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This blog was originally posted under: IM Blog
Regular readers will know that I am a fan of graph databases and there are a number of vendors that are currently embedding graph databases into other products. However, these are mostly infrastructure products in their own right: data matching, metadata mapping, data discovery, master data management (MDM) and so forth. Reltio, however, has gone a step further and is delivering applications based on a graph database that has, or may have been (depending on the application), also based on MDM.
From a technical perspective Reltio offers what it calls a “modern data management” Platform as a Service (PaaS) product. This is based, in part, on purpose-built graph technology (called Commercial Graph) built on top of Apache Cassandra. The product also leverages machine learning and analytics (based on a Spark framework), all of which is deployed in the Cloud to support a variety of data-driven applications. These applications include key account management, various solutions that use Reltio for making recommendations, and a Mergers & Acquisitions application, amongst others.
While IT teams will typically be interested in the core PaaS technology, frontline business users will be interested in Reltio’s data-driven applications, which are currently focused in Healthcare and Life Sciences, High Technology, Insurance, Media & Entertainment, Oil & Gas, and Government. The possible exception to this is the company’s Mergers & Acquisitions offering, which is potentially usable in any industry.
Of course, this isn’t the first time that a vendor has used a graph database to support MDM but Reltio isn’t really marketing itself as an MDM-only offering but as a data management platform that can not only handle any type of master data, but also transaction and interaction data, and with pre-built applications running on top of that set of capabilities. In effect, these are applications with actionable insight built-in. Thus, for example, key account management is one of the company’s applications and the idea is that you can use the graph technology in the product, along with the machine learning, to understand the relationships and hierarchies within key accounts, and based on the role and goals of the business user using the application, make recommendations about which person to contact or, in a different use case, which product to recommend. As an aside, recommendation engines are a major use case for graph databases, it’s just that Reltio has built the applications for you rather than your needing to create them from scratch.
It’s also worth considering the Mergers & Acquisitions solution. Suppose that two companies are considering a merger: you create Reltio master data as a tenant in the Reltio Cloud – which blends together data across all data sources within the enterprise which will include existing on premises MDM, CRM, ERP, financials and ERP systems – for each of the companies involved. You then create a “clean room” tenant where you can analyse overlaps and synergies. In the pre-merger phase, relevant information that is sensitive may need to be masked, but even with that it is easy to see how this can be used as part of the due diligence process: you can very quickly tell, for example, how many common customers you have. However, it is in the post-merger phase that this approach really comes into its own. Typically, merging key operational data can take years, delaying the administrative benefits that are often a key driver for mergers. Using Reltio’s graph-based approach for discovering relationships this can be accomplished very much faster.
That’s a pretty brief description but it should give you an idea of what the company is doing: I think it’s pretty neat.