Recently, I joined Imply. I’ll further discuss this on a different post, but what really amazed me during these first few days, is how much this company and the team believe in eating our own dog food.
Real-time analytics at scale
But first thing is first… what does Imply do?
Well, if you’re reading this post, you might already know that Imply’s motto is “real-time analytics at scale”. That means we want our customers, regardless of their technical expertise, to have a fast, easy to use, secure and flexible way to analyze the huge amounts of data collected by their organization, and make data-driven decisions. This is what we strive to provide to our customers, and it’s also what we want for ourselves.
It serves 2 main goals:
1. The first one is the same goal as mentioned above, i.e give all (internal) stakeholders an excellent way to make data-driven decisions.
2. As a tech company, it adds another layer of tests to our systems. If I’m a bookkeeper at Imply’s finance team, and I found a system glitch while slicing and dicing through the financial data - I can just file a ticket and let the relevant team handle that.
By now you probably think: “yeah, yeah… all companies are saying they are data-driven, and several of them also claim to eat their own dog food. How are you different?” Well, it’ll be easier to just show you…
This is an actual dashboard used by my team, the Solution Architects Team at Imply. The dashboard is built using Imply Pivot, based on data stored in an Imply cluster (Imply is based on Apache Druid. A deep-dive into the technical details is coming up in the next post of this series…).
The pie chart is divided into the different tasks the team spends time on (e.g training, writing articles like this one, supporting customers, etc.) Furthermore, one can quickly change various aspects in this dashboard, from the chart type, through the dimensions used for filtering, to the measure used, and almost everything in-between.
If you want to see how much time the team spent on events like meetups and conferences during August - it’s 3 clicks away (trust me, I tried!). If you want to understand how much time was spent assisting existing users, breakdown by user - you can! You can even see how many sick days were reported during 2020 so far…
And lastly - once I had access to this dashboard (which was a day or 2 after I joined the team), I was able to do all that slicing and dicing by myself in no time, without any assistance and with very little knowledge of Imply Pivot.
Now, one can argue that I have technical expertise, and a less-technical person might need a few minutes, and maybe a video guide, in order to get there. To that, I’ll say: perhaps, but compare that to the other solutions you might be familiar with. Are they as flexible and easy to use? Would they produce the results in sub-second?
OK, I’m sold! How can I try it myself?
If you got so far, I’m sure you understand the need for self-service analytics, and how Imply can fulfil that need.
So all you need to do is give Imply a try for free!
Lastly, I’m sure you’re eager to know more, so stay tuned for the next post in this series, where I’ll dive deeper into a few use-cases (including the technical details), and in the meantime - please share your feedback with us at firstname.lastname@example.org.