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 complex relationships between data points could be the biggest problem facing today’s enterprises.

The cost of a poor-performing data management system will affect an enterprise because data is constantly being shared, remixed, enhanced and connected. As a matter of fact, a majority of data management systems are created with a relational database, which aren’t even made for traversing connected data.

Yet, relationships in a data are essential to maintain competitive advantage with business analytics 

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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 organization’s business and operations are often well understood by a few subject matter experts that have gained extensive knowledge over their years of experience.

Providing a way for people and systems to connect and leverage a holistic perspective of this knowledge that exists perpetuates better insights and decision making by your subject matter experts because they 

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Graph Advantage: Network and IT Operations

Network and IT operations are increasingly complex in their distribution and operation. Data complexity is a function of structure, size and connectedness. It doesn’t take an organization long to

reach a point where non-graph databases just don’t keep up with constantly evolving components and topology of the network infrastructure. Network outages and failures are detrimental to any organization so being a step ahead of potential failures is a huge advantage.

Challenges in Network and IT Operations

Enterprises are facing an increasing number of challenges as network complexity continues to increase. Here are a few examples of such challenges:

• Network troubleshooting
  Regardless if it involves network changes, increasing security access or enhancing infrastructure usages, the interdependencies of the network elements involved are highly intricate, which makes it very hard to troubleshoot.
Cause-and-effect analysis
Relationships within different nodes in the network are neither hierarchical nor linear. This makes it challenging to quickly determine dependence of sub-groups of network elements on one another. The more systems being brought together, the more complex these relationships become and the more difficult it is to isolate the chain of failure.
• Expanding virtual and physical nodes
  With surging growth within the size of networks as well as the components included to support users and services, your enterprise’s IT team will have to create systems that make room for both future and current requirements.

Network and IT Operations Benefits from Neo4j

For any IT and network operations, the Neo4j graph database should be considered for the benefits it provides with flexible graph model, which more accurately represents the topology and

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Graph Advantage: Developer Productivity

Developer productivity is critical for any organization building software. There are many factors to consider ranging from effective communication with the business on requirements to ease of working

with the chosen technologies. When the chosen technology is new to the enterprise, understanding the ability to transition smoothly and build a quality solutions with the chosen technology are essential to confidently move forward.

I often receive questions from enterprises around the actual development impact of introducing a database technology that changes the paradigm for reading and writing data — albeit to one that has an immediate appeal for intuitively representing the connections we see in the world around us. About four and half years ago we were in the same position, considering Neo4j for the first time, with a team proficient with SQL and critical project deadlines to keep.

We did it because we saw the potential benefit of the native graph paradigm and our software team today laughs at me if I ever jokingly suggest we may make a wholesale change back to SQL. They laugh because they’ve experienced the benefits of using the Neo4j native graph database and the developer productivity level only continues to increase with each new release of Neo4j.

Consistent Developer Productivity with Neo4j

There are a few consistent benefits of the Neo4j native graph database we’ve experienced in dealing with connected data throughout the evolution of Neo4j:

• constant time traversals via index-free adjacency avoids the time-consuming dance of normalize, de-normalize, generate view table to squeeze out performance via fewer JOIN operations
• an intuitive property-graph model that is intuitive with contextually relevant relationships avoids the overhead of creating additional, and often complex, application logic to aggregate and orchestrate those connections after retrieving the entities
• the whiteboard-friendly nature of the graph model avoids confusion around the way things are connected resulting in fewer misunderstandings between product/business concepts and engineering leading to incorrect implementations requiring rework later

A major cornerstone in developer productivity was the introduction of Cypher in Neo4j 2.0 which is a very intuitive and direct representation of the graph data model being queried. Cypher makes

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Baskteball In Game Interactions with Neo4j

The NBA has enjoyed explosive growth in recent years; so much so that its TV deal, currently fetching $930 million annually from ESPN and Turner, will raise that number to $2.6 billion beginning

next season, a 180 percent increase. In addition to its globalization, nutritional advancement, and technological progress, the quality of play itself has been consistently climbing season after season. Much of this trend can be attributed to team staffs making better decisions about personnel, playing time, play style, matchups, lineups, and the like. And as much as Barkley and other old-school players would like to minimize its impact, it is undeniable that the best teams who make the best decisions have a common underlying focus: data.

Hard data, and how to interpret it (or “analytics”). Finding patterns and adjusting accordingly is crucial in any field. It is certainly no less applicable in basketball, whether it be within your own team, your opponents, or player prospects. All this data can be easily and efficiently stored within a graph database, where anything can be a node. Players, coaches, teams, games, stats, possessions, arenas, management, even gear – these are just some of the things that can relate with each other to have an impact on the ultimate goal of

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Graph Advantage: Personal Gamification

User attention spans online are quite short today with all the options vying for their eyeballs. Gasification is an approach that surfaced as being an effective way to engage with users in a more

engaging and interesting manner. Gamification in a non-generic, personal manner with the right incentives for each individual requires a very complete and connected understanding of that user.

Defining Gamification

Gamification involves integrating gaming mechanics into marketing and user interaction strategies. It’s a method that drives consumer engagement and participation since it produces positive behavior via incentives and rewards. As a matter of fact, this form of “gaming” can drive recurring user engagement and positive perception, as long as the approach is based on the right incentives.

Benefits of Gamification with Neo4j

Gamification is becoming more widely seen in today’s user strategy and is on the mind of those driving product and marketing decisions. Getting gamification right can transform the way businesses deal with their customers. Businesses are developing these game mechanics for their target users by means of rankings and customizations to get them to work for customers. The mechanics apply to situations that help promote customer loyalty, motivate buyers to continue making purchases, and provide attractive incentives to maintain their interest.

Involving not only the customer and your products, but also introducing rewards, challenges, and purpose as 

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Graph Advantage: User Personalization

User personalization is intended to tailor each individual’s experience to them and really provide a more human element to the interaction. Providing this aspect of feeling known and understood rather

than just being a generic set of eyes can go a long way towards more fulfilling and continued engagement with your users.

Digital retail is an major space where personalization surfaces because it’s challenging to find the right balance and approach for the interaction. By concentrating on the online retail journey of your customer and making it a personalized experience, you and your customers both benefit from the more personal and meaningful interaction.

Personalization through Digitizing Retail Knowledge

Many strategies for personalization involve long-running offline batch processes that take a considerable amount of time to complete in order to consider changes to what the system understands about me as an individual user. This delay in response is a major barrier to a personalized engagement with your user. The amount of data to be considered in total is quite astounding for the large established retail chains. However even with all this data there is still the possibility to provide a real-time user personalization experience for your customers. The information that is relevant for enhancing the online experience of each

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