A New Take on Master Data Management

A New Take on Master Data Management

A New Take on Master Data Management
Master Data Management (MDM) is evolving. Forrester Research in its Forrester Wave: Master Data Management Q1 2016, released this spring, points out that organizations’ needs are becoming more complex, with many companies tightly linking their MDM efforts to customer engagement and business processes, and with data models becoming more dimensional while data levels grow deeper. To that end, its customer references tended to prefer contextual and analytic Master Data Management solutions over traditional MDM tools, the report explains.

One of the newer entrants in the field, founded just a few years ago and based on a Cloud model, is Reltio. It was recognized by the research firm in the Wave document as a leader, thanks to executing a vision for next-generation MDM that includes “converging trusted Data Management with business insight solutions at scale” and using “Machine Learning and graph technology capabilities [to] enable a contextual data model.”

The positioning of Reltio for its Reltio Cloud begins with providing a clean data foundation for producing relevant and reasonable Big Data Analytics, says Ajay Khanna, VP of Product Marketing at the company. Once the reliable data foundation is in place for entities such as people, products, companies or accounts – pulling in data from multiple internal sources, third-party subscriptions and so on – the value for the business that Reltio provides is creating data-driven, business-facing applications on top of it that are pertinent to individual users’ or departments’ needs, he says.

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“The idea is to create a single common data foundation and visualize and present information for the business user in the context of their work, role, or department,” he says. “Present that in a unified view plus have intelligent recommendations around that.”

As transactional, interaction, social, machine-generated, and other realtime or batch data comes into the Reltio Cloud, the system matches and merges the data while keeping a full lineage and audit trail as part of its offering of complete core Master Data Management functions, like governance, in a highly visualized format. So, if a data attribute or record changes, it maintains a full click-through history of what user looked at it or updated it, how data streams were mashed to create the complete current record or data profile, and so on. Multiple departments can collaborate together on curating the data through ad hoc discussion threads or more structured workflows.

The company passed on using any existing relational or graph databases for storing and establishing many-to-many entity relationships (people to products to places, for example, or organizations to accounts) across Big Data volumes. Instead it designed a hybrid storage solution including the Reltio Commercial Graph – a columnar database with graph technology atop it, he says. “It’s the best of both worlds to manage profiles, with interactions and transactions, and to establish many-to-many relationships at Big Data scale,” Khanna says.

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The company says its Commercial Graph hybrid solution marries its years of experience with databases to handle both operational and analytical functions within the same data-driven application, providing complete views of relationships and covering various scenarios to provide flexibility to solve business challenges.

Part of that job is handling complex connections, hierarchies, and relationships between multiple domains. “There are big issues around hierarchies,” Khanna says. Companies often import legal hierarchies from various sources, standardizing on one structured set of information only relevant for one of their divisions, only to find that that particular set doesn’t give other groups the information that is relevant to their roles.

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