Jim Webber Picture

Jim Webber

Chief Scientist, Neo4j

Jim Webber is the Chief Scientist at Neo4j working on next-generation solutions for massively scaling graph data. Prior to joining Neo4j, Jim was a Professional Services Director with ThoughtWorks where he worked on large-scale computing systems in finance and telecoms. Jim has a Ph.D. in Computing Science from the Newcastle University, UK.


Latest Posts by Jim Webber

Learn everything you need to know about Neo4j 4.0 graph database.

Introducing Neo4j Graph Database 4.0 [GA Release]

I feel particularly pleased to be able to announce the GA release of Neo4j Graph Database 4.0. I've been with the Neo4j codebase since 2009 and have to tell you: 2009 Jim Webber couldn't imagine the way 2020 Neo4j would look. “2009 me” was building a REST API (if you've ever seen... read more


Check out this rapid retail example of Cypher query code for graph data recommendations.

Powering Recommendations with a Graph Database: A Rapid Retail Example

It’s one thing to say that Neo4j streamlines real-time recommendations; it’s another to show you the code so you can see for yourself. In this series, we discuss how real-time recommendations support a number of different use cases, from product recommendations to logistics. Last week,... read more


Discover how real-time recommendations support a number of different use cases that translate into business value.

Powering Recommendations with a Graph Database: Proven Business Benefits [+ Case Studies]

Relevant, real-time recommendations drive revenue, but they are challenging to deliver. That’s because good recommendations require bringing together so much data, surfacing the relationships between all that data and delivering just the right suggestion in context and in the moment. In... read more


Learn how graph database technology powers recommendation engines with connected data

Powering Recommendations with a Graph Database: Connect Buyer and Product Data

Effective recommendations increase revenue and drive up average order value. But delivering highly relevant, real-time recommendations requires as much context as possible. Connecting the user to the perfect recommendation is an art. In this three-part series, we explore using recommendations to... read more


Learn why the distinction of native graph databases matters when it comes to connect data

A Note on Native Graph Databases

It’s fun to watch the graph database category evolve. From being a seemingly niche category a decade ago (despite the valiant efforts of the Semantic Web community) to a modest – but important – pillar of the data world as it is today. But as the category has grown, we see some... read more


Learn how Neo4j's engineering evolved into the scalable native graph database that it is today

The Engineering Evolution of Neo4j into a Native Graph Database

Hi everyone. My name is Dr. Jim Webber. I'm Neo4j's chief scientist, and I'd like to take a few minutes to talk to you about Neo4j's evolution from an engineering point of view. Our Origin Story: Overcoming Complicated SQL Queries I don't know if many people know this, but Neo4j actually... read more


Learn why the native graph database distinction matters and how Neo4j grows with hardware capacity

The Co-Evolution of Computer Hardware & Graph Databases: Why Graph-Native Matters

Neo4j is a native graph database, which means all of the code – from the web browser to the drivers, Bolt protocol, page cache, cluster protocols, disk storage format — is optimized for graph workloads. As hardware advances come along, we’re uniquely positioned to adapt our software design to... read more


Watch Jim Webber’s Very Sciency Keynote Presentation on the Graph Database Landscape

Trekking Boldly into the Future: Going Who Knows Where in the Graph Space

Editor’s Note: Last October at GraphConnect San Francisco, Jim Webber – Chief Scientist at Neo Technology – delivered this very sciency closing keynote on the graph database landscape. For more videos from GraphConnect SF and to register for GraphConnect Europe, check out... read more


Learn More about the Competitive Advantage that Graph Databases Lend to Your Enterprise

The Competitive Advantage of Graph Databases in the Enterprise

“Big data” grows bigger every year, but today’s enterprise leaders don’t only need to manage larger volumes of data – they critically need to generate insight from their existing data. So how should CIOs and CTOs generate those insights? To paraphrase Seth Godin, businesses need to... read more


Learn More about the Graph-Based Search Use Case of Graph Databases in the Enterprise

Graph Databases in the Enterprise: Graph-Based Search

Graph-based search is a new approach to data and digital asset management originally pioneered by Facebook and Google. Search powered by a graph database delivers relevant information that you may not have specifically asked for – offering a more proactive and targeted search experience,... read more


Learn More about the Identity and Access Management Use Case of Graph Databases in the Enterprise

Graph Databases in the Enterprise: Identity & Access Management

Identity and access management (IAM) solutions store information about parties (e.g., administrators, business units, end-users) and resources (e.g., files, shares, network devices, products, agreements), along with the rules governing access to those resources. Identity management solutions... read more


Learn More about the Network & IT Operations Use Case of Graph Databases in the Enterprise

Graph Databases in the Enterprise: Network & IT Operations

Graph databases are, therefore, an excellent fit for modeling, storing and querying network and IT operational data no matter which side of the firewall your business is on – whether it’s a communications network or a data... read more


Learn More about the Master Data Management Use Case of Graph Databases in the Enterprise

Graph Databases in the Enterprise: Master Data Management

Master data is the lifeblood of your enterprise. The umbrella of “master data” includes vital data such as: Users Customers Products Accounts Partners Sites Business units Many business applications use master data and it’s often held in many different places, with lots of... read more


Learn More about the Real-Time Recommendation Engine Use Case of Graph Databases in the Enterprise

Graph Databases in the Enterprise: Real-Time Recommendation Engines

Whether your enterprise operates in the retail, social, services or media sector, offering your users highly targeted, real-time recommendations is essential to maximizing customer value and staying competitive. Unlike other business data, recommendations must be inductive and contextual in... read more


Learn about the Fraud Detection Use Case of Graph Databases in the Enterprise

Graph Databases in the Enterprise: Fraud Detection

Banks and insurance companies lose billions of dollars every year to fraud. Traditional methods of fraud detection fail to minimize these losses since they perform discrete analyses that are susceptible to false positives (and false negatives). Knowing this, increasingly sophisticated fraudsters... read more


Listen to Jim Webber’s Keynote Presentation on the History and Future of Graph Data Technologies

Impossible Is Nothing: The History (& Future) of Graph Data [GraphConnect Recap]

Editor’s Note: Last May at GraphConnect Europe, Jim Webber – Chief Scientist at Neo Technology – gave this inspiring (and nerdy) keynote talk on the history and future of graph data. Register for GraphConnect San Francisco to hear more speakers like Jim present on the emerging world of... read more