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RowanC's avatar

I think the problem is hiding in the idea of an outsourced ‘data team’, seperate and independent from ‘the business’. Businesses with a lot of data have for decades had technology teams managing data and tech stacks, and trusted advisors and analysts in their own teams. This model worked well (but variably, as it often depended on an individual) and depended on the tech stack not changing too much (perhaps upgrading the version of oracle). But these advisors built trust through thier relationship with key business figures. They didn’t use a ticketing system, because they knew what was important to their leaders. Their leaders held the information with regard, because it was delivered by someone who they felt truly understood their problem (and was probably regularly slipped into different projects, outside their area of expertise, Eg. Business requirements for rewards programs, or delivering a new market segmentation including staffing sales channels).

We used to deliver customised insights to help an individual make a decision, because we knew how they made decisions. Now we use best practice, design frameworks, the latest shiny tools (where we lose a whole bunch of our time and effort) to deliver insights for a “business”.

The big consulting firms understand this, and while they have some great data teams, results from those are often repackaged in PowerPoint for the senior leaders that hired them. They build relationships with individuals.

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Tristan Handy's avatar

Thought-provoking. Thanks for writing this.

From what I can tell there's some priors you're bringing that you're not being explicit about. It might be useful to see if we agree on what those are to draw conclusions from on the overall post.

1. I think you're largely talking about the concept of a "data team" at a digital native business. The entire conversation about "what is the appropriate role of / interface to the 'data team'?" isn't really being had inside of most enterprises. The enterprises that I talk to have a very well-understood org structure (understood inside the organization, anyway) and I don't perceive any lack of clarity around who is "buying" their services and why. There is no lack of PMF for data in the enterprises I talk to; rather, it's the opposite. That's not to say that enterprise data is some shining beacon on a hill (they have their own problems for sure)...they just don't experience this particular problem in this particular way.

2. I think you're really talking about the "analysis / insights / strategy" function of a data team, not the "pipelining / modeling" function of a data team. I don't think you're making the case that companies don't care about _data_, rather, that companies aren't buying the "data team as strategic partner" model.

If those two things are true, then I agree with you. If not, we can fight about that in some other forum :)

And if those two things are true, then it sharpens the question. The question becomes (I think):

> Why should we expect data professionals, who by definition specialize in technical--as opposed to functional--skills, to know more about strategy in a given functional area than their peers who have actually built many-years-long careers inside of that functional area?

Sure, data people do and should build competencies in the functional areas that they are partnered with. But after spending 7 years as a marketer, I can tell you that it will be very unlikely that a data analyst (however skilled) is going to understand the marketing data they're looking at better than I do. Maybe if they're working in a sufficiently-narrow and well-defined problem domain. But if you all the sudden observe a drop-off in a particular conversion rate, the instincts that will get you closer to an answer _fast_ are marketing instincts, not data instincts. Having access to data to answer this question is critical (and so pipelines and modeling and metrics are critical), but the specific expertise required to diagnose and fix this problem is marketing expertise.

The hardest part about data in a digital native business is to figure out how to get two skillsets--functional and data (technical)--sharing a headspace. Long-lived embeds of data people on specific product teams are the model that many of the most successful companies have learned to do this. But this team structure is unusual...perhaps because it's more expensive than most companies can afford. These companies do it because their scale and value of data more than justifies it, and because the atomic group--the two pizza engineering/product team--is very receptive to the addition of this person.

IMO there is not a single right answer to this, but there are wrong ones. If you're expecting a really small data team to support all of the functional areas of a business and to be able to offer "strategic insights" that's likely just not a real thing unless you have some really unusually exceptional data people or unless you focus them really narrowly. But IMO that's fine. That just means that data teams need to be really clear about their mandate given the particular context of their business. And analysis / insights / strategy may or may not be a part of it. And that's ok.

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