Semantic Arts exists to shepherd organizations on their Data-Centric journey.
Our core capabilities include:
• Semantic Knowledge Graph Development and Implementation
• Legacy Avoidance, Erosion, and Replacement
We can help your organization to fix the tangled mess of information in your enterprise systems while discovering ways to dissolve data silos and reduce integration debt.
What is Data-Centric?
Data-Centric is about reversing the priority of data and applications.
Right now, applications rule. Applications own “their” data (it’s really your data, but good luck with that). When you have 1,000 applications (which most large firms do) you have 1,000 incompatible data silos. This serves to further the entrenchment of legacy systems, with no real motivation for change.
Data-Centric says data and their models come first. Applications conform to the data, not the other way around. Almost everyone is surprised at the fundamental simplicity, once it’s been articulated.
It sounds simple, but fifty years of “application-centricity” is a hard habit to break. We specialize in helping firms make this transition. We recognize that in addition to new technology and design skills, a major part of most projects is helping shepherd the social change that this involves.
If you’re fed up with application-centricity and the IT-fad-of-the-month club, contact us.
Read More: What is Data-Centric?
What about those legacy systems?
The move to a more data-centric architecture requires thoughtful planning. Early phases look more like a surgical process of dealing with legacy applications in a way that realizes quick wins and begins to reduce costs, helping to fund future phases. Usually, it looks something like this:
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Legacy avoidance: The recognition that a firm has slowed down or stopped launching new application systems projects, and instead relies on the data that is in the shared knowledge graph.
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Legacy erosion: Occurs when firms take use cases that were being performed in a legacy system and instead implement them directly on the graph. Rather than wholesale legacy elimination (which is hard), this approach allows the functionality of the legacy system to be gradually decommissioned.
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Legacy replacement: Once enough of the data, functionality, and especially integration points have been shifted to the graph, legacy systems can be replaced. Not with “legacy modernization” systems, but with lightweight standalone use cases on the graph.
Read more: Incremental Stealth Legacy Modernization
ABOUT US
<p>Learn more about our mission, our history, and our team.</p>THOUGHT LEADERSHIP
<p>See how we are leading the way towards a data-centric future, and those who have taken note.</p>PROBLEMS WE SOLVE
<p>Discover how we can help you along the journey.</p>Taking a different path STARTS NOW. Become Data-Centric to simplify and enhance your enterprise information landscape:
5 Business Reasons for Implementing a Knowledge Graph Solution
1. Comprehensive data integration
2. Contextualized knowledge discovery
3. Agile knowledge sharing and collaboration
4. Intelligent search and recommendation
5. Future-proof data strategy
Integrating semantic capabilities into enterprise business processes has been the foundational shift that organizations such as Google, Amazon, and countless others have leveraged. The results are tangible: increased market share and revenue, lower costs, better customer experiences, reduced risks, and the promotion of innovation.
Semantic Arts’ professional services deliver true solutions (not gimmicks) for current and future information management challenges.
FROM OUR BLOG
Data-Centric’s Role in the Reduction of Complexity
Complexity Drives Cost in Information Systems A system with twice the number of lines of code will typically cost more than twice as much to build and maintain. There is no economy of scale in enterprise applications. There is dis economy of scale. In manufacturing, every doubling of output results in predictable reduction in the... Continue reading→
The Core Model at the Heart of Your Architecture
We have taken the position that a core model is an essential part of your data-centric architecture. In this article, we will review what a core model is, how to go about building one, and how to apply it both to analytics as well as new application development. What is a Core Model? A core... Continue reading→
Enterprise Ontology, Semantic Silos, and Cowpaths
Paving Cow Paths Numerous modern day streets in downtown Boston defy logic – until you realize that the city fathers literally paved over the transit system created and used by cows.* This gave the immediate benefit of getting places faster, while losing out on longer-term gains that designing a purpose-built street plan could have yielded. ... Continue reading→
The Data-Centric Revolution: Gaining Traction
There is a movement afoot. I’m seeing it all around me. Let me outline some of the early outposts. Data-Centric Manifesto We put out the data-centric manifesto on datacentricmanifesto.org over two years ago now. I continue to be impressed with the depth of thought that the signers have put into their comments. When you read the... Continue reading→
A Semantic Bank
In the last two months I’ve heard at least 6 financial institutions declare that they intended to become “A Semantic Bank.” We still haven’t seen even the slightest glimmer as to what any of them mean by that. Allow me to step into that breach. What follows is our take on what it would... Continue reading→
The Data-Centric Revolution: Integration Debt
Integration Debt is a Form of Technical Debt As with so many things, we owe the coining of the metaphor “Technical Debt” to Ward Cunningham and the agile community. It is the confluence of several interesting conclusions the community has come to. The first was that being agile means being able to make a simple... Continue reading→
The Inelegance of having Brothers and Sisters
This blog follows from a recent blog by Dan Carey called Screwdrivers and Properties. It points to a longer whitepaper on the topic of avoiding property proliferation. One way we keep the number of primitives small is to avoid creating a subproperty if its meaning is essentially the same as the superproperty, but has a more... Continue reading→
Whitepaper: Avoiding Property Proliferation
Domain and range for ontological properties are not about data integrity, but logical necessity. Misusing them leads to an inelegant (and unnecessary) proliferation of properties. Logical Necessity Meets Elegance Screwdrivers generally have only a small set of head configurations (flat, Phillips, hex) because the intention is to make accessing contents or securing parts easy (or... Continue reading→
Screwdrivers & Properties
Screwdrivers generally have only a small set of head configurations (flat, Phillips, hex) because the intention is to make accessing contents or securing parts easy (or at least uniform). Now imagine how frustrating it would be if every screw and bolt in your house or car required a unique screwdriver head. They might be grouped... Continue reading→
Binary Instances
Sometimes when we’re designing ontologies we’re faced with design choices that would lead us to create what we call “binary instances” or a situation where it will take the instantiation of two instances (often of different classes) in order to capture one concept. For instance we may be considering creating a patient instance that is... Continue reading→
gist: 12.x
gist: is our minimalist upper ontology. It is designed to have the maximum coverage of typical business ontology concepts with the fewest number of primitives and the least amount of ambiguity. Our gist: ontology is free (as in free speech and free beer–it is covered under the Creative Commons 3.0 attribution share-alike license). You can use as you see fit for any purpose, just give us attribution.
Semantic Arts Plays Well With Others
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