A few weeks ago, I listened to the MLW20 manufacturing industry breakout session and was fascinated by the discussion around the optimal data integration processes for developing and industrializing knowledge in an enterprise. In other words, the process for transforming data into knowledge.
Every speaker, from organizations that included DuPont, U.S. AFRL, Eaton, and Chevron, spoke to the common key ingredient for making this systematically possible at an enterprise level: Semantic Data.
If you’re reading this post, you’ve probably heard of semantic data, but may not have considered the critical role semantic data plays in enabling the transformation of data into knowledge.
Continue reading to find out why semantic data should be a core element in your data transformation approach and how some of our customers have applied semantic technology to improve the value of their platforms, applications, and products.
The deciding factor between the success and failure of a project is not the volume of knowledge at your disposal, but rather its discoverability.”
U.S. National Aeronautics and Space Administration (NASA)
To understand the role and importance of semantic data, we need to start with an overview of the steps used to transform data into knowledge:
When these steps are used in a business environment, it is often referred to as the process or system for developing Business Intelligence (BI) or Knowledge Management (KM). KM usually encompasses the activities of BI, but includes additional activities related to creation of new knowledge and the sharing of knowledge throughout an organization.
There are many different types of end-user applications that can fall under the BI or KM umbrella, such as search, data visualization, data mining, on-line analytical processing, and content management to name a few. But, all of these applications are only as effective as the data that feeds them from the data curation step (#2 above).
Contextualization of your data makes it easier for you to discover information, and for information to discover you. If the data is not contextualized prior to the data discovery step (#3), then data users at your company will spend more time searching for the data they need and less time generating useful information and insights.
Advancements in the area of data discovery effectiveness can have substantial impacts on productivity. Research shows that knowledge workers can spend anywhere from 20% to 30% of their time just searching for information, equating to millions of dollars in lost productivity costs for individual companies.
This need for contextualized data makes the application of semantic data foundational for improving data discovery and insights.
Semantic Graph technology, referred to as Semantics, provides more intelligence in the data layer by making it possible to model complex relationships so that computers can understand context.
Semantics provides enterprises with a standard format for defining the relationships between disparate facts and provides context for those facts. The standard way to represent semantic data is with RDF Triples (Resource Description Framework), and the standard query language is SPARQL.
Triples can form the fabric of a knowledge graph to help improve search and discovery. For example, it may be helpful to surface facts about London when a user searches for London, or facts about who owns a company and what its subsidiaries are when a user searches for that company.
Importantly, the use of Semantics enables inferencing. Inferencing is a very powerful mechanism that allows the system to draw inferences between facts in your database automatically. This can help uncover relationships you never knew existed.
With semantics, organizations have a powerful capability for dramatically improving data and information discovery to increase productivity, lower risks, and accelerate insights.
During the manufacturing industry session and as part of MLW20, experts shared how they are using semantics to improve data discovery and deliver smarter applications. Here are a few of those use cases:
The Boeing Company, a top aerospace manufacturer, uses MarkLogic Semantics to help unify, govern, and secure data so it can be safely accessed and shared with employees, partners, and customers. Boeing is also practicing Model-Based Systems Engineering (MBSE), using MarkLogic to discover, link, share, and manage data in a secure and highly scalable manner to improve the systems development lifecycle.
Find out more about this use case by watching Boeing’s video.
DuPont, a science-based company with over 200 years of heritage and a diverse business portfolio in nutrition, biotechnology, electronics, transportation, safety, and construction, has deployed MarkLogic to deliver on its vision and roadmap for corporate digital services at the company. DuPont leverages MarkLogic to capture and integrate product and market intelligence, and deliver “accelerated insights” to support competitive marketing, product launch, and technical service.
Find out more about this use case by watching DuPont’s video.
The Air Force Research Laboratory (AFRL), a scientific research organization operated by the U.S. Air Force Materiel Command, created the HyperThought™ data management platform on MarkLogic — a scalable, agile, and flexible way to make exabytes of data discoverable and securely shareable for 700 scientists and engineers, with thousands more external collaborators.
Watch AFRL’s video to learn more.
With our resources section it is easy to find the content you need to learn how to transform your data into knowledge. Just use the navigation bar to find content, or you can click on links below to go directly to relevant topics and video content:
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Successfully responding to changes in the business landscape requires data agility. Learn what visionary organizations have done, and how you can start your journey.
Sharing data can be relatively easy. Sharing our specialized knowledge about data is harder – and current approaches don’t scale.
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