Within the data management industry, it’s becoming clear that the old model of rounding up massive amounts of data, dumping it into a data lake, and building an API to extract needed information isn’t working. It’s outdated, it’s clunky, and it was built for a different era.
Today’s data needs are defined by several major changes in recent years: exponential growth in the amount of available data, increased data compliance enforcement by governments around the world, and an increased reliance on machine-to-machine communication.
With each of these features in mind, more companies are turning to Master Data Management as the gold standard for managing their data. Master Data Management involves a comprehensive approach to managing all enterprise data using a single platform. This strategy avoids data silos and redundancies while making it easier to establish enterprise-wide data governance..
When turning to Master Data Management, there are many tools that are optional, but there is one tool that is imperative to sustained success: graph databases.
What’s so special about graph databases?
Unlike standard databases which only focus on capturing individual data points, a graph database consists of two types of entries: nodes and relationships. The nodes are the individual data points, and the relationships describe how those data points are related to one another. That relationship information is built directly into the database and, importantly, is queryable just like any other piece of data.
By building relationships directly into the database, graph databases prevent programmers from having to create makeshift or workaround solutions in an attempt to query the data in a way that takes relationships into consideration. Rather, relationship-based queries are straightforward and can be returned nearly instantaneously. This level of query sophistication becomes increasingly important for a voluminous and complex set of data, like that of a company’s master data repository.
As an extremely pared down example, instead of storing a person’s name and place of employment without any context as a standard relational database would, a graph database would store both of those pieces of information as nodes as well as the relationship between those nodes — say, Person A “works for” Company B.
Currently, graph databases are commonly seen in recommendation engines, social media, and fraud prevention tools. In each of these cases, the additional context of relationships between data points helps companies gain a deeper understanding of important connections.
Why do graph databases make sense for Master Data Management?
There are three main reasons why graph databases should be a part of any company’s efforts to employ Master Data Management:
1) They help companies using MDM make sense of massive amounts of data.
There’s no doubt that data stored in graph databases as part of an MDM framework becomes eminently more discoverable and usable, especially in the context of the Semantic Web. They promote interoperability by making it easier to share data with relevant stakeholders, allowing those stakeholders to make data-driven decisions based on accurate, complete, and easy-to-interpret information.
Companies are always trying to make sense of their data and put it to more intelligent use. For a long time, the technology that allowed them to do so was too sophisticated for all except a few huge businesses like Amazon and Google. Now, graph databases put that technology within reach.
2) They allow companies to take advantage of machine-to-machine communications.
When thinking about moving to a Master Data Management model, companies must prioritize solutions that enable the shift to Web 3.0, also known as the Semantic Web. This is the next generation of the Internet, and it’s being defined by the common semantic standard (W3C) that allows machines to easily communicate with each other.
Graph databases are uniquely positioned to thrive in this environment. That’s because graph databases store information in a way which provides deeper levels of context and meaning, making connections and highlighting relationships in ways that help machines — from artificial intelligence applications to drones and robots — make better decisions.
They also help to close the gaps between machine understanding and human understanding. Making relational connections is something that has long been a relative advantage of humans, but graph databases are helping to close that gap. Building relational information into databases makes it possible for artificial intelligence to understand that information and do more of the legwork as a result.
3) They offer major benefits when it comes to data compliance.
As we think about interoperability and increasingly move toward sharing data with stakeholders, it’s impossible to ignore one of the most pressing reasons for the shift toward Master Data Management: compliance. Data privacy regulations are becoming the norm, and their stricter enforcement in the past year has led to costly penalties for businesses. Well over half of the EU’s $330 million worth of GDPR-related fines to date were issued in 2020, demonstrating an increasingly stringent approach to enforcement.
Graph databases, especially those secured by immutable ledger technology, offer the perfect combination of flexibility and security. In the context of MDM, they give companies the gift of data provenance and lineage – an ability to understand precisely how and where data is shared with a reproducible audit trail. This level of traceability helps organizations both ensure compliance on an internal level and prove it to regulators on an external level.
A single source of truth
Graph databases are a critical tool to leverage enterprise data to make efficient, information-based decisions and take advantage of next generation Internet technologies — all while being reliably secure. Just like Master Data Management, graph databases will play an important role in shaping the future of enterprise technology.
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