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In an era where data is distributed more than ever across different applications and modalities, the need for master data management (MDM) is extremely important to ensure data accuracy and quality. MDM is a fundamental type of data technology, providing a “golden master” or uniform master record of data for each entry.
Among the leading vendors in the space is Reltio, which has developed a cloud-native MDM platform. Reltio raised $120 million in funding in November 2021 and has steadily expanded in the months since, including adding data quality measurement capabilities in June 2022. The company is increasingly adding improvements with artificial intelligence (AI) and machine learning ( ML) in the platform’s MDM as well, helping organizations better manage and dominate their data.
Today, Reltio is further expanding its MDM capabilities with the release of what it calls “velocity packs” that package MDM configuration and best practices designed for specific industry verticals. The first pair of verticals are life sciences and healthcare, with more to come in the coming months. Velocity packages are partly a response to the current economic climate where organizations are under pressure to demonstrate value from data faster.
“There’s more focus now on total cost of ownership and how things can tie into ROI-type outcomes,” Manish Sood, founder and CEO of Reltio told VentureBeat. “Where customers might have made any data investment too easy in the past, they are now focused on how it will get them to value in a shorter period of time.”
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The intersection of AI/ML and MDM
According to Sood, data in the modern world is an asset that is constantly updated and must also be constantly curated.
A key part of maintaining accurate and up-to-date data, for any industry, is having data quality rules that help ensure data freshness, lineage and accuracy. The ability to automatically apply intelligent rules to MDM data quality is an area where Reltio has invested its time and money. A significant part of the effort falls in the realm of AI and ML.
Sood said his company has focused on applied artificial intelligence to manage data. There are many areas in particular where Reltio is using AI today, including entity analysis. He noted that the MDM system receives data from multiple sources and there may be slight variations between sources in some of the field entries. for example, with how a name is spelled or how a product is categorized. Entity analysis helps solve this issue.
“We use artificial intelligence algorithms to bridge that gap and understand that it’s the same type of information provided for the same person, the same product, or the same type of supplier information, and we can create a unified record,” Sood said.
The AI capabilities developed by Reltio can also go a step further and help clean the data to remove duplicates, as well as enrich it to add context.
With the new speed packs, the AI capabilities are specifically tuned to enable the right data formats and insights for a given industry sector. Sood explained that speedpacks also incorporate regular schemas defined for the specific data sectors.
The graph model at the core of Reltio MDM
While artificial intelligence is important, underlying the Reltio MDM platform is a graph data model that forms the foundation for the platform.
Sood said the graph data model his company has built is actually a hybrid approach that integrates columnar and graph database capabilities.
“The simple way to think about it is that entities are the basic constructs defined with infinite performance, which comes from the columnar capabilities, as well as any kind of relationships or links between different entities,” Sood explained.
He added that Reltio uses a multilingual persistence model for storage, where older data can be stored in secondary and tertiary storage to help reduce latency for the most active data, while also enabling granular queries for historical data.
Overall, the goal for Reltio is to use its graph data model and data science expertise to help organizations recognize the value of data faster, Sood said. He emphasized that the new speed packages are a key part of achieving this goal.
“Everyone so far has assumed that if we have better data we will have better results,” Sood said. “But now it’s time to test it and make sure we can achieve those tangible results.”
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