Graph Databases for Impact Analyses:
The Key to Actionable Insights


on December 9, 2021 • Back to Blog index

As in all aspects of life, every decision in business has consequences. When leaders do not think things through, the results can be disastrous. Impact analysis can uncover the probable effects of a change and assist businesses in making informed decisions by focusing on the unanticipated, sometimes negative, outcomes.

This article defines impact analysis and discusses why a graph database is essential for impact analysis.

What is Impact Analysis?

The importance of impact analysis for requirements management cannot get overstated. It gives business teams a clear picture of the repercussions of a proposed change, allowing them to make informed judgments about which proposals to approve and discard.

Moreover, the analysis looks at the proposed modification to see what components would need to be produced, updated, or eliminated and how much effort will be required to accomplish it.

What is a Graph Database?

Graph databases are designed specifically for storing and navigating relationships. Relationships are first-class citizens in graph databases, and they account for the majority of the database's value. Nodes get used to store data entities, and edges get used to store relationships between things in graph databases. An edge has a start node, an end node, a type, and a direction, and you can use it to define parent-child relationships, actions, and ownership, among other things.

You can navigate a graph with specified edge types or over the entire chart in a graph database. Because the relationships between nodes are not calculated at query time but are persisted in the database, traversing the joins or relationships in graph databases is relatively fast.

The graph style gives a more flexible platform for examining data based on factors such as the strength or quality of a relationship, which is especially useful in impact analysis.

Graphs let you explore and discover connections and patterns in social networks, IoT, big data, data warehouses, and also complex transaction data for multiple business use cases, such as the following:

  • Recommendation Engines: For recommendation applications, graph databases are an excellent solution. Organizations may record graph relationships between information categories like client interests, friends, and purchase history in graph databases. Moreover, organizations can provide product recommendations to users based on which products are purchased by others who have comparable purchase histories using a highly accessible graph database.
  • Fraud Detection: Sophisticated fraud prevention is possible with graph databases. You can leverage relationships in graph databases to handle financial and purchasing transactions in near real-time. Moreover, numerous people linked with a personal email account or multiple people sharing the same IP address but living in various physical addresses can be easily detected using graph databases.


Impact Analysis is a method for identifying important business functions and predicting the repercussions of a disruption in one of those functions. It also enables organizations to collect data necessary for developing recovery strategies and limiting potential losses.

Without knowing the possible path between a beginning point and the impacted element, organizations can use graph databases to locate related things. The capacity to derive insights in increasingly complicated ways makes graph databases a must-have for today's demands and tomorrow's breakthroughs. As businesses and organizations continue to push the capabilities of big data and impact analysis, graph databases are proving to be an essential piece of the puzzle.

Yoann Maingon
Co-Founder & CEO of Ganister