Overcoming Data Integration Challenges to Unleash Huge Potential: Knowledge Graphs 101
Doing integrations in the storage layer is kind of like asking the first person you date to marry you. It’s not necessarily a bad idea, but it’s risky and requires a big upfront commitment. As a hasty guess, when it comes to integrating data, moving and copying data in order to integrate it can work well, but it comes with risks and requires a large upfront obligation. If something changes, and something always changes, you have to rerun all the jobs, make new copies, and move the data again.
Because you committed to a particular point of view so early and then consolidated it at the storage layer, you also aggressively ruled out other possibilities. Early ties and tight couplings are all fun and fun until things don’t work out and where are we?
While the enterprise dataset may seem like an objective fact – like a hard, fixed, unchanging thing that represents the world exactly as it is – in reality, it’s more instrumental than all that. Enterprise data is something; it represents a part of the world and we manipulate data largely to manipulate the world. Which means that data is full of subjective human choices and human values. Data is really made up of answers to very human questions: What data should I collect? What data needs to be transformed? What data should be summarized or aggregated and what data matters? What matters and what are we trying to accomplish? Each of these choices becomes or influences a decision or a modeling transformation or an invariant or a business rule. The technical apparatus for data integration and analysis is imbued with human values.
The result is two facts: first, integrating data at the storage layer rules out the possibilities; and, second, that human data is a function of human choice. It follows that when an analyst has a new idea about how to organize and understand data or a strategic initiative is prompted by a regulatory decision or a competitor zigsags instead of zags, organizations can having to throw everything away and start over.
Suddenly, data teams have to recreate a new version of the data and train a new dataset. Which means they have to go through the process of reshaping, transforming, and summarizing data all over again, including rerunning week-long ELT jobs and exploding schedules, budgets, bandwidth, and storage.
What if they didn’t have to?
Leverage knowledge graphs to accelerate insights
Whether called data sprawl or data silos, data resides in many places. In its natural state, data is disconnected, both from the other data it needs to be connected to and from the business context that gives it meaning. The natural disconnect from enterprise data presents a challenge for organizations that must drive business transformation with data. It’s just to say that it’s a challenge for everyone. Data management practices that limit range of motion and increase rigidity, including integrating data only at the storage layer, impede everything from application development, science, and analytics data, process automation, and even reporting and compliance.
However, there is an alternative to greedy data integration at the storage layer; namely, connecting the data lazily to the compute layer. Late binding and loose coupling in data architectures increase flexibility and range of motion. Companies are increasingly adopting new data management techniques, including data structures and knowledge graphs (KGs) to unify. Knowledge graphs provide a flexible and reusable data layer that enables organizations to answer complex queries across all data sources and provides unprecedented connectivity with contextualized data, represented and organized in the form of intelligent graphs .
Designed to capture the ever-changing nature of information, Knowledge Graphs embrace new data, definitions, and requirements in a fluid, simple way, and in a way that promotes radical data reuse in large organizations. This means that as the business scales and greater volumes of data, sources and use cases emerge, they can be absorbed unmanageably and
loss of accessibility, while fully representing the current extent of what the company knows.
Dissect the components of a knowledge graph
Enterprise Knowledge Graph is a technology that combines the capabilities of a graph database with a knowledge toolkit, including AI, ML, data quality, and reusable smart graph models, for the purpose to unify data at scale. Simply put, KGs know everything the business knows because they can represent data sprawl and silos in a connected data structure.
Since knowledge graphs are built on graph database technologies, they natively represent and store data as entities (i.e. nodes) with relationships called edges with other entities. Like traditional graph databases, knowledge graphs quickly navigate chains of these edges to find relationships between various pieces of data. By tracking many chains of edges at once, they can identify many-to-many interrelationships at multiple levels of granularity, from summary rollups to the finer details of a record, so that relevant data can be retrieved through a single query. Unlike simple graph databases, knowledge graph platforms query connected data using data virtualization and query federation techniques, moving data integration from the storage layer to the calculation layer.
As data and queries become more complex, the benefits of the Knowledge Graph intelligent data model increase because it can connect silos of data into facts that constitute contextualized knowledge. A knowledge graph also contains tools that enable businesses to add a richer layer of semantics to support knowledge representation in the graph and enhance machine understanding, which simple graph databases do not. do not.
For example, when a simple graph database knows that there is an interrelation between a person node in silo A and an organization node in silo B, a knowledge graph also understands the nature of this interrelation. and can query that relationship without first moving or copying data. from silos A and B to silo C (i.e. a simple graph database).
Fighting the 3Vs of Big Data: how KGs reveal hidden information
Stepping back and looking at the big picture of the modern data analytics stack, there are many tools and techniques to address the volume and velocity challenges of big data. The cloud means, for example, never running out of storage again and it makes it easier to operate distributed systems, even if they are still very difficult to build and maintain.
But the big data variety challenge has been largely ignored until recently. Perhaps the greatest contribution of knowledge graphs is to solve the problem of variety by providing a consistent view over heterogeneous data. Note, however, that the view is homogeneous and consistent while the underlying data remains heterogeneous and even physically separated.
Knowledge graphs encompass the large, diverse, and ever-changing data found in modern enterprises using full abstraction (i.e., semantic graphs) based on declarative data structures and languages of request. They combine key technologies that work together to unify data at scale, including a powerful and reusable data model; virtual graph capabilities to handle structured, semi-structured, and unstructured data; and inference and reasoning services.
Given the importance and potential of data, companies cannot ignore the costs associated with not being able to access or apply the knowledge accumulated in the company. In today’s hybrid multicloud world of increasing complexity and specialization, data sprawl and data silos are hardly avoidable, but manageable as long as data can be unified with each other. By applying knowledge graphs to take full advantage of what the business knows, they can grow alongside the business and enable users to use this untapped data and insights to help them innovate and achieve real competitive advantage.
About the Author: Kendall Clark is founder and CEO of Stardog, an Enterprise Knowledge Graph (EKG) platform provider. You can follow the company on Twitter @StardogHQ.
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