How We Built Our No-Jerk Culture
Some jerks never ask questions. They don’t ask how your day is going. They don’t ask for your input on a project. They certainly don’t ask you for permission to eat your yogurt in the office fridge that clearly had your name written on the label. These jerks have an obvious deleterious effect on morale.
Hard Problems We Like
You can’t throw a stone these days without hitting an article about “big data.” There’s endless advice on how you can be more “data-driven” (we prefer “data-informed”); how data can drive you and your business to infinite success; how X or Y analytics product will make your data dreams reality.
Interana Friends & Family Program: Introducing a new, exclusive way to experience Interana.
If you know about Interana you probably know it’s the interactive behavioral analytics solution that some of the biggest and most innovative digital businesses like Bing, Tinder and Sonos rely on to sustain a data-informed culture. These companies use Interana because it’s the only way to give everyone in their organizations the speed, scale and flexibility to ask any question about the behavior of people or things, and get answers back in seconds – even up to the billions of searches, swipes, and song plays that they deliver every day.
Now It’s an Open Secret – Try Our Live Demo!
When I learned about Interana a year ago I was blown away. I’ve been working with systems giving access to event data since 1998 at MSN yet knew nothing about it. Turns out, Interana’s the Holy Grail I’ve been searching for. Because Interana is visual, it really lets anyone run queries fast on event data…
What’s New in 2.21
Hello Interanians! It’s been a while – you’ve probably been wondering what we’ve been up to the past few months. The answer is: a lot! We are super excited to announce the release of 2.21 – this is our most feature-packed release in a while, which is why it took us a little longer to…
Quick Thoughts on Filters
In this blog, I’m going to talk about filters within metrics and within explorer by using a very simple dataset as shown below: My dataset has 11 events comprised of two user_ids (my shard key) with several different event_names. All events happened between 5AM and 5:15AM PDT. I’m going to create a metric to count…