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Is analytics a necessary evil rather than a real value driver?

5•tiazm•1h ago
I’ve been working in analytics and advanced analytics for about 6 years. I started in a large consultancy and later went solo, so I’ve seen both enterprise and smaller product teams up close.

Something keeps bothering me. In most projects, analytics feels like infrastructure that no one is genuinely excited about. People rarely want to invest in it when building a product. It’s treated as something you should have, not something you want to have.

Teams are “happy” to pay for software development, advertising, copywriting, design. Those are seen as directly useful. Analytics (GA4, event tracking, or even more structured setups like CDPs) is often perceived as background noise, necessary to keep the engine running, but not something that meaningfully moves the product forward day to day.

In practice, many teams end up using only a handful of metrics to make decisions, even when a complex analytics stack exists underneath. The rest is there “just in case.”

I’m curious whether others see the same pattern. Is analytics undervalued because its ROI is indirect and delayed? Or is most analytics work simply over-engineered for the actual decisions teams make? At what point does analytics shift from “necessary plumbing” to a real competitive advantage?

Would love to hear perspectives from founders, engineers, and product folks who’ve built and scaled things.

Comments

lbhdc•1h ago
That is interesting. I have worked in the space too, and have seen similar attitudes.

I think it often comes down to who is responsible for making decisions with that data. If a product or business person is the one driving a feature, and looking for adoption, the engineers likely aren't going to be invested in building out sophisticated metrics. They get the metrics they are responsible for from their cloud provider (resource use/latency/scale).

I think that problem is compounded by the perception that these integrations are going to tank your products perf (may hurt the metrics engineers care about).

I think all of those dynamics change in really big companies with thousands of engineers. Then you can often end up in a situation where engineers are now required to maximize product metrics, and need visibility into their small slice of the pie.

So, I think its largely incentive, which is why I see all of the metrics vendors targeting product and sales people in small/mid sized companies.

PaulHoule•1h ago
So I did a lot of business development in the 2010s in a space that involved: semantic web, lo/now code, schema-driven development, business rules, entity matching, "centaur" systems where people work together with ML systems to do work, etc.

There was the obvious choice of "analytics oriented" or "LoB oriented" with the complication that "centaur" and anything subjective like "entity matching" needs some analytics no matter what.

My take now is that you're basically right: overall the spend on LoB is bigger because it right on the path to delivering value whereas analytics are secondary... if you're going to get any value out of analytics you're still going to have to execute in the LoB to realize that value!

On the other hand, analytics might be an easier sell because the analytics system can be dropped on top of what's there and the "low code" capabilities could efficiently accelerate the process. Whereas, "rip and replace" on the LoB would be a huge commitment, anything missing from the new system is a dealbreaker, and if it has to interface with the old system the old system is likely to diffuse the benefits of low code. (with the caveat that maybe a framework that implements "strangler fig" might break the impasse)

One thing that was seductive at the time was being saturated with ads and conferences and sponsored blog posts and such about analytics, but you have to realize this: if something is heavily advertised people want to sell it, not buy it That is, advertising is a bad smell.

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