Neo4j is for the Non-Technical

Neo4j unifies organizations across departments and across teams, both technical and non-technical, enabling a greater level of understanding and clarity in communication than previously possible. A Neo4j graph model is whiteboard friendly and allows everyone from business to engineering groups to speak the same language of connections. Communicating in contextually relevant connections that bring together business concepts reduces the potential for misunderstandings that cause delays and rework later.

Neo4j Connects Your Organization by Connecting Your Data

The world today is highly connected. Graph databases are whiteboard friendly and effective in mimicking erratic and inconsistent relationships through intuitive means. They help provide insights and understanding by creating connections within complex big data sets. As enterprises become increasingly data driven it is essential that all individuals, especially the non-technical groups have the ability to collaborate with engineering in a more integrated fashion. Neo4j removes the intimidation factor of technology typically required to deal with complex data and enables more unified collaboration because we all can relate to connections.

There a number of reasons why both technical and non-technical teams within an organization could all agree on Neo4j:

• It offers incredible performance
  The more connected data gets in typical RDBMS and NoSQL databases, the faster performance query degrades. It’s fact that data within all organizations is growing rapidly in size and connectedness. Neo4j provides constant time navigation through your connected data whether your one level deep or ten levels deep.
• It guarantees data reliability

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Graph Advantage: Research Organizations

Many enterprises today build their business around research that involves piecing together meaningful data from the public domain for their customers. When trying to connect data across a domain in a meaningful way building around a graph database is a great tool because it models very well exactly how the business analysts at these research organizations are piecing together the real-world data they are finding during their research.

A business analyst may begin with one person and from there, move to the company they’re working for and then shifting to colleagues before moving on to places where their current colleagues previously worked, before finally settling for their past colleagues. Suddenly the business analysts has nearly finished building out an intuitive network of complex connections around this person of interest which would have been challenging and time consuming to try to represent in Excel.

Graph Database in Research Organizations

Research organizations are more than just managing large data volumes, their core goal is finding understanding that comes through research to gain insight of the available data. To properly leverage data relationships, a research organization requires a database technology that houses data relationship as a first-class entity.

As a native graph database, Neo4j provides several essential advantages for businesses today:

• Neo4j structures data connections precisely as they exist in the world around us with the contextually specific connections between entities as primary entities that can be explored in constant time.
• Real-time results for queries that are exploring the many different and complex connections around and 

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Graph Advantage: Connected Enterprise

The connected enterprise is the new norm. Traditional chain paradigm with sequential and siloed operations lacking a connection between customer and factory is no longer cutting it. Today enterprises are excepted to be sufficiently in touch and aware of how to interact with each uniquely individual person they are fortunate to call their customer. Technologies and operational procedures are rapidly changing to enable information to be connected and taken together to drive decision making, direction, and interaction with the customer.

Connected Enterprise: Data Essentials

Connected data is the lifeblood of today’s enterprise. Yet, it’s frequently isolated in varying silos across an organization, with different accessibility, redundancy, quality, and varying data formats. Managing connected data involves identifying, cleaning, storing, and governing increased data volumes within an enterprise. Connected data involves essential information such as customers, users, products, services, sites, and business units.

Adequate practices for connected data management differ along a wide range of approaches. On one end, many believe that connected data should be united in one location; while on the other end, some recommend managing data assets from one application or service, even if information is housed in multiple locations.

In both cases, data architects require a data model that’s versatile and fluid when exceptions arise and business needs change. And the only model that can answer this is the graph database.

Data Management and Graph Databases

Enterprises today are flooded with “big data”, a majority of which is master data. Dealing with

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Every Organization Needs a Knowledge Graph

A knowledge graph as it relates to individual organizations is a unification of information across that organization enriched with contextual and semantic relevance. Introducing a knowledge graph creates a comprehensive and baseline set of knowledge accessible by personnel, applications and customers alike to gain understanding and drive actions and direction.

This foundational knowledge graph is not only useful for people and applications, but provides a relevant and evolving dataset for sophisticated learning and intelligence software systems to utilize in providing personalized internal guidance as well as highly engaging interactions with customers.

Knowledge Sharing Falling Short

To engage all personnel in collaboration and knowledge sharing, a majority of organizations today have adopted social networking trends and offering different kinds of internal tools. However, such applications can generate large volumes of unstructured organization data stored in isolated systems across an organization. This attempt at creating a holistic understanding falls short because all this knowledge sharing and information isn’t actually being connected together.

The main result from this approach is a complex infrastructure containing data silos filled with duplicated, expired, and redundant information. This makes it hard to see the right information and acquire important insights. Organizations today need a graph data platform to support increasingly complex data management needs; deal with information flow, data infrastructure and communication problems; and allow next-generation systems to effectively seek, share, filter, and review data.

Knowledge Graph: Understanding and Growth

By embracing the nuanced complexities, semantics and contextual connections within an organization, a knowledge graph can be a catalyst for understanding and growth. The diverse and complex aspects of an 

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Master Data Management with Neo4j: Merging Two Financial Institutions

Master Data Management (MDM) is an increasingly complex topic for organizations today. The rate at which data in an enterprise to is flowing and evolving as a business asset, requires a the need for a more flexible and connection-centric master data storage solution.

This simple demonstrations shows some the benefits of a schema-free flexible graph model that treats relationships as first class citizens.

Master Data Management, is a practice that involves discovering, cleaning, housing, and governing data. Data architects for enterprises require a data model that offers ad hoc, variable, and excellent structures as business needs are constantly changing. This rapidly changing model ideally fits with a graph database.

Read more: https://www.graphgrid.com/graph-advan...

Better Insights from Your Master Data - Graph Database LA Meetup Demo

Master Data Management, is a practice that involves discovering, cleaning, housing, and governing data. Data architects for enterprises require a data model that offers ad hoc, variable, and flexible structures as business needs are constantly changing.

We'll be discussing the benefits of using the Neo4j graph database for Master Data Management including the flexible schema free data model, concepts of layering in data, keeping your data current and flowing and then the benefits of connected data analytics and real-time recommendations that can result.

An overview of MDM with Neo4j https://www.graphgrid.com/graph-advan...

Neo4j Integration with ElasticSearch - Elastic User Group LA Meetup Presentation

Complimentary technologies are very important as it's rarely possible for a single technology to be optimized for everything. There are always trade-offs - jack of all trades master of none also applies in technology solutions: https://www.graphgrid.com/jack-of-all....

In this increasingly polyglot world of data storage options one natural pairing that has proven quite effective is Neo4j and ElasticSearch. Neo4j is designed for optimal graph traversal (i.e querying highly connected data) and ElasticSearch is a powerful search server with great language analyzers and aggregation strategies out of the box. While you could try to rebuild the search capabilities in Neo4j because it's built on Lucene, you'd be relaying much of the foundation you already have in ElasticSearch with a subpar result and similarly while you could try to represent connected graph structures in ElasticSearch you'd be expending much effort with short comings in reliability due to the underlying storage mechanisms compared to using Neo4j for that connected data aspect. In this talk we'll look at paring them together and reaping the benefits of both doing what their best at.