Podcast interview with Ralf Becher, TIQ Solutions

In the past couple of months I have had the opportunity to have a number of conversations with people about the role, similarities and differences between the world of graph databases like Neo4j, and the world of Business Intelligence. One of the people that has been instrumental in many of these conversations has been Ralf Becher, of TIQ Solutions in Germany. Ralf has done some super work integrating BI solutions like Qlikview and Tableau with Neo4j, and that's one of the reasons for inviting him to our podcast. So here goes:

Here's the transcript of our conversation:

RVB: Hello everyone. My name is Rik Van Bruggen from Neo Technology. And here I am again recording another session of our Neo4j Graph Database podcast. It's another remote session. I'm here in cloudy Antwerp. I'm joined here today by Ralf Becher from TIQ Solutions in Germany. Hi Ralf. 

RB: Hi Rik. 

RVB: Hey. It's great to have you on the podcast. We've been on the road a couple of times doing meet-ups. Today is another good occasion to talk about a great topic. But maybe first, you can introduce yourself, Ralf. What's your background? 

RB: Yes. I'm Ralf Becher. I'm working as a management director for TIQ Solutions. We provide professional service in the field of data management, big data, and business intelligence. Basically, I work also as a principal consultant in system audio tech. That's basically it. 

RVB: Yeah exactly, so as I understand, you guys have been doing a lot of work around business intelligence work with products like Tableau and QlikView and those type of things, right? 

RB: Yeah right, that's our main business at the moment, bringing business intelligence solutions to our clients. 

RVB: Yeah, and then you've been integrating that with Neo. Is that what I've seen? 

RB: Yeah right, a few years ago we've developed a JDBC connector for QlikView and we looked around about interesting big data or no SQL data sources and then I came across Neo4j. And I was very impressed about the Cypher query language we can then leverage to pull in interesting data in BI assessment. 

RVB: So for those of you that are listening, Ralf has some really great presentations and demonstrations around how you can integrate Neo4j with these BI tools like QlikView and Tableau and stuff like that. But, Ralf, maybe I can ask you a question. What's attracted to you to graph databases and to Neo4j specifically? What do like about them most? 

RB: Yeah, I would say it's a more natural flexible approach for data modeling if you're using your graph because you can represent the aspects of the reality in a better way. So if you consider organizations or business processes and business rules as a graph, then it's much more hands on to use it as a graph database and to use it as a model. 

RVB: Okay. So that's more representing reality like it is. 

RB: Yeah, right. You can leverage more complex aspects than with tables in a relational world. So if it considers small devices, all those machine to machine communication or IOT business, so it's kind of a more model integration route would be needed in the future, so why not use this model data management? 

RVB: Is that something that you've lived as a problem in the BI world as well? Is that something that people struggle with, you think? 

RB: Yeah, not only in the BI world, in the whole classical database world. So if you start modeling problems with your own model also, then you come to a complexity where it's hard to build or to represent the real problem with data and then at the end you end up with a variance in performance solution or a lot of joints and so on. 

RVB: So it becomes messy, right? I've heard that from a lot of people on this podcast as well. The model is really so powerful and so interesting for representing complex domains, I think, right? 

RB: Right. And you can easily extend it to new aspects, new insights. You don't need to update your tables or your model earned end relations, so you just add some more notes or more relations. It's more like an evolution approach model. 

RVB: I've seen you do your presentations and your demonstrations, and you talk a lot about some use cases. What are some of the use cases that you've been working on? 

RB: The moment for all those meet-ups, we've prepared a fraud detection use case, where we want to try to detect fraudulent behavior in retail purchases in online stores. You can show how easy you can discover synthetical identities or fraud rings or suspects in this kind of business. So a graph database like Neo4j helps a lot. 

RVB: It's fantastic. Okay, thank you. Maybe one more question Ralf, if you don't mind. Where do you see this going? Where do you see the worlds of graph databases and maybe BI tools converging in the future? You have any perspectives on that? 

RB: Yeah, I see graphs as an addition and layer on top of BI so it gives you the capability to model more complex scenarios and get more insights from your data and, finally, it's made for better decisions at the end. 

RVB: So it's like an extension of the current BI in use, would you say? 

RB: Right, I see still both worlds evolving in the future. But probably they will overlap a little bit more and come together, and you have strengths and weaknesses in both worlds, but let's just use the strength of all together under one roof. 

RVB: That's a great perspective. Thank you so much, Ralf [crosstalk]. I think we're going to wrap up here. We want to keep these podcasts short and sweet. So thank you for joining us today. For everyone who wants to know more about this topic, I'll put the links on my blog post, TIQ Solutions in Germany. You can find it through Google. And thank you so much, Ralf, for joining us. 

RB: Thank you, Rik. Have a nice day. 

RVB: Thank you, bye. 

RB: Bye.

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All the best

Rik