Welcome to the 2019 Predictive Analytics World (PAW) Conference Series. Recorded live in Las Vegas, this series is bringing interviews straight to you from exhibitors and speakers at this year’s event. In this interview, host Jamin Brazil interviews Brian Shindurling, VP of Marketing at Big Squid. Find Brain Online: Email: bshindurling@bigsquid.com LinkedIn Big Squid [00:02] Hi, I’m Jamin, and you are listening to the Happy Market Research Podcast. We are live at Predictive Analytics World, Marketing Analytics World, and many other worlds. My guest right now is Brian, VP of Marketing at Big Squid. You guys have the coolest booth, I think, on the show floor by the way. [00:20] Oh, thank you. [00:20] The hot pink's great. I have a kick-ass sticker that I’ve added to my bag. Thanks very much for the tchotchke stuff. [00:26] Absolutely. [00:27] Tell me a little bit about the company. [00:29] Cool, so, we’re Big Squid. We’re based in Salt Lake City, Utah. We’ve been around for ten years actually. The company’s kind of evolved out the marketing and analytics world. We’ve done a lot of business intelligence consulting in our years past. And, really, kind of the genesis of where we are today was we were consistently hearing a lot of the same challenges with our customers where leveraging business intelligence for an analytics environment, you’re building really interesting and cool dashboards all the time, but you’re always looking at data in a historical context, which led our customers to the next questions, which is “What’s going to happen? “Why is it happening?” and “What can I do about it?” [01:07] I mean that’s the Holy Grail. [01:09] Absolutely. That’s right. So, a couple of years ago, we launched our product we call Kracken. It’s an automated machine-learning platform. [01:16] Bad-ass name. [01:17] Thank you, thank you. And the approach that we’ve taken is to integrate with the analytics infrastructure that BI analysts are using every day. So, most of your enterprise data warehouses that are on the market today, most of the major business intelligence platforms... We’re able to round-trip data in and out of those environments where we’re basically just enhancing it with predictive metrics to give analysts a little bit better idea of what’s going to happen. [01:39] That’s really cool. What kind of data are you dealing with? [01:42] It depends. We deal with all kinds of different data. We’re kind of a horizontal play. We work with companies across basically any vertical that you can think of. The data that we play with is always structured, again coming out of kind of that business intelligence environment. [01:57] So it’s been cleaned. [01:58]
Welcome to the 2019 Predictive Analytics World (PAW) Conference Series. Recorded live in Las Vegas, this series is bringing interviews straight to you from exhibitors and speakers at this year’s event. In this interview, host Jamin Brazil interviews Brian Shindurling, VP of Marketing at Big Squid.
Find Brain Online:
Email: bshindurling@bigsquid.com
Big Squid
[00:02]
Hi, I’m Jamin, and you are listening to the Happy Market Research Podcast. We are live at Predictive Analytics World, Marketing Analytics World, and many other worlds. My guest right now is Brian, VP of Marketing at Big Squid. You guys have the coolest booth, I think, on the show floor by the way.
[00:20]
Oh, thank you.
[00:20]
The hot pink's great. I have a kick-ass sticker that I’ve added to my bag. Thanks very much for the tchotchke stuff.
[00:26]
Absolutely.
[00:27]
Tell me a little bit about the company.
[00:29]
Cool, so, we’re Big Squid. We’re based in Salt Lake City, Utah. We’ve been around for ten years actually. The company’s kind of evolved out the marketing and analytics world. We’ve done a lot of business intelligence consulting in our years past. And, really, kind of the genesis of where we are today was we were consistently hearing a lot of the same challenges with our customers where leveraging business intelligence for an analytics environment, you’re building really interesting and cool dashboards all the time, but you’re always looking at data in a historical context, which led our customers to the next questions, which is “What’s going to happen? “Why is it happening?” and “What can I do about it?”
[01:07]
I mean that’s the Holy Grail.
[01:09]
Absolutely. That’s right. So, a couple of years ago, we launched our product we call Kracken. It’s an automated machine-learning platform.
[01:16]
Bad-ass name.
[01:17]
Thank you, thank you. And the approach that we’ve taken is to integrate with the analytics infrastructure that BI analysts are using every day. So, most of your enterprise data warehouses that are on the market today, most of the major business intelligence platforms... We’re able to round-trip data in and out of those environments where we’re basically just enhancing it with predictive metrics to give analysts a little bit better idea of what’s going to happen.
[01:39]
That’s really cool. What kind of data are you dealing with?
[01:42]
It depends. We deal with all kinds of different data. We’re kind of a horizontal play. We work with companies across basically any vertical that you can think of. The data that we play with is always structured, again coming out of kind of that business intelligence environment.
[01:57]
So it’s been cleaned.
[01:58]
It’s been pretty well curated, pretty well cleaned. This is data that has been used or is being used for reporting on a day-to-day basis. So we’re lucky in that sense; there’s already been some thought behind the business questions that we’re trying to support with analytics. Yeah, structured data and set up in a way that it’s being used in reporting environments.
[02:18]
Who is an ideal customer?
[02:20]
Good question. So, our ideal customer is the BI-analyst or data engineer, those that are leveraging these platforms like a Snowflake and/or a Tableau, Looker, Click (Places where they’re leveraging data on a day-to-day basis to derive insights and then reporting on and telling stories to their executive stakeholders about what’s happening in the business and what do we think is going to happen, how should we be thinking about making things better. But they haven’t really been classically trained on data science and machine learning in the past. So what we’ve done is we’ve created a platform that enables them to very easily navigate towards that concept that Gartner calls a “citizen data scientist.” So,