In today’s digital age, enterprises are constantly striving to gather, store, and analyze data from various sources to make informed decisions. However, the data that needs to be analyzed often lies across complicated or even multiple databases, and sometimes even multiple organizations. One of the biggest challenges faced by businesses in such scenarios is the chaining together of many-to-many relationships contained across multiple tools within different enterprise-level databases.
Now, imagine that the organization has customer and product data stored across many tables in different databases. These databases might be managed by different teams, use different tools and technologies, and even be located in different geographic locations. In such a scenario, chaining together the many-to-many relationships across multiple tools and databases becomes a significant challenge.
Common issues creep in when trying to solve this problem, some but not limited to:
Data inconsistency:
When data is stored across multiple databases and tools, it’s common for inconsistencies to arise. For example, the same product might have different identifiers across different databases, making it challenging to link customer purchase data to the correct product.
Data duplication:
When data is stored across multiple databases, it’s common for duplication to occur. For example, the same customer might be stored in multiple databases, leading to data redundancy and inconsistencies.
Performance issues:
Chaining together many-to-many relationships across multiple databases can result in performance issues. If we want to chain 6 facts together, we actually need to perform an 11 table join (6 core fact tables and 5 mapping tables). Retrieving data from large table joins takes longer, leading to exponentially slower application response times the more complicated the query.
Lack of standardization:
When different teams manage different databases and tools, it’s common for there to be a lack of standardization in data definitions, schema, and formatting. This can lead to data integration challenges and hinder the ability to extract meaningful insights from the data.
Security concerns:
Sharing data across multiple databases and tools can pose significant security risks. It can be challenging to ensure that sensitive data is appropriately secured and that only authorized personnel have access to it.
So what can we do about it?
Turning the relational table row and column mindset on it’s head, we use Graph. In Graph databases we think of data as nodes and links instead of rows of tables. Unlike Relational Database Management Systems (RDMS) mapping tables need to be built and maintained; in Graph we can traverse the network chain efficiently – we traverse the nodes and links instead. This is Graphshare.
In conclusion, rather than suffocating in planning lets see what Graphshare can do to help simplify your data management strategy. Let’s reduce the number tools needed to overcome the challenges and help extract meaningful insights from your data. Use the Graph, use Graphshare.
