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
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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
How do I track down and debug errors in my ontology?
I will answer this in the context of OWL ontologies where the errors are found using Hermit or Fact++ as the inference engine. I happen to use Protégé, but other ontology tools have similar functionality. There are a few cases. First, if the ontology is inconsistent, then one or more classes will be inferred to...Continue reading→
Are There Any Tools to Help Find Common Pitfalls in Ontologies?
Yes. Check out http://oeg-lia3.dia.fi.upm.es/oops/. The authors looked through the literature and discovered 29 common pitfalls that arise when building ontologies. Code has been written to automatically find 21 of the 29 pitfalls. Try it out and give the authors feedback. The whole community will benefit.
Oh brave new world… How semantic technology can improve your IT productivity while protecting your legacy IT investments
Workers, professionals and managers are all becoming acutely aware of the discrepancy between the performance of consumer based technology and their internal IT department. The gap between the information and services they can get for ‘free’ on the web vs. what they get from IT is creating a high level of frustration and dissatisfaction between IT...Continue reading→
What is gist, and what does it stand for?
gist is a minimalist upper ontology designed to aid the production of business oriented ontologies. It does not stand for anything; it is a word, meaning “a general understanding.” gist is freely available at www.semanticarts.com/gist and is licensed through the Creative Commons Share Alike license. It has been under development by Semantic Arts for over...Continue reading→
Semantic technology – the path to becoming more perceptive, intelligent and collaborative
Semantic technologies will create incremental value by making us more perceptive, intelligent and collaborative learners. The knowledge modeling capabilities of semantic technology will encourage us to create abstract models of our ideas, run simulations to evaluate and improve them and then implement the new models faster. This will enable us to solve problems faster, implement...Continue reading→
Effectiveness, efficiency and strategic advantage – the promise of semantic technology
Semantic technology makes us more effective, efficient and strategic. Effectiveness. A system that can easily be modeled to reflect the way you think or the processes you manage, increases your effectiveness. You can solve problems faster because you can quickly apply your problem solving process to a greater range of structured and unstructured data and you can...Continue reading→
What is the real difference between “on premise” systems and SaaS?
They can be the same software. That isn’t the essential difference. There are many exceptions and edge cases but the central difference in moving from on premise to SaaS is that you are moving from a large capital budget item that takes a long time to approve to the steady drip of transaction or usage...Continue reading→
What is the pragmatic difference between a public cloud and a private cloud?
It mostly depends on whether you are the cloudor or the cloudee. If you are consuming cloud services theoretically there isn’t much difference, but practically there is. You will likely be paying more for a private cloud (not so much because it is inherently more expensive, but because there isn’t any competition) and consuming the...Continue reading→
How do you handle a two phased commit in an SOA environment?
There are four approaches (and a couple of the can be combined). The first is to implement on top of XP compliant data bases that allow the services to accept a commit to but still wait for the final confirmation. Given the heterogeneity of most environments this is highly unlikely. The second is to design...Continue reading→
Can an ontology provide enough detailed information to build logical data models in the future?
A good ontology simplifies your information management systems because it creates an enterprise-wide definition of what things are called and their interrelationships. In our experience, even the complex requirements of multi-billion, global companies can be organized into ontologies of about 1000 concepts. Because ontologies reuse properties they can represent the real world complexities of a...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.
