Today, business users increasingly require personalization and flexibility in the apps they use for work, similar to the apps they use in their personal lives. What’s behind this trend? The type of data model powering the application plays an outside role in the application’s ability to deliver on those requirements. When built on a graph database (as opposed to its more rigid brethren, the relational database), a SaaS solution allows businesses to easily tweak back-end processes without losing capabilities. Consequently, when naming graph databases as one of the biggest data trends of 2019, Gartner predicted 100 percent annual growth through 2022, showcasing its adaptability, flexibility, and potential across all industries.
Social media sites were among the first businesses to realize the power of the graph database. Facebook was originally built upon a relational database, but, in an effort to more effectively communicate relationships between people, places, and other data categories, the company built a graph database for its service.
As a result, GRC (governance, risk management and compliance) solutions, sales enablement tools, and marketing software have more in common with Facebook than you may think. All of them rely on a flexible data model to store information and pull different types of data, meaning it has the ability to unlock insights by identifying and assessing relationships with no previous connection. This creates new levels of flexibility and visibility for users.
Early on, Facebook experienced platform growth issues with its expanding spiderweb of data. Such a large volume of data created too much complexity for the existing processes the company had in place. Industries experiencing a similar influx of data and complex relationships are experiencing the same issue, and developing graph database solutions allow industry professionals to identify relationships they wouldn’t have been able to beforehand. In some cases, it can potentially save lives.
For example, the Candiolo Cancer Institute (IRCC), a scientific research non-profit fighting against cancer, relies on managing and tracking complex hierarchical data to garner insights into cancer research. The IRCC team needed a flexible data model in order to organize and track cancer samples and procedures. Cancer research data is constantly changing and relationships need to be identified quickly and efficiently, so the IRCC could no longer rely on a relational database. Like Facebook, they implemented a graph database infrastructure.
Within the realm of enterprise solutions, the same rings true. Businesses are constantly changing the organization of data and data-based processes, such as GRC. And, as a result, they require SaaS solutions that have the ability to change with them. Relational databases are too rigid and structured to keep up with that pace of change. Graph databases, on the other hand, fit well into an infrastructure without predetermined categories. It conforms to specifics and relationships to increase efficiency without diminishing processes. If an enterprise uses a software platform built on a relational database, they’ll bend their processes to fit into the infrastructure instead of the solution adapting to the relationships it uncovers. So, like IRCC being able to correlate and model data relationships between concepts, an enterprise can uncover data-driven insights to identify, monitor, and remediate risks as its business grows and evolves.
Many processes within certain industries and businesses require complete transparency and the ability to showcase data lineage and thorough reporting. Sticking with the business area of GRC, highly regulated industries such as banking and financial services are required by regulatory bodies and legislation to provide data lineage and lifecycle for risk and governance purposes. Visibility within a GRC solution infrastructure is crucial because it’s also used to track workflow and identify, and subsequently reduce, potential areas of damage and risk within an enterprise’s workflow and model of business. With a graph database, reporting data and connecting the dots between data relationships is much more efficient than with a relational database.
Ultimately, enterprise software built on graph databases allows the solution to store information and pull data. Because of its flexibility and visibility, it generates data despite a high number of relationships, adds new relationships easily without diminishing processes, and pulls and organizes data in real-time. Despite this, it may not be the best infrastructure for every type of software. But, for an enterprise-critical area like GRC, it allows solutions to leverage and unlock power and functionality, unlike anything we’ve seen before. It also allows risk management to align with and improve already existing processes, creating a better overall culture of risk and compliance.