Ask HN: Lessons learned from implementing user-facing analytics / dashboards?

For instance, if a marketing head wants to plot CAC (cost of acquiring customers) over time, saying CAC is number of customers divided by marketing spend is manager-speak. Spends are budgeted higher early in the month and adjusted with actuals. Customers ask for refunds and cancel accounts. Some campaigns have volume incentives which are known later... and so on. The solution is to write well commented SQL which laymen can audit and improve.

I’m not sure what they were trying to manage, but it was purdy and looked dashboardy.

- You can output the most elegant metrics, you will never know if it was the right one until you talk to actual customers. Most of the time, they don't even understand what is presented.

- Use libraries, ui-kit made for this, it will save a huge amount of time.

- Whatever you do it will: never be enough, wrongly interpreted, used in the wrong context.

- Try to tie graph and metrics to use cases or questions. e.g titling: "Active user" vs "How many users were active* in the last 30days?" (* define active in the description) can make a huge diff in terms of comprehension

Having been on both sides of this, I think the challenge is that the CEO/COO's job is to figure out "what should we do about this?," which is the right approach to coming up with that summary (it's not just "here's a text version of the chart"). And the corollary challenge is that, in most cases, non-technical people with domain knowledge are the ones who need to produce the analysis: so any feature incomplete dashboard is going to stymie them and any general framework that requires a technical person to step in for code or configuration is going to slow the process to a crawl.

It's the rule (not the exception) that (especially if things are going poorly) the next step is asking more questions, which involves investigating something else in more detail. A dashboard, however pretty, is as useless as a doorknob if it doesn't have the needed information.

I have found that dashboards per say are always great as the high-level KPI trackers, like the things you would consider hanging on a wall in an office (e.g. "revenue growth this month" or "new customers acquired"). You'll always want to know that information, and many people of unrelated departments need to have that information shared to them.

The other helpful area is a deep-dive domain-specific analytics programs, like for example Google Analytics, where it has a very full featureset for non-technical marketing people to go in and drill down to answer questions. The UI/UX designers of that product have spent years honing and A/B testing which types of graphs to show where, and mapping out how to have people click around to find what they're looking for, to the point it is pretty easy for non-technical people. They even have courses and certifications on how to use the system.

Organizations that try to internally build a feature-complete system like google analytics for a specific domain need to consider it like building an entire software product (even if there's a general low/no-code BI SaaS to assist) because you'll need collaboration between general technical experts and non-technical stakeholders with changing and vague requirements. It can be done, but likely only with years of investment and UI/UX research, just like any other software product that solves a domain problem well. In practice: millions of dollars.

Technologists often forget that Excel *is* a turing complete programming language (and it's a functional programming paradigm too!). If an org is not committed to spend years and millions of dollars on deep dive analytics for a specific domain, the right choice is almost always using a commercial analytics system for that domain that costs less than the internal build, or embracing the trusty spreadsheet.