This week, I’m discussing the three tentpoles of this year’s INMA Smart Data Initiative: testing, growing the data team, and AI/machine learning.

This second topic is the winner in terms of its prevalence, according to my interviews and e-mails last year. This touches on both hiring and organisation, but also on questions of how the data team colocates its engineering enablement: Is data engineering in data? Does it stay in engineering? Are product analysts in product? In data?

All of these could be their own self-standing topics I suppose, but this is all to say: lots of ground to cover. 

Last year, I took a look at some of the zero-to-one journeys of data teams, which also touched on everyone’s favourite topic (because I guess there are a lot of nerds in these parts): Org charts (prior newsletters on the topic can be found here, here, and here.)

I don’t know that I will revisit org charts specifically, but there are at least two really interesting conversations I had recently that offered some high potential for debate:

Where do product analysts belonged and what background they should bring to the role?

They often lean more to product management in media companies, but elsewhere in tech, they are often deep analytics folks.

A product manager friend also remarked on something that had never occurred to me, which is the career path of product analysts. Often, these are folks who are looking to move into a more traditional product management role, so their incentive is to get deeper in with the PM crowd. That is also different from what you find in tech, where a product analyst’s career will not necessarily affiliate with product but with analytics/data. 

I suspect there are many other variations on this question — roles where data is more embedded into a specific craft and what this means in terms of the data practice built into these roles and careers (in newsroom, you’d see this with analytics folks vs. audience engagement folks).

Should I hire offshore or onshore?

On the hiring/people side, I want to focus on offshore vs. onshore.

This topic that came to me last year from folks in medium-sized organisations who were both intrigued by the good reviews from larger publishers’ establishing data operations in certain offshore locations with known pools of good talent in data (Eastern Europe being popular for this) but concerned about downsides of such decisions.

Managing data remotely is tricky because subject matter expertise is useful for our industry. Says a large North American publisher: “Sure you get a lot of data science for your buck, but you have to invest in a lot of onshore talent to manage it.” 

We know that testing and optimising a checkout cart is a fairly prevalent problem, but our specific problems around advertising or paywalls, for example, are uniquely large. So the balance of arguments for offshore’s economic efficiency isn’t without its downsides. (If you work at a publisher with an offshore data team, I can promise anonymity — but please come speak to me. Your industry friends  want to know.)

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