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June 28th, 2023  |  7 min read

Graph versus Relational databases: which is best?

In the world of data management, businesses face the challenge of efficiently handling vast amounts of information. Traditional relational databases have been the go-to solution for many years, but the rise of graph databases has introduced a compelling alternative, especially when it comes to managing master data. In this article, we will explore the key differences between graph and relational databases and discuss why graph databases are particularly well-suited for master data management.

Understanding Graph and Relational Databases:

Relational databases have long been the standard choice for storing structured data. They organize data into tables with predefined schemas, where relationships between tables are established through primary and foreign keys. Relational databases excel at managing transactional data and complex queries involving multiple tables.

On the other hand, graph databases are designed to store and manage highly connected data. They use a network-like structure composed of nodes (entities) and edges (relationships) to represent data relationships. Graph databases prioritize relationships as first-class citizens, making it easier to model and traverse complex connections between entities.

Master Data Management (MDM) and its challenges:

Master Data Management focuses on creating a single, consistent, and reliable version of key data entities within an organization. This includes customer information, product catalogs, employee records, and other critical data elements. The challenges in MDM stem from the need to handle vast amounts of interconnected data and maintain data integrity across multiple systems and business units.

Why Graph Databases Excel in MDM:

1.    Relationship-Centric Model
Graph databases inherently prioritize relationships, making them ideal for managing complex interconnections within master data. Integrating data using a Graph-based MDM system is much easier and quicker than one which uses a relational database, because the Graph will naturally find connections between the data that would be impossible for you to stipulate or discover on your own.

This is particularly beneficial for a number of use cases, such as building a single customer view. To be effective, a single customer view must aggregate data from various touchpoints and channels to create a holistic profile. This means integrating both unstructured and structured data from a number of source systems and applications and finding the relationships between them. This is simply not achievable using a relational database with pre-prescribed schemas and relationships. 



2.    Zero Upfront Modeling
The main advantage of using Graph-based MDM is that you no longer need to model your data upfront. Once ingested, a Graph database will intuitively build a natural data model based on the relationships it uncovers. Traditional, relational databases demand that you already know or have a clear expectation of what your data model should look like. The reality is that data is constantly changing, and the six months you spent building your data model were a total waste of time by the time you actually started using it.

3.    Flexibility and Scalability
Graph databases offer unparalleled flexibility when it comes to evolving data structures. In MDM, data models can change frequently due to evolving business requirements. Unlike relational databases, which often require significant schema modifications to accommodate new relationships, graph databases can effortlessly incorporate new nodes and edges without sacrificing performance. This flexibility allows businesses to adapt and grow without hindrance.

How Graph Accelerates time to data value:

In addition to the above, graph databases also allow organizations to master, govern and drive value from their data much faster than their traditional, relational counterparts.

Rapid Data Integration and Onboarding:
Graph-based MDM platforms streamline the data integration process, allowing businesses to quickly onboard new data sources. The flexible schema of graph databases enables agile data modeling, eliminating the need for upfront schema design and lengthy data mapping processes. As a result, organizations can accelerate the integration of diverse data sets, bringing them into the MDM system faster and reducing time-to-value.

Efficient Data Exploration and Navigation:
Graph databases excel at data exploration and navigation, providing intuitive traversal of relationships. With a graph-based MDM platform, business users can easily navigate through interconnected data entities, enabling quick and ad-hoc data discovery. This agility in exploring data relationships allows for faster identification of insights, patterns, and anomalies, speeding up the process of extracting value from the data.

Agile Data Governance:
Effective data governance is crucial for driving value from data, and graph-based MDM platforms offer agility in this aspect. The inherent relationship-centric nature of graph databases allows for granular and context-aware data governance. Policies, rules, and data quality controls can be directly tied to the relationships between data entities, ensuring consistent enforcement and governance across the interconnected data landscape. This agile data governance approach reduces the time and effort required to establish and maintain data governance frameworks.

Collaborative Workflows and Data Stewardship:
Graph-based MDM platforms support collaborative workflows and data stewardship by providing a shared and unified view of master data. Multiple stakeholders, including data stewards, business users, and subject matter experts, can work collaboratively on the same graph-based MDM platform. This collaborative environment improves data quality, speeds up issue resolution, and enables faster decision-making, resulting in quicker realization of value from the data.

In today’s highly competitive and changeable business environment, no organization can afford to overlook the role of data as the foundation of its growth initiatives. With many companies still struggling to fully unlock the value of this data, however, it becomes a race to see who can put their data to use in the most efficient and impactful way. The use of Graph in MDM allows organizations to dramatically shorten the path from data acquisition to actionable insights – for example, CluedIn customers often have their first use case delivered within five weeks. This accelerated timeline is simply impossible for relational-based systems to achieve. When speed is paramount, it is graph that has the edge every time.

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