Drowning in a deluge of data
Our ideal customer is a company that is drowning in a deluge of data. If that is you, give me a shout!
Soon after I’d sent out a newsletter announcing that Babbage is now being offered as a service, an old business partner messaged saying that I hadn’t been clear in my messaging of who Babbage Insight is for.
I promised to shoot off another blogpost “the next day”, but it’s taken a while since it took time to internally (within myself) clarify the messaging, and then some more time when I let this stew in my drafts. Finally, nearly a month later I’m shooting this out.
We’ve done this the old way - by simply talking to prospective customers. Admittedly the sample size so far has been small, but there has been a very clear pattern in terms of who is getting excited by what we are building.
Our ideal customer is a company that is drowning in a deluge of data.
Yes, I like alliterations. In fact, did you know that the Hindi phrase for alliteration is itself an alliteration? (anupraas alankaar)
What we are building
Just to remind you - this is what I had written on what we are building:
Think about your current analytics workflow - right now you either have highly rigid repeatable metric calculations, which appear in your dashboard; or entirely bespoke analyses, for which you need to rely on your analytics team.
It doesn’t all have to be this way - a lot of the currently “bespoke” questions answered by the analytics team are predictable, if only one were to look at the data in the dashboard. Technology available in 2024 means that this is actually a repeatable process, one that can be done by an AI data analyst - or a pair of bots that together function as an AI data analyst.
How companies get a deluge of data
That data is being produced nowadays well-at-a-faster-rate is tautological. A lot of companies are recognising this, and are trying to figure out how to get value from this data. Some companies have been quicker than others in terms of figuring this out, but it is safe to say now that there is a sort of FOMO among companies in terms of getting value out of their data.
Having set up their data warehouses, most companies start by being a dashboard. The dashboard gives a quick summary of what is happening in the company. With this new-found transparency, questions get asked. People figure out that the data should be able to provide answers as well. And so a data analytics team gets built.
The analytics team, usually consisting of smart people, figures that a lot of these questions are repetitive. And so it adds “legs” to the dashboard so that they can “be self serve”, and commonly asked questions can be answered right there. I’ve written about how this leads to dashboard sprawl:
And then you find that when the request comes in the future, there is a new nuance to take care of because of which the old dashboard doesn’t work anyway. And in all likelihood, you’ll either add a new dashboard, or more features to your old one
[…]
Related to this, you find some new pattern, and then want to make sure that you don’t miss that pattern the next time it occurs. And so you quickly build a dashboard to notice this pattern. I’ve personally not done this but observed several teams who do.
Soon you have dashboard after dashboard. Report after report. Some of the stuff that we’ve heard from prospective customers in our conversations in recent times:
“Right now there is way too much data that is getting pushed to our managers, and they don’t know how to make sense of it. We need help”
“We track all these numbers regularly. Yet, we often find that we missed some signal, despite it being available in the data”
“I get this massive report daily, but all I care about is these two numbers. The rest of the report is for the others”
“Individual numbers in the dashboard I’m able to easily process, but it is incredibly hard to identify trends. So if you can tell me about trends that are forming, that can be incredibly useful”
How Babbage saves you from drowning
I’m talking about the limited interpretation of Babbage based on what we’ve built so far - not about what we will ultimately build.
Basically, think of how we got to the data deluge - it is because data is consumed in a static manner. You add in analyses “just in case this question gets asked”. The way we turn around is by predicting what questions will get asked - based on the data. And preemptively answering those (and pretty much only those).
So rather than computing everything and showing everything to everyone all the time, we do this in a smarter fashion. All the uninteresting information is pushed to the background - it doesn’t get computed at all. Stories are prioritised and appropriately highlighted. Graphs are carefully chosen based on the story or insight they need to convey.
You get the insight without having to either ask for it or hunt for it. You don’t need to drown in the deluge of data any more!
So who is Babbage for?
If you are a business that either tracks a large number of metrics, or tracks complicated metrics, you will find value in Babbage.
You should be generating high volumes of data (not putting a number on it - as long as you “struggle processing data”, you qualify).
You need to already have a culture of using data to make decisions (rather than simply to confirm biases).
You should have a strong intent to grow - else there is no utility of getting “immediate” insights.
You believe you can get a lot more value from your data than you currently are getting.
If you identified with all the above, Babbage can be of use for you! I’m happy to talk.