The Performance Graph (or why Xtiva loves Graph Computing)

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by Jayson Ambrose on October 1, 2018

Imagine developing a software product, then building a successful business around the software over a 20 year period, then deciding one day to completely rebuild your software solution from scratch.

That’s precisely what Xtiva decided to do four years ago. See Project Liberty and the Self-Disruption of Xtiva and Rebooting Sales Performance Management in Financial Services for the full backstory.

To accomplish rebooting Sales Performance Management (SPM) we needed an approach that would integrate lots of disparate data, deliver powerful analytics, enable complex business rules, support dynamic workflow and be highly tailorable to each user or business entity. It also meant a need to develop new approaches and perspectives on measuring, evaluating and influencing performance. Enter graph computing. Facebook has the social graph. Linkedin, the professional graph.

Xtiva has the performance graph.

Embracing this key technology – graph computing – will enable the XtivaCloud SPM Platform to power the complex relationships found in the modern financial services enterprise. Compared to a traditional relational database, on which most software designed 20 years ago was built, a graph data model enables dynamic organizational structures, flexible business rules, new customer insights and faster time to market – all things that modern financial services enterprises need to survive and thrive. The ability to uncover, discern and understand the subtle relationships and influences between parts of the enterprise is a powerful and key aspect of how Xtiva can bring SPM for Financial Services to life.

The complexity of financial services begs for graph computing

Advisor turnover, mergers, acquisitions and myriad lines of business means there is no one-size-fits-all, hierarchical organizational structure in today’s financial services enterprise. Agile, high-growth firms need to support multiple subsidiaries, uneven levels, matrixed reporting structures and changing relationships. The data model should also enable cascading data rights and cross-functional feature access.

Delivering this type of flexibility with a relational database model is incredibly complex and can lead to significant scale and performance challenges. Graph technology reduces the code complexity when building features that use the many types of connected data and their relationships. This means a better fit for your business, consistent performance, faster feature development and increased reliability in production.

Graph data model empowers flexibility and deep understanding of relationships between data

In a graph data model, the relationships between entities are just as important as the metadata associated with those entities. As such, entities (or nodes) in the model can have multiple relationships (or edges) of different kinds. Because the relationships themselves can have variable attributes, we can apply business logic to our organizational hierarchy.

Let’s consider a use case that explains this point. For example, in the XtivaCloud SPM Platform, relationship effective dating is applied throughout. We may wish to apply a start date to a contract assignment or an end date to a split payout or the timing of a person joining a team. In our graph organization structure, we could schedule in advance someone reporting to a new manager while also retaining the history of the previous reporting relationship or maintain multiple reporting relationships. Account and client relationships are similarly flexible. Multiple household definitions can be established manually or automatically inferred from other known relationships.

Beyond supporting the business rules and relationships within XtivaCloud SPM Platform, our graph data model supports powerful insights that would not be feasible with a traditional relational database. The connections between customer and the products they own can be combined with data about similar customers, available products and other external sources to create personalized recommendations for advisors. Compare this with simply suggesting the top sellers across all customers, updated as a batch process. Customers today expect personalized service, and graph technology can empower advisors with timely, specific product suggestions. Furthermore, a product line manager could answer questions about performance across a specific customer demographic or what types of products tend to be purchased together.

Graph is a key to success in financial services

The graph data model reflects the relationships between data (or more accurately what that data represents) in a much more natural and organic fashion. It reflects the layers and the differentiated relationships that exist between the same data with varied context. In other words, a graph data model reflects reality better.

The success of wealth advisory firms and financial services businesses will increasingly depend on the ability to deliver solutions that are customer-centric, personalized and easy to use. A graph data model provides Xtiva with the best foundation and tools to provide these types of solutions to our clients.

Today, the Xtiva SPM product platform has been re-engineered from the ground up, based on 20 years of experience and expertise in incentive compensation management (ICM) for financial services businesses. Welcome to Sales Performance Management exclusively for Financial Services and welcome to the performance graph.