Three Remarkable Observations about Reltio

The latest entry on The Disruptive Master Data Management Solutions List is Reltio. I have been following Reltio for more than 5 years and have had the chance to do some hands on lately.

In doing that, I think there are three observations that makes the Reltio Cloud solution a remarkable MDM offering.

More than Master Data

While the Reltio solution emphasizes on master data the platform can include the data that revolves around master data as well. That means you can bring transactions and big data streams to the platform and apply analytics, machine learning, artificial intelligence and those shiny new things in order to go from a purely analytical world for these disciplines to exploit these data and capabilities in the operational world.

The thinking behind this approach is that you can not get a 360-degree on customer, vendor and other party roles as well as 360-degree on products by only having a snapshot compound description of the entity in question. You also need the raw history, the relationships between entities and access to details for various use cases.

In fact, Reltio provides not just operational MDM, but through a module called Reltio IQ also brings continuously mastered data, correlated transactions into an Apache Spark environment for analytics and Machine Learning. This eliminates the traditional friction of synchronizing data models between MDM and analytical environments. It also allows for aggregated results to be synchronized back into the MDM profiles, by storing them as analytical attributes. These attributes are now available for use in operational context, such as marketing segmentation, sales recommendations, GDPR exposure and more.

Multiple Storing Capabilities

There is an ongoing debate in the MDM community these days about if you should use relational database technology or NoSQL technology or graph technology? Reltio utilizes all three of them for the purposes where each approach makes the most sense.

Reference data are handled as relational data. The entities are kept using a wide column store, which is a technique encompassing scalability known from pure column stores but with some of the structure known from relational databases. Finally, the relationships are handled using graph techniques, which has been a recurring subject on this blog.

Reltio calls this multi-model polyglot persistence, and they embrace the latest technologies from multiple clouds such as AWS and Google Cloud Platform (GCP) under the covers.

Survival of the Fit Enough

One thing that MDM solutions do is making a golden record from different systems of records where the same real-world entity is described in many ways and therefore are considered duplicate records. Identifying those records is hard enough. But then comes the task of merging the conflicting values together, so the most accurate values survive in the golden record.

Reltio does that very elegantly by actually not doing it. Survivorship rules can be set up based on all the needed parameters as recency, provenance and more and you may also allow more than one value to survive as touched in the post about the principle of Survival of the Fit Enough.

In Reltio there is no purge of the immediately not surviving values. The golden record is not stored physically. Instead Reltio keeps one (or even more than one) virtual golden record(s) by letting the original source records stay. Therefore, you can easily rollback or update the single view of the truth.

The Reltio platform allows survivorship rules to be customized in rulesets for an unlimited number of roles and personas. In effect supporting multiple personalized versions of the truth. In an operational MDM context this allows sales, marketing, compliance, and other teams to see the data values that they care about most, while collaborating continuously in what Reltio calls the Self-Learning Enterprise.

Going beyond operational MDM

 

3 thoughts on “Three Remarkable Observations about Reltio

  1. Matt Gagan 21st June 2018 / 18:20

    Great observations and synopsis, Henrik. Thank you for the inclusion.

  2. Barry Cernie 5th August 2018 / 08:31

    While there are pros, you have not discussed the cons of each of the observations.
    1. The gap in the thesis is to understand if Reltio IQ combines the recently created Master with the transactional data in realtime. If it does not, one fails to see the difference between what exists today and Reltio’s offering.
    2. Multiple storages are required to keep in sync. Will there be no inconsistencies between these repositories?
    3. I see each persona creating a silo of data it calls Master, by combining data from the sources in different ways.
    Reltio is certainly different; is the offering unique enough to be pursued?

    • Henrik Liliendahl 6th August 2018 / 16:50

      Hi Barry. Thanks a lot for commenting. In my work when being involved in vendor selection activities we certainly work with both the pros and cons of each solution and your considerations does cover by what such a solution is evaluated and what we hear from analysts. You can certainly evaluate alternatives to Reltio IQ by having separate MDM and big data platforms. Having multiple storage concepts does raise concerns around real time consistency (and so does each NoSQL approach in general). Several versions of the truth must be balanced wisely. Every offered solution has its own unique pros and cons that must be evaluated together with the vision, mission and the current landscape for each MDM implementation.

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