know yet.
On the second point, sure, but that's sorta my point. If you go walk into a house and say, "I'm a plumber but I also went to school in interior design, can I design your living room?" and I say yes, that doesn't mean plumbers are good at interior design. It means that people who go to school for interior design are good at interior design, and some people who go to school for interior design sometimes become plumbers. So is a data person who knows finance or marketing valuable when we talk about finance or marketing? Absolutely; I don't think anyone would dispute that. The question is how valuable are most data people if they haven't done that.
Yeah I suppose I've felt it was a given for data people to get generally good at business too in the way that marketers and Finance people are, especially analysts.
Maybe it's the solid grounding in business that all senior leadership need that makes them eligible, data or otherwise. However, by the same argument data people who have this are eligible and shouldn't be excluded.
I like the analogy and believe it is more like a general contractor vs trade specialist.
They are valuable because they know how to get the pieces to work together to get to an outcome.
Most heads of data or analytics I know fit two profiles. They were recently promoted from a technical role like data engineer. Or they backed into the role by first being good with analysis in spreadsheets in their department, then gradually took on more responsibility.
Does this not still beg the question of if our work is as valuable as we say it is though? For example, I want to believe things like "our opinions, shaped by our knowledge of the data discipline, are equally valuable" and "our instincts where there isn’t complete data, or any data at all, can be very valuable." But...why would it actually be true?
Other teams don't treat us that way; they treat us like question answering services. And it doesn't necessarily follow that because they use us that way, there's so much more we could do if they used us in other way. I'm not under-using a plumber by asking them to fix my pipes but not do my interior design.
Moreover, finance and marketing has expertise in a functional domain - they know how we businesses operate, and what customers thing, and so on. When working on problems about businesses and customers, those perspectives seem plainly useful, even if they're incomplete.
I'm not sure what our version of that is. We can tell you how to set up data stuff, but assumes it's useful in the first place. If the same plumber wanted to join an exec team, they couldn't just say "I know all about pipes!;" they'd have to first prove why pipe expertise extends to other things that matter too. We either are get really bad at convincing people of that, or we're (like the plumber) trying to convince people of something that may not be true. And I think we gotta more seriously grapple with the possibility of the latter.
I think if we focus too much of our attention as an industry and community on data engineering, then there is risk that we are just the plumber trying to persuade people that we know business. We've had to focus so much on DE in the past few years because it was so fundamentally broken. We're not in this place any more, I've spoken to many data leaders over the last few weeks who are starting roles this year knowing: "I can just implement this median data stack and it gets me to a place where I can operate and start to answer the businesses questions and model the world of the business to an acceptable extent." We can be thankful for the likes of dbt, Fivetran and Snowflake who invariably come up in these stacks.
I started out as an analyst. I never got into data to do engineering, I just realised I liked it along the way. I did well in data by doing this: "know how we businesses operate, and what customers think, and so on. When working on problems about businesses and customers, those perspectives seem plainly useful, even if they're incomplete." Maybe I'm a bit of an anomaly in being CPA too, but it just helped me understand how to frame data in the context of a business and how the data models related to business performance and customer behaviour. I always saw my opinions as very similar to those of people in Finance, their knowledge came from what they have seen in their journals/ledgers/P&L/balance sheet, mine came from my data models which in part reflected the same things but also in part gave insight into areas nothing else could. I knew the customer in some ways better than any marketer could, I knew in some ways why money flowed better than any accountant could.
On the first point, I think that's certainly possible. Maybe our problem was that we spent too much time on data infrastructure stuff, and we're just now getting to the point that we can really do the other stuff. But in that case, we can't say it's all valuable; we can just say we don't really know yet.
On the second point, sure, but that's sorta my point. If you go walk into a house and say, "I'm a plumber but I also went to school in interior design, can I design your living room?" and I say yes, that doesn't mean plumbers are good at interior design. It means that people who go to school for interior design are good at interior design, and some people who go to school for interior design sometimes become plumbers. So is a data person who knows finance or marketing valuable when we talk about finance or marketing? Absolutely; I don't think anyone would dispute that. The question is how valuable are most data people if they haven't done that.
Yeah I suppose I've felt it was a given for data people to get generally good at business too in the way that marketers and Finance people are, especially analysts.
Maybe it's the solid grounding in business that all senior leadership need that makes them eligible, data or otherwise. However, by the same argument data people who have this are eligible and shouldn't be excluded.
I like the analogy and believe it is more like a general contractor vs trade specialist.
They are valuable because they know how to get the pieces to work together to get to an outcome.
Most heads of data or analytics I know fit two profiles. They were recently promoted from a technical role like data engineer. Or they backed into the role by first being good with analysis in spreadsheets in their department, then gradually took on more responsibility.
Neither stereotype is prepared
In your opinion how should a data lead get prepared so that it's unreasonable for them to be excluded based upon unpreparedness?
Possibly, what did you have in mind?
Sure
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