Unlike other databases, relationships between data is a priority to graph databases. This helps the querying of relationships to be done faster because they are perpetually stored in the database.
Graph databases are useful for heavily interconnected data and are an essential choice for master data management.
Graph databases eliminate the need for programmers to devise temporary or workaround solutions in an effort to query the data in a way that takes relationships into account by including relationships directly into the database. Instead, relationship-based searches are simple to use and provide results very immediately. For a large and complicated amount of data, like the master data repository of a firm, this degree of query sophistication becomes more crucial.
Since master data management (MDM) is all about the consistency, accuracy, and accountability of an enterprise’s official shared master data assets, we can see just how important graph databases are to MDM.
Let us see exactly why.
Helps Create Relationships Between Data Points
In MDM, programmers usually query voluminous and complex sets of data just like those in a company’s master data repository. In doing this, they have to create makeshifts or work around solutions to know what relationships there are between data sets.
Graph databases can prevent this volume of work by building relationships directly into the database and so any relationship-based queries by the programmers are returned almost immediately because they are straightforward. So, unlike standard databases which only focus on capturing individual data points, a graph database consists of two entries which are the nodes and the relationships.
The latter is very essential for Master Data Management and this additional context of relationships between data points helps companies gain a deeper understanding of important connections.
Takes Advantage of Machine-to-machine Communications
Companies that embrace Master Data Management models always look at solutions that enable the shift to web 3.0, thereby allowing machines to communicate easily with each other. Graph databases are essential to MDM here because they will work well in web 3.0. Graph databases store information in a way that provides down-to-earth meaning with connections between each set of data. This helps machines like AI and robots make MDM work easier. This makes better decisions possible.
Graph databases will also help to bridge whatever gaps there are between machine analysis and human understanding of Master Data Management. The building of relational information into databases makes it possible for AI to easily make something of the information and do the rest of the work needed for data management.
Helps with Data Compliance
When sharing data with stakeholders, it is necessary to remember one of the most pressing reasons for the shift toward MDM which is compliance.
Data privacy regulations are key these days and there have been severe punishments to businesses that strayed away from that pathway. This is where graph databases come in. Graph databases usually offer flexibility and most importantly, security. This is because they are mostly secured by immutable ledger technology. So they are essential to MDM because they assure companies making use of it of the data lineage with a reproducible audit trail.
For companies that use knowledge graphs to understand their customers, business decisions, and product lines, this level of traceability by graph databases, helps ensure compliance on an internal level which is very key to business growth and development.
Helps Make Sense of Big Data
Any data that is stored in graph databases as part of the Master Data Management framework is more discoverable and usable in the context of the semantic web. This allows interoperability by making it very easy to share with stakeholders and enabling them to make meaningful decisions. It is easier to make these data-driven decisions because graph databases help make more sense of massive data based on its accuracy and completeness.
This is very important to companies, especially the FMCG that are always trying to make sense of their shipping and sales data and put it to intelligent use by projecting it for the future. The technology that supported these analyses in years past was too sophisticated then but now, graph databases have made it easier.
Traditional hierarchical data must be reinterpreted as graphs because business demands development and personnel reporting connections are only one illustration of this. Tracking data in a graph database is very simple after it has been restructured into a more flexible paradigm.
With the use of graph databases as a critical tool, leveraging enterprise data to make information-based decisions has become way easier than years ago when companies relied on programmers to query relationships between data sets. Just like Master Data Management, graph databases are now playing an important role in shaping enterprise technology’s future.
At the moment, fraud protection technologies, social media, and recommendation engines frequently use graph databases. In each of these situations, the additional information provided by the interconnections between the data pieces aids businesses in recognizing key linkages. All the big companies that had their work cut out for them with big data analysis now rely on graph databases to simplify the process with even more in the bag than a mere data analysis.