Partnering With Neo4j to Meet Unmet API Security Needs
In this interview, Phil Meredith, CEO & Founder of Process Tempo and ReactFirst, and Dan McGary, Senior Account Executive at Neo4j, sit down to discuss the benefits of graph technology, the Neo4j Startup program, Graph Data Science, and the gaps that the combined power of Neo4j & ReactFirst is helping to plug in the API Security Space.
Dan: My name is Dan McGary, I'm a Senior Account Executive here at Neo4j and I am based out of New Hampshire. I've been with Neo4j for about three and a half years now, and I've been working with Phil and the Process Tempo team for several years, specifically on helping power the ReactFirst solution with Neo4j's graph database technology. Phil, how are you doing today? Phil: Fantastic! Thanks for joining us.
Dan: Phil, can you tell us why you chose Neo4j so early in your process, and what exactly led you to choose Neo4j?
Phil: If you can believe it, the first time I started looking into Neo4j was 10 years ago. It was 2012, and I thought that it was a really neat technology. I saw some of it in those early days. I saw some of its potential, and as a technologist, and as a data geek, I just liked looking into new things. I remember I was with a client, and they were struggling with a certain use case they were implementing. They asked me to come in and help them understand what they were doing wrong. I remember sketching it out, and I'm like “Wow, this is a graph!” as I sketched out the products and the services and the other challenges they were having.
I instantly thought you know what, I got to take this and build solutions on it. That was 2012. Fast forward to 4 years later, when I finally got the nerve to start up a company, I wanted to build technology on that capability.
One of the first things I really liked was the fact that Neo4j had the Startup program. They were very developer-centric, and it was perfect for us. At the time, we were a very small crew, developer heavy, as we were trying to develop the product. The support, and the program itself from Neo4j, really helped us catapult our initial stages of the company. So that was huge for us. So from a partnership perspective, you guys gave us everything we needed to really kind of get off the ground quickly.
"The support, and the program itself from Neo4j, really helped us catapult our initial stages of the company. That was huge for us. From a partnership perspective, [Neo4j] gave us everything we needed to really get off the ground quickly."
Of course, the graph is also really cool. And the graph flexibility was something I really liked. Someone in my industry, at the level of my career at that time, was 20-something years of writing SQL queries and designing entity-relationship diagrams, I did not want to build a company where I'd have to do that ever again. So I threw out the SQL, brought in the cypher, brought in the graph, really brought in that aspect of it too.
Fundamentally, the flexibility of the platform, of the graph, was important because we didn't know what the end product was going to look like at that time. We didn't know what use cases our customers would throw at us. To have something very rigid and very design-heavy was a non-starter. We wanted to be agile, and the graph allows us to be agile. Lastly, the development of the graph data science library really cool. That was a clear differentiator for us and continues to be a differentiator for us, so that’s why we made the leap [to Neo4j].
Phil: Why don't you tell us a little bit about Neo4j and where you guys are going?
Dan: Great segue there. You mentioned the developer. If you hear our co-founder Emil Eifrem speak, he's always talking about the commitment to the developer. We are a very developer-first organization, so that goes through the whole organization. So a little bit about Neo4j:
We were founded in Malmo Sweden over a decade or so ago, so we've been at this game for a very long time. Our official headquarters now is in Silicon Valley, so we're pre-IPO at this point. You know when you think of databases: we are the category creator of the graph database market space. We created the property graph model. And as such, we’ve become the thought leader. Organizations like Gartner and Forrester look to Neo4j to find out what's going on the graph. Where are things today, and where are things going in the future.
"We created the property graph model. And as such, we’ve become the thought leader. Organizations like Gartner and Forrester look to Neo4j to find out what's going on the graph."
You were mentioning the graph data science library. That's something that we started tooling several years ago, and now it represents about 25% of our business. So we’re continuing to grow from not only a technology standpoint, but as a thought leader, and as such, the market has responded. We’ve become what we call the commercial leader. We have thousands of customers subscribing and partnering with Neo4j. We have thousands of clients who trust Neo4j for their mission-critical applications. And we like to brag a little bit that we've got about seven hundred plus enterprise customers. So these are very large organizations that trust Neo4j with their mission-critical applications.
So what does that mean? It means eight of the top 10 insurance companies trust Neo4j for fraud detection and claims applications. 20 of the top 25 Banks trust Neo4j again for fraud detection, customer 360, KYC, the use cases that are around relationships and in understanding where those relationships are. Seven of the top 10 retailers use Neo4j for customer 360, recommendation engines, fraud detection.
8 out of the top 10 Insurance companies trust Neo4j. 20 of the top 25 banks trust Neo4j. 7 of the top 10 retailers use Neo4j. And 7 of the top 10 software companies leverage Neo4j.
I work with a lot of healthcare organizations that use neo4j for the patient journey - so understanding the journey of a patient as they go through the healthcare system and to leverage Neo4j to understand how to approve that experience on the customer side, reduce fraud on the back of the transaction, and then also increase effectiveness and efficiency while reducing costs and reading admission rates and things of that nature.
Then lastly, I’d like to point out that seven of the top 10 software companies leverage Neo4j, so these are organizations that leverage Neo4j to modernize their new or existing technology stocks that they actually sell to their clients. So companies like Cisco or Adobe leverage Neo4j for that. I've had the experience my three and a half years here working with small-medium businesses and medium-sized Enterprises where I’ve worked with a lot of organizations and had the opportunity to show them how Neo4j can really modernize their tech stack. Even startups who are building their applications from the ground up like Process Tempo.
So it's been very exciting since I've been here. Three and a half years seems like 10 because we've just grown exponentially. I was employee number for 150 give or take, and three and a half years later we’re over 600. Last year we received the largest VC funding in database history. $325 million investment series F funding as we go pre-IPO. Lastly, we support all different verticals, all different industries, small and large, and it's been great working with you feel over the past year-and-a-half two years as you've built your company. I really appreciate you trusting Neo4j and the technology in the organization in building a partnership with us.
Phil: Hitching our wagon Neo4j was a smart decision back then and any organization, any developer listening to this webinar right now should be comfortable with making a similar decision.
Phil: Always on my mind are the Graph Data Science (GDS) capabilities, the recommendation engine capabilities. Can you tell us where that sits today?
Dan: So GDS, like I mentioned before, is one of the fastest-growing areas for Neo4j today. We literally wrote the book on graph data science. We've got several books out there explaining the graph data science library. We've got a GDS for Dummies book, which is a fantastic easy quick way to learn about GDS. And at the end of the day, Neo4j as a database is all about the relationships between the data. We like to say that the relationships between the data are as important as the data itself.
"We like to say that the relationships between the data are as important as the data itself."
GDS is basically an advanced set of algorithms for folks who are building applications, whether internal or software, for organizations like Process Tempo to really accelerate time-to-market. So it's all about these algorithms fast-tracking and creating best practices around the development. We’ve pre-built these algorithms so they're fully tested, they’re fully functional, they’re in mission-critical applications, across all the different use cases and they're fully supported. So as folks look to build applications on Neo4j as a technology they can trust that the GDS algorithms are time-tested, battle-tested, and you know they're going to work.
So where it fits with you guys is the relationships between APIs. The relationships between the APIs can help uncover those obscure patterns and understand and identify if there are duplicate efforts, or vulnerabilities. And you know we like to say you can't secure what you can't find. GDS makes it really simple and really effective and really efficient. On the development side - I spoke to this earlier - but it just really accelerates development by leveraging these common pre-built algorithms so that folks who are building applications or modernizing tech stacks can get to production quicker, helping speed up time to market and product delivery.
GDS really accelerates development by leveraging common pre-built algorithms so those building applications or modernizing tech stacks can get to production quicker, helping speed up time to market and product delivery.
Part 2 of this interview coming soon!
Watch the full webinar here. Enter passcode: Kg=k2N$g to view.