Actual Self Serve in Business Intelligence
What We've Been Up To
We used to send this newsletter every week, for more than a year. We did it through the pandemic, and we did it even back in 2019, when nobody seemed to be reading. And then we stopped in March.
Why did we stop? Where did we go?
The answer is simple: we've been repositioning.
Repositioning is a fancy marketing term, though, and it shouldn't mean anything to you (and doubly so if you're a data professional reading this newsletter). The way I like to put it is this: 'what's the one set of business outcomes that we want to enable in our customers?'
If we know what those business outcomes are, we can tell people about them, and then we can orient our entire company around that North Star.
And what we've learnt has led us down a surprising path.
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Here's the short version of what we did. We sat up calls with our customers and asked them what they were using us for. We wanted to know how they saw Holistics, and what they got out of using us.
We learnt that our happiest customers belonged to one of three broad clusters:
- A group of customers used us for embedded analytics.
- Another group of customers used us for self-service (more on this in a bit).
- And one last group of users used us as faster augmentation to their first or second wave BI tools.
For a variety of reasons that I'll leave for a longer future blog post, we picked the second cluster of customers to focus on. In other words, we decided to go all-in on enabling self-service in our customers's businesses.
But then we had a problem: self-service has had a long history in the business intelligence world — one of mostly failure. When our customers said 'self-service', they meant a very specific thing: they wanted their non-technical business users to be able to create reports and dashboards of their own.
Not prepared reports where you could do some filtering of results and call it 'self-service'.
Not dashboards where you have to do some minor SQL-query writing (and call this 'self-service').
Self-service means being able to get the data you want, in a format you want, in a dashboard you want, inside your business intelligence tool, preferably without bothering your data team.
(Who, god forbid, is likely already inundated with data requests to begin with).
The good news is that this does kinda already exist in Holistics (otherwise our customers wouldn't have told us that they liked us for this reason). The bad news is that we're going to have to do a lot more work — both product-focused and education — necessary to make as many customers successful at getting self-service to work in their orgs.
This is both a tool problem, as well as a org transformation/culture problem. The good news is that we know a few companies who have taken our tool and done just such a transformation themselves, so we can dig into those for generalizable insights.
All of this is to say: we're back! And we're now focused on helping you get your business users off your backs I mean, helping you achieve self-service in your businesses.
I hope to talk more about this topic soon.
Insights From Elsewhere
Data: Use With Caution — Stay SaaSy is an anonymous SaaS blog, written by two operators, and it's quickly become my favorite business blogs of 2021. In this post, the authors write about data usage in a growing SaaS company. I quote:
What you see less are the glaring imperfections in most data. The show must go on - fine-grained analysis of the data used for strategic decisions holds up things up. Deep dives into data require ability to get that data, interpret that data, and understand that data. This creates a power imbalance where the data-creator is much better positioned to tell the story they want to tell; the data-consumer is hard-pressed to disagree with that story in real-time. Alas, decisions happen in conference rooms, data analysis happens at a desk.
These two realities are Saasy's Axioms of Data:
- Data wins arguments. Imperfect data beats no data.
- All data is imperfect.
These realities have 3 major implications for how you should approach getting things done:
- Show up with data.
- Use data as a compass, not a map.
- Beware the abuse of data.
I think you can see why I like them in just that one snippet alone: they're pragmatic, plainspoken, and they very clearly know what they're talking about.
Why you sometimes need to break the rules in data viz — Rosamund Pearce from The Economist writes about a couple of data visualization rules, and why she's perfectly ok breaking them.
I loved the examples here. As a quick snippet:
Don't do 3D
Ever since Microsoft Excel launched a thousand 3D pie charts into business meetings, 3D charts have been among the most vilified of data visualisations. There are two types of 3D visualisations: ones where the third dimension is used to depict a third variable, and others where it is purely cosmetic. Decorative 3D is particularly frowned upon as it needlessly distorts the data in multiple ways: perspective causes nearer items to appear larger, and using volume to represent a single variable magnifies the appearance of larger data points.
But 3D can work well under certain circumstances. In the following visualisation for The Economists's Graphic Detail section, we used a series of cubes to show the relative volume change of Arctic Sea ice:
As usual, expertise is in the edge cases, not in what is commonplace.
Spotting a cherry-picked ML paper — So you've seen a new ML paper, talking about a new ML technique. Would it work? This is the big question. Eugene Vinitsky has a collection of nice heuristics he uses to answer it.
That's it for this week! If you enjoyed this newsletter, I'd be very appreciative if you forwarded it to a friend. And if you have any feedback for me, hit the reply button and shoot me an email — I'm always happy to hear from readers.
As always, I wish you a good week ahead,
Warmly,
Huy,
Co-founder, Holistics.
PS: If you've not seen it already, we've got a guidebook to bring you up to speed on the ins-and-outs of a contemporary analytics stack — get your copy here.
PPS: Yes really, I'm seeing you next week.