Welcome to the MRMW NA 2019 Conference Series. Recorded live in Cincinnati, this series is bringing interviews straight to you from exhibitors and speakers at this year’s event. In this interview, host Jamin Brazil interviews Rudy Bublitz, Director of Digital Taxonomy. Contact Rudy Online: LinkedIn Rudy@DigitalTaxonomy.co.uk Digital Taxonomy [00:00] My guest is Rudy with Digital Taxonomy. Digital Taxonomy.co.uk is name of their website. I hope you’ll check them out. It’s really an interesting combination of AI and human judgment to transform unstructured text into actionable insights. I was impressed with their framework for how to derive and pull out high quality in that really-becoming-a-crowded space. I hope you’ll check it out. [00:30] So, my guest today is Rudy with Digital Taxonomy. Tell us a little bit about Digital Taxonomy. [00:36] We are a software provider. We have two products in our portfolio. We focus on verbatim coding within the survey research context and that includes traditional verbatim coding where humans are employed. But our application tries to make best use of both a natural language processing, text analytics, sentiment analysis as well as a machine learning capability to try to automate the things humans do. We don’t want to replace humans; we just think it’s... you know we can optimize their performance by making best use of these tools that have been around for a while, trying to work that into a human interface. So, that’s the real struggle. [01:19] So, talk to me a little bit about how you guys are different. There’s a couple of different text analytics companies out there in the market research space. [01:26] A couple? [01:27] Yes. [laughter] [01:28] And all the freebies and all the … [01:30] Right, and then you have AWS, you know what I mean, at scale. [01:32] Yeah, you can go out, and you can write things in R, and you can do a lot of things on your own. But if you’re going to create an interface that humans are going to interplay with those things and control them – those different technologies, that’s a bit harder. The human interface is really the most important piece because you have to have ways for a lot of people in an organization to use the software, not just specialists. A lot of this stuff... There’s a roomful of three or four specialists that are in charge text analytics, machine learning, and they go off and do their thing. We’re trying to create an application that can be used by analysts, by coders, by DP staff, by end-customers, everyone. [02:12] What is one of your favorite projects that you guys worked on? [02:16] Oh, I’ve done a bunch of work. I’m kind of a Londonphile, and I’ve done a bunch of work on London hotels and restaurants, massive numbers of reviews. So, I’ve come to know the restaurant and hotel industry quite well in London, which is very helpful. Many of these are the very posh places. Using the text analytics and a combination of a little bit of human as...
Welcome to the MRMW NA 2019 Conference Series. Recorded live in Cincinnati, this series is bringing interviews straight to you from exhibitors and speakers at this year’s event. In this interview, host Jamin Brazil interviews Rudy Bublitz, Director of Digital Taxonomy.
Contact Rudy Online:
Rudy@DigitalTaxonomy.co.uk
Digital Taxonomy
[00:00]
My guest is Rudy with Digital Taxonomy. Digital Taxonomy.co.uk is name of their website. I hope you’ll check them out. It’s really an interesting combination of AI and human judgment to transform unstructured text into actionable insights. I was impressed with their framework for how to derive and pull out high quality in that really-becoming-a-crowded space. I hope you’ll check it out.
[00:30]
So, my guest today is Rudy with Digital Taxonomy. Tell us a little bit about Digital Taxonomy.
[00:36]
We are a software provider. We have two products in our portfolio. We focus on verbatim coding within the survey research context and that includes traditional verbatim coding where humans are employed. But our application tries to make best use of both a natural language processing, text analytics, sentiment analysis as well as a machine learning capability to try to automate the things humans do. We don’t want to replace humans; we just think it’s... you know we can optimize their performance by making best use of these tools that have been around for a while, trying to work that into a human interface. So, that’s the real struggle.
[01:19]
So, talk to me a little bit about how you guys are different. There’s a couple of different text analytics companies out there in the market research space.
[01:26]
A couple?
[01:27]
Yes. [laughter]
[01:28]
And all the freebies and all the …
[01:30]
Right, and then you have AWS, you know what I mean, at scale.
[01:32]
Yeah, you can go out, and you can write things in R, and you can do a lot of things on your own. But if you’re going to create an interface that humans are going to interplay with those things and control them – those different technologies, that’s a bit harder. The human interface is really the most important piece because you have to have ways for a lot of people in an organization to use the software, not just specialists. A lot of this stuff... There’s a roomful of three or four specialists that are in charge text analytics, machine learning, and they go off and do their thing. We’re trying to create an application that can be used by analysts, by coders, by DP staff, by end-customers, everyone.
[02:12]
What is one of your favorite projects that you guys worked on?
[02:16]
Oh, I’ve done a bunch of work. I’m kind of a Londonphile, and I’ve done a bunch of work on London hotels and restaurants, massive numbers of reviews. So, I’ve come to know the restaurant and hotel industry quite well in London, which is very helpful. Many of these are the very posh places. Using the text analytics and a combination of a little bit of human assistance, built basically my own Yelp for London for hotels and restaurants. You pick sort of the things that are important to you and the ratings of the restaurants. and I can show you selections in that category.
[02:51]
So, from a workflow perspective, how do companies interact with you? Do they provide you their unstructured data in like a file or are you usually part of a quantitative study? How does it...?
[03:07]
So, right now we license the software. So an agency will license the software from us. The data can come in from any form. And we have an open API as well; so, if it’s a consistent method for transporting data from tools like Decipher, from Askia, from Survey Monkey, anywhere, we can set up an automated process so that just flows, in the evening. We also have the ability to code in surveys; so,