A New Take on Master Data Management
Jennifer Zaino and Ajay Khanna recently discussed key aspects of modern data management and Reltio Cloud. The full article by Jennifer Zaino is available at DataVersity
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.
“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.”
Inside Reltio Cloud
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 Self-Learning 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.
The company says its Self-Learning 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.
Maybe, for instance, the account profile includes a legal hierarchy that may be relevant for finance, but is not useful for sales, which may want product roll-up information in that account, Khanna says by way of example. That leads users to create hierarchies in systems outside of the core Master Data Management system that are better suited to their needs, creating multiple versions of account information. With Reltio, information from multiple sources is blended and personalized to address different functions’ needs – only the data that is relevant to a user’s role is provided to that person. Now finance, marketing, sales, and compliance have their own personalized views of account hierarchies while maintaining the single source of customer information.
There are multiple use cases, Khanna continues, for enterprise data-driven applications built upon reliable and user-appropriate master data, which offer relevant Big Data insights and intelligent recommended actions. Such actions come courtesy of Reltio’s Machine Learning technology that helps users make decisions, discovering new relationships through the graph technology or next best actions for customer engagement. “The recommended actions based on Machine Learning and graph analytics can guide business users to make better data-driven decisions. This is a differentiator for many businesses,” he says.
For instance, a company wishing to get its products used across more divisions at one of its customers’ accounts may explore the account hierarchy or commercial graph to discover which units currently are using which products, and where the gaps in product usage lie across its customers’ divisions. From there, sales account managers could be guided to the next best action to take to penetrate more of the customer’s environment. They can also discover key influencers in the account, their affiliations and the strength of their influence in a business unit that is currently not using its products.
Currently, the company offers data-driven solutions that: incorporate bringing together data from multiple internal, third-party, public and social sources for complete profiles; that uncover relationships across various entity categories; and, that present relevant information to users based on their roles in a number of areas. The use cases include its Customer 360 or Account 360 to complement CRM and marketing automation applications, aimed at improving sales rep productivity, and targeted customer engagement through multichannel interactions with more reliable customer data, for example.
Another is Key Opinion Leader Management (KOL) for the life sciences sector, which makes it possible to identify thought leaders in a particular disease area by consolidating information such as specialty, publications, speaking engagements, and clinical trial participation. It also supports visualizing their expertise, hospital affiliations, influence, social networks, and history of interactions. This information can then be used for targeting thought leaders to contract with during a new drug launch or even for compliance reporting, Khanna says.
Recently the company introduced Reltio Cloud 2016.1, which added new analytics integration, collaboration, and recommendation capabilities. It also provided access to Reltio Insights, a new module that seamlessly connects data, mastered and managed within Reltio Cloud, to the Apache Spark Big Data processing environment.
“We made it easier for business people to take MDM and transactional data and run more sophisticated analytics in the Spark environment,” Khanna says. “It’s a bi-directional connection so once you run that, you can bring your insights back as part of your Master Data Management profile and visualize the analytics within business applications.”
In June it also announced an alliance with healthcare information and technology solutions provider IMS Health: Reltio Cloud will power IMS Health’s IMS One Platform, to enable IMS One customers to quickly deploy new analytical environments, combining internal sources with data from IMS Health. Reltio Cloud’s built-in Data as a Service (DaaS) capabilities will make IMS Health reference data assets, including the OneKey healthcare professional database, available on-demand, to help customers resolve identities and enable better compliance. Reltio also said in July that it has closed deals with three of the top ten global pharmaceutical organizations in the last six months.