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. 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.
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.
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 GraphGrid Connected Data Platform uses the Open Native Graph Database to help 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.
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 ONgDB on GraphGrid excels in Master Data Management:
- A native graph store
ONgDB 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 ONgDB 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 ONgDB 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 ONgDB 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 Open Native Graph Database (ONgDB) can organize your master data and GraphGrid will keep it current and flowing so your organization can leverage this critical business asset.