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Graph Advantage: Master Data Management

Master Data Management with Neo4j on GraphGridMaster 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. 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.

Why a Graph Database for Master Data Management?

Because master data is constantly shared and connected, poorly made Master Data Management (MDM) systems can cost business agility in a way that slows your evolution as organization. A majority of MDM legacy systems depend on a relational database that isn’t even optimized for rapid response or traversing relationships.

Fortunately, graph databases are perfect for housing, querying, and modeling hierarchies, metadata, and connections in your master data. Master data is a lot simpler to model with graph databases as it requires less resources than making relational approach. Furthermore, you won’t have to move all your master data into one location. Graph relationships easily connect your siloed information across your CRM systems, inventory structures, point-of-sale systems, and accounting to offer a comprehensive view of your enterprise data.

Master Data Management Challenges

Enterprises today are inundated with Big Data. Organizing its highly complicated relationships can also pose a challenge. These are some challenges in Master Data Management that the Neo4j graph database helps enterprises overcome:

  • Complicated data sets
    Managing data models with the use of a relational database can lead to a complex code that takes up a lot of time to maintain, slow to operate, and costly to make.
  • Query performance in real-time
    Master data systems should assimilate and offer data to applications within an enterprise. Going through a complicated and interconnected data set to offer real-time details can be challenging.
  • Dynamic setup
    Master data is very dynamic since it involves continuous re-organization and addition of nodes, making it difficult for developers to design systems that consider current and future needs.

Neo4j on GraphGrid for Master Data Management

RDBMS, or Relational Database Management Systems, aren’t as efficient when it comes to housing master data. You’ll require a schema-flexible, query-efficient, and real-time approach to make your master data into a utilized business asset. Some key reasons why Neo4j on GraphGrid excels in Master Data Management:

  • A native graph store
    Neo4j is capable of housing interconnected master data. Its native graph storage makes it a lot simpler to traverse through your data without resorting to indexing at every turn. The native graph processing engine of Neo4j offers support for non-JOIN graph traversal queries on master datas to allow complex decision making in real-time.
  • A flexible schema
    The versatile schema-free property graph model of Neo4j makes it a lot simpler for enterprises to evolve models of master data across all their existing database schemas.
  • An optimized data pipeline
    The write optimized data pipeline of GraphGrid enables enterprises to keep their master data current by efficiently and continuously extracting data from current databases, transforming it to the graph master data model and loading it into the Neo4j graph database.

Ideal data-driven business approaches are now being based on real-time master data consisting information on data relationships. With highly efficient querying and modeling, the Neo4j graph database can organize your master data and GraphGrid will keep it current and flowing so your organization can leverage this critical business asset.