ENCORE - Podcast transcription

RVB: 00:00:01.974 Hello, everyone. My name is Rik. Rik from Neo4j and here we are recording another special edition, I would call it, of a podcast that we've been doing; podcast series that we've been doing over the past month or so. It's the quest for graph value podcast series and of course, I have my wonderful partner in crime on the other side of this Zoom call, and that's Stefan. Hi, Stefan. Good morning.

SW: 00:00:33.480 Hello and good morning, Rik. Nice to be back. Today I'm off travelling, so I am recording from the [inaudible] office this time. So nice to see colleagues again.

RVB: 00:00:45.365 Definitely. Yeah. Yeah. Well, and it's a little bit of a special occasion this morning, right, because we're going to be talking-- a little bit of an unplanned--

SW: 00:00:52.207 Is it my birthday?

RVB: 00:00:54.292 What's that [laughter]?

SW: 00:00:55.294 Is it my birthday?

RVB: 00:00:58.028 No, it's not your birthday. It's a little bit of a special occasion because we didn't plan for this session really to happen this quickly after the graph value series. We really wanted to basically wrap that up. But I think it was two weeks ago or something like that, Neo4j together with the analyst firm Forrester Research published a really interesting study that I think both of us really felt was so relevant to this series, right? So this is the total economic impact study that Forrester did. It basically assesses the impact of a Neo4j graph data platform implementation based on some objective data. I'm assuming you've read it, Stefan?

SW: 00:01:50.646 Yes actually, for once, I did read it. I was super kind of energised about reading it. Also, a lot of the reasons why I joined Neo4j a couple of years ago were in there, as well. So I think it's a very, very good study. Of course, it is in collaboration with us so it would be weird if I did not like it or we had to do a lot better--

RVB: 00:02:21.448 Yeah.

SW: 00:02:21.440 --which we always can. But I think it's a good, really good study.

RVB: 00:02:25.278 Yeah. So to give a little bit more context here, this is a commissioned study, right? So it's something that we asked our friends at Forrester to analyse and obviously, that doesn't really make it a 100% objective, peer-reviewed type of study. So it's a bit different from that. But it's still really kind of-- it's a lot more objective than if a vendor would publish something like this themselves, right? But there's a couple of things that I wanted to highlight, and I wonder what your thoughts on this are. Did you see the people that they interviewed? I mean obviously, it's anonymous, but there is a section about the profiles of the people that they interviewed. Did you see that?

SW: 00:03:11.185 Yeah. First off, it was just such a great mix of the people we work with, right? Everything from a data architect, some senior software engineer, chief architect, and some sort of principal architect. And also, spanning across verticals, right? Banking, software, non-profit, financial services, and so on. I think that is also kind of a cool reflection. Did you pick up anything on that topic, by the way?

RVB: 00:03:41.067 I did. I think what's fascinating here is it's a study about the economic impact, right? And what these guys did - and it's actually something that we touched on in the graph value series in quite a bit of detail - they started talking to the practitioners, right? So they didn't talk to the business owners. No, no. They talked to the practitioners and then they went through this entire exercise of peeling the onion; of really trying to understanding, "Okay, you as a practitioner must really see the value of this graph data platform; but help us understand what the economic impact is." And they went through that journey, right? And that's why I thought it was such a relevant thing to follow up on on the graph value series, because that's what we've been talking about. It's taking them through that journey, taking them through that quest for graph value to better understand what is the economic impact, the quantifiable business value of a graph data platform. It's exactly that. Yes, so I did pick something up, Stefan [laughter].

SW: 00:04:52.713 No, no. I was just thinking the same. So it almost felt like when seeing this, I was like, "Do anyone shadow me? What are you doing, Forrester? Do you know what me and Rick did here?" Because it's exactly those kind of things, right? And taking that data and covering a lot of these topics that we've talked about, making the most unquantifiable thing [as?]-- the most fluffy buzzword around, most likely-- and I'm not talking about blockchain this time. I'm talking about digital transformation, right? What is it? How do you do it? And I know these companies-- because that's also a lot of what I actually previously did in my career, working with change management and trying to help organisations go from the McKinsey kind of report into rather start doing differently. For you that know me and my sessions, you might remember the one in Amsterdam when I talked about Yoda, and, "There is no try. Just do," right? So that classical thing, by starting doing differently we will also start to think differently. So we're going to actually stop-- we can't on one say, "We should not be a siloed organisation," and then run all our data in siloes, because the artefacts and the tools also shape our thinking. So I think that was kind of super cool, breaking it down as well.

RVB: 00:06:20.260 Yeah. I mean, we always think of-- I mean, whenever a user or a customer approaches us and talks about, "Why do we want to consider a graph data platform?" It always boils down to these three things: it's a very intuitive platform, it's a very flexible platform, and it's a very fast platform. That's kind of the summary, right? But then to quantify that, to make that really, really specific, the TEI study I think is really interesting there because it succeeds in taking those practitioners by the hand and then saying, "Okay, we're going to make these things quantifiable." And what they end up with, very often, is either a speed in development time - it takes less time to develop a piece of software for a particular application - and efficiency. We can run this a little bit more efficient, or much more efficient; with fewer hardware, fewer operating costs, those types of things. But they really succeed in making those unquantifiable, high-level buzzwords very specific. And I really love it, in spite of the fact that it's by no means perfect. I think this is really one of the fascinating things about this entire quest and this entire discussion: this is not a perfect study. It's far from it. It's not zero or one, it's not black or white. It's a lot of grey.

SW: 00:07:57.913 What? Binary? What happened? I cannot analyse. No, I'm joking.

RVB: 00:08:05.341 Yes, you can. Yeah.

SW: 00:08:06.786 Yes, you can. Yes, I am, as we learn. But I think this whole idea of the binary thing, right, especially how it's within practitioners, within the data field, how it's overly a way of thinking-- it's so often that happens at work. So then it's also-- of course, then rewrite the way we think. And Yoda-- everyone that knows me know how much I like to talk about these mental models, right? So I think this is such an interesting thing because it forces us to go out of that box and look upon the space in between. And I usually say my worst favourite quote, if that's a phrase you can use, from [inaudible], when he once said, "I am right and you are wrong." Making it very binary. But think of that: would you ever change your mind by somebody basically bullying you and saying you are wrong and they are superior to you?

RVB: 00:09:00.847 Yeah. Yeah.

SW: 00:09:01.307 That's not going to really change anything. So--

RVB: 00:09:04.520 Not going to change, yeah.

SW: 00:09:06.414 So stay in these kind of-- how can we talk about these hard things, or how can we stay in this ambiguity space and understand it and thinking about it, yeah.

RVB: 00:09:15.256 When I read the TEI study, I mean, it kind of dawned on me a little bit - and I think I knew this already - that there's a lot-- and we talk about this in the podcast series, as well-- the practitioners, the technical folks that love Neo4j and the graph approach to data problems so much, they have some kind of like inherent hesitancy or friction where they don't want to get started on these quantitative, economic evaluation studies. And I think what might be contributing to that is the fact that it's never going to be perfect. If you're a technical person, you like zeroes and ones. You like binary things. And this, by definition, cannot be binary. It's going to be grey. And I think that's mainly a source of friction, a source of reason why people don't want to get started with it. And it's also what I think-- what is one of the key takeaways for me and for us and for our community on this quest, is that we need to love the grey. We need to love the non-binary stuff. Not just in this domain, right?

SW: 00:10:46.607 It's in everything. Yeah. Yeah. It's a life thing, right? It's a human feeling, right? And looking back even to the great book of algorithms, as I call it, the Bible. If you live in a country where Christianity exists - you can do it with any other religion, I guess - it's a set of rules on how to behave. And the good part with these things are that it just removes those binary things because-- let's do a tragical example. I have a kid and the kid dies. I don't have a kid, and he hasn't died, but this is just an example. Then it would be a hard thing. "Why did this happen? This wasn't fair." But then it's very easy to make it very binary because it was the will of God, right? It removes that friction of uncertainty, ambiguity, and that space where you need to-- are forced to handle these things. Makes it into these kind of binary things. And looking upon society and in the rear-view mirror - hopefully, touch on wood - with COVID, I mean, we spent almost two years in front of Zoom calls. Not meeting with people, not working as usual, as human has evolved over time. And we see a lot in this on how we behave and how we start to adapt, basically, the conversational style of any messenger or commenting behaviour. It's a very binary approach. So I think it's not only the practitioner, even if they would most likely understand that they are, in that sense. But it's a very, very human thing. So I think it's a tricky thing for all of us. But as you said, we need to spend more time in the grey. And we're not talking about 50 Shades of Grey here [laughter].

RVB: 00:12:32.326 Well, on that bombshell, there's lots of lessons to be learned here. I think the Forrester study is super relevant and there's a lot of important data in there, but there's also a number of important lessons in there. I would highly recommend that all of our listeners and readers take a look at it. And obviously, we'll link it in the show notes. But for now, I think we'll wrap up. And I want to thank you, Stefan, for taking the time to discuss this with me and also share those wonderful life lessons.

SW: 00:13:16.648 Life lessons from a crazy person. But just going to second that. Just read it. It's awesome. And one thing that it actually handles, again, and I cannot even stress this enough, is this: how digital transformation can actually be fun as well as you save money and time. Right? It's just mind-blowing, right? It doesn't have to be fluffy. It doesn't have to be hard. It can be super fun. And you can also save money, which is kind of why I fell in love with Neo. So with the love of Neo, I think that's a good way to end this call. So nice talking to you, and see you soon, my friend.

RVB: 00:13:55.191 Talk to you soon, Stefan. Have a good one. Bye.