Our vision for MarkLogic® is and has been to make it the best database for integrating data from silos. This naturally led us down the path towards Master Data Management (MDM) as organizations started choosing MarkLogic to integrate data, harmonize data and match and merge data to deduplicate records.
Recently released, MarkLogic’s Smart Mastering is an open source feature available on GitHub that provides the capability to quickly match and merge data from any domain to create a unified 360 view. And, the feature is easily integrated as part of MarkLogic’s Operational Data Hub architecture—it’s not a separate MDM tool.
The highlights include:
Figure 1: Smart Mastering Process in a MarkLogic Operational Data Hub (ODH)
MDM—the process of creating a golden master record—falls under the umbrella of data integration and focuses on data quality and data governance. The result is that if you do MDM right, it improves everything from customer service to business analytics. With the rise of big data and the increasing challenges of data integration, this specific aspect of data integration is now a big priority.
Mastering data is not a simple task; it’s much more than just looking at two records and determining which one is the right version of the truth. In reality, it’s quite complex. Let’s look at these examples from various domains:
As these examples illustrate, finding “truth” in your data is quite difficult, especially without context and knowledge of upstream data sources. That is why in today’s world, it’s better to use a trust-based approach that acknowledges different perspectives of the same entity. In other words, we can trust that one customer record is the better one because it was data entered into a loan application, whereas another record was just created from a customer-service call. But, we’re not just going to throw out the other record—we just might trust it less for a given purpose.
The opposite of a trust-based approach to MDM is a truth-based approach. A truth-based approach seeks to determine the single source of truth or “golden record.” It also leads to throwing out the data that doesn’t “survive” simply because it’s deemed to not be the best version of the truth. Most leading MDM vendors think about MDM using a truth-based approach. In reality, finding that golden record is as elusive as finding the golden snitch—it’s really hard, if not impossible.
The challenge is that traditional MDM tools have a huge technological barrier to implementing trust-based MDM. The static relational model on which they are built, along with a legacy philosophy, means that they are slow to implement and prone to error. And, standing alone, they are not built to handle operational workloads at the center of the enterprise architecture.
According to one study, there is a 76% failure rate with traditional MDM. Why is that?
Here are some of the main reasons why traditional MDM fails:
MarkLogic was designed as a database for integrating data, and it so happens that it is also very good for doing MDM with Smart Mastering. But, it’s not a separate solution, it’s a feature of the MarkLogic Operational Data Hub that you can leverage as part of the hub.
Organizations see this as an advantage—they don’t need a full-fledged traditional MDM tool. They are choosing MarkLogic over Informatica to do MDM because it accomplishes the end goal of mastering data faster and with higher-quality results.
The comparison table below walks through some of the reasons why MarkLogic’s Smart Mastering is a better approach to MDM:
|MarkLogic Smart Mastering
|Traditional MDM Tools
|Fast and lightweight
|Yes Mastering happens in the database as a feature of the data hub.
|No Slow waterfall approach. Not operational or transactional. Still requires a relational database (and schema).
|Secure and governed
|Yes Full data lineage and auditability since everything happens in the database.
|Maybe May not stand up to regulatory scrutiny without enterprise data governance capabilities.
|Yes Load data as is, do iterative transformation in the database.
|NoETL tool required to move data, lack of data lineage.
|Yes Probabalistic approach that acknowledges real-world ambiguity and change.
|No “Truth-based” approach that defines data sources as either trusted/untrusted or correct/incorrect.
|“Fit for purpose”
|Yes Acknowledges that some data may be right for one use case but not for another.
|No Tries (in vain) to create a “golden record” that is a single source of truth for every use case.
|AI and fuzzy logic
|Yes Relevance scoring, database intelligence and probabilistic algorithms for accurate results.
|No Simplistic models and deterministic matching that result in poor accuracy.
|Ability to unmerge
|Yes Merge corrections are okay; they can be reversed. No data is ever lost.
|No Strict survivorship rules and data loss that is irreversible.
To better understand how Smart Mastering works, let’s walk through how it works at a high level. First of all, it’s important to understand that Smart Mastering is just one significant step on the data integration journey towards creating a 360 view.
Data integration is a very agile process in MarkLogic:
Figure 2: The Smart Mastering process as part of the overall data integration process.
As you can see, Smart Mastering is part of the curation process. The goal of the curation process is to define your entities and build a canonical model. Through an iterative process, incoming data is loaded and searched. A minimum amount of modeling is done to understand the data in the context of existing data, map it to a canonical model and harmonize it to make it consistently queryable. Smart Mastering occurs during the curation phase when similar records are matched, scored and merged. It is an iterative, flexible process that can be repeated and refined as necessary. The result is immediate business value, but with the flexibility to handle later changes.
Once engaged, the Smart Mastering process begins with matching. The feature relies on AI and fuzzy matching to find entities that are the same. To do this, it relies on MarkLogic’s sophisticated indexing capabilities. For Person entities, you can include thesaurus lookups for name/nickname variations or other synonymous values. For non-person entities, there are algorithms such as double metaphone, levenshtein distance, smart date matching and field weighting. Score backoff is included to reduce scores when some elements match but very important ones like date of birth or personal identification number do not (for example, to account for family members or twins who are distinct, not duplicates). Running these matches against batches of records creates a results list with confidence scores associated with the matches and potential matches.
When it is determined that two entities match to a high enough degree of certainty, the merging process kicks off. A third instance of the data is created that includes the data from the two source entities, along with a section at the top for the mastered data. For any values that were the same from both (or more) sources, a source count for each data element is included. The rules that determine which data to prioritize as the “master” (i.e., which data should be noted as being more trusted) go something like this:
There’s obviously a lot more under the covers, and the process (and the APIs used) is determined somewhat by the domain in which you’re working.
If you’re interested in understanding how MarkLogic’s Smart Mastering feature works and also want a deeper dive into some customer examples, I encourage you to check out the presentation given by Kasey Alderete and Damon Feldman at MarkLogic World 2018: Master Data in Minutes with Smart Mastering.
Smart Mastering is useful anytime you have disparate entity data that may have overlaps and require deduplication. Every organization can benefit from this capability.
Here are some examples of organizations that are already using MDM with MarkLogic:
Smart Mastering also has benefits for handling regulatory compliance use cases, such as GDPR. These use cases have similar requirements to “360-view” initiatives and usually overlap.
A recent report from the analyst group, Ovum, highlights the need for proper MDM to address GDPR guidelines, saying, “Failure to associate a piece of data with an individual because it, for example, uses a nickname or a former address can run afoul of [GDPR] requirements to erase, rectify or produce data for the data subject to view.” Ovum’s report goes on, saying “MarkLogic’s architectural approach to mastering further bolsters compliance.”
They note how MarkLogic’s ability to keep all data available, maintain a full audit trail and apply granular security controls makes it particularly well-suited to address GDPR.
Interested in learning more about Smart Mastering? Here are some resources to start with. And, if you’re a developer, we encourage you to download the bits on GitHub and try it out!
Matt Allen is a VP of Product Marketing Manager responsible for marketing all the features and benefits of MarkLogic across all verticals. In this role, Matt interfaces with the product and engineering team and with sales and marketing to create content and events that educate and inspire adoption of the technology. Matt is based at MarkLogic headquarters in San Carlos, CA and in his free time he is an artist who specializes in large oil paintings.
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