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Dave McComb I read “How Big Things Get Done” when it first came out about six months ago.[1] I liked it then. But recently, I read another review of it, and another coin dropped. I’ll let you know what the coin was toward the end of this article, but first I need to give you my … Read more

Phil Blackwood Every Enterprise can benefit from having a simple, standard way to represent quantitative data. In this blog post, we will provide examples of how to use the new gist model of quantitative data released in gist version 13. After illustrating key concepts, we will look at how all the pieces fit together and … Read more

This article originally appeared at The Data-Centric Revolution: Best Practices and Schools of Ontology Design – TDAN.com. Subscribe to TDAN directly for this and other great content! I was recently asked to present “Enterprise Ontology Design and Implementation Best Practices” to a group of motivated ontologists and wanna-be ontologists. I was flattered to be asked, … Read more

Gartner has put “Knowledge Graphs” at the peak of inflated expectations. If you are a Knowledge Graph software vendor, this might be good news. Companies will be buying knowledge graphs without knowing what they are. I’m reminded of an old cartoon of an executive dictating into a dictation machine: “…and in closing, in the future … Read more

For the last 20 years,  Semantic Arts has been helping firms design and build enterprise ontologies to get them on the data-centric path. We have learned many lessons from the enterprise that can be applied in the construction of smart city ontologies. What is similar between the enterprise and smart cities? They both have thousands … Read more

There was a time when RDF and triplestores were only seen through the lens of massive data integration. Teams went to great extremes to show how many gazillion triples per second their latest development could ingest, and large integrations did likewise with enormous datasets. This was entirely appropriate and resulted in the outstanding engineering achievements … Read more

One of the ideas we promote is elegance in the core data model in a Data-Centric enterprise.  This is harder than it sounds.  Look at most application-centric data models: you would think they would be simpler than the enterprise model, after all, they are a small subset of it.  Yet we often find individual application … Read more

Achieving clarity in your data ecosystem is more difficult than ever these days. With false news, cyber-attacks, social media, and a consistent blitz of propaganda – how does one sort it all out? Even our data and information practices have suffered from this proliferation (data warehouse, data lake, data fabric, data mesh … this is … Read more

Written by Peter Winstanley The Data-Centric Architecture Forum was a success! If growth of participants is an indicator of the popularity of an idea, then the Third Annual Data-Centric Architecture Forum is reflecting a strong increase in popularity, for this year over 230 participants joined together for a three-day conversation.  This is a huge increase on … Read more

We recently recast large portions of the telecom Frameworx Information Model into an Enterprise Ontology using patterns and reusable parts of the gist upper ontology.  We found that extending gist with the information content of the Frameworx model yields a simple telecom model that is easy to manage, federate, and extend, as described below.  Realizing … Read more

Let’s Stop Basing Capital Cost Decisions on Lagging Indicators. Remember the good old days of emerging digital technology? Accessing information through a dial-up internet connection. Saving data to floppy discs or CDs. Sending emails to have them printed for storage. Mobile connectivity was new, exciting, and… slow compared to what we have today. In the … Read more

by Phil Blackwood, Ph.D. Without a doubt every business needs to have a clear idea of who its customers are and would love to have a 360 degree view of each customer. However, this customer data is typically scattered across hundreds of applications, its meaning is embedded in code written years ago, and much of … Read more

Those who are familiar with Marie Kondo know that she is a ruthless disposer. If you’ve read parts one and two of this series, you know that the process is more nuanced than just “throw it all away,” but we’ve come to the point in the process where it’s important to focus on discarding. If … Read more

We’ve been working on something we call “SemOps” (like DevOps but for Semantic Technology + IT Operations).  The basic idea is how can we create a pipeline to go from proposed enterprise ontology or taxonomy enhancements to “in-production” as frictionlessly as possible. As so often happens, when we shine the Semantic Light on a topic … Read more

The COVID-19 pandemic is a clear example of how healthcare practitioners require swift access to enormous amounts of diverse information to efficaciously treat patients. They must synthesize individual data (vital signs, clinical history, demographics, and more) with rapidly evolving knowledge about COVID-19 and make decisions relevant to the conditions from which specific patients suffer.ners rely … Read more

Envisioning Your Dream System with the Marie Kondo Method Before you begin gathering your belongings, discarding, or reorganizing, Marie Kondo asks you to envision your dream lifestyle.  She insists that this is the critical first step to ensuring success with her method, and she provides some guidance on how to do so and examples from … Read more

We just finished a conversation with a client who was justifiably proud of having centralized what had previously been a very decentralized business function (in this case, it was HR, but it could have been any of a number of functions). They had seemingly achieved many of the benefits of becoming data-centric through decentralization: all … Read more

Why is Marie Kondo so popular for home organization? Marie Kondo released her book, “The Life-Changing Magic of Tidying up,” almost ten years ago and has since gained much notoriety for motivating millions of people to de-clutter their homes, offices, and lives. Some people are literally buried in their possessions with no clear way to … Read more

Recently IDC predicted that IT spending will drop by 5% due to the COVID-19 pandemic.[1] Last week, Gartner went further by predicting that IT spending would drop by 8% or $300 Billion.[2] (Expect a prediction bidding war.) Both were consistent: highest hit areas would be devices, followed by IT service and enterprise software. The predicted $100 billion … Read more

In my last article I described Meaning-First data modeling. It’s time to dig into its predecessor and antithesis, which I call Structure-First data modeling, specifically looking at how two assumptions drive our actions. Assumptions are quite useful since they leverage experience without having to re-learn what is already known. It is a real time-saver. Until … Read more

In our previous installment of this two-part series we introduced a couple of ideas. First, data governance may be more similar to DevOps than first meets the eye. Second, the rise of Knowledge Graphs, Semantics and Data-Centric development will bring with it the need for something similar, which we are calling, “SemOps” (Semantic Operations). Third, when you … Read more

Last year, we decided to call the first annual Data Centric Architecture Conference a Forum which resulted in DCAF. It didn’t take long for attendees to start calling the event “decaf” but they were equally quick to point out that the forum was anything but decaf. We had a great blend of presentations ranging from discussions … Read more

Google ontologist, Denny Vrandečić started a vigorous thread on the question of what constitutes a decade. See for example, the article: “People Can’t Even Agree On When The Decade Ends”. This, is a re-emergence of the question from 20 years ago on whether the new millennium will/did start on January 1 of 2000 or 2001.This … Read more

Person uses language. Person speaks language. Person learns language. We spend the early years of life learning vocabulary and grammar in order to generate and consume meaning. As a result of constantly engaging in semantic generation and consumption, most of us are semantic savants. This Meaning-First approach is our default until we are faced with … Read more

At Semantic Arts we often come across ontologies whose developers seem to take pride in the number of classes they have created, giving the impression that more classes equate to a better ontology. We disagree with this perspective and as evidence, point to Occam’s Razor, a problem-solving principle that states, “Entities should not be multiplied … Read more

I’m at a graph conference. The general sense is that property graphs are much easier to get started with than Knowledge Graphs. I wanted to explore why that is, and whether it is a good thing. It’s a bit of a puzzle to us, we’ve been using RDF and the Semantic Web stack for almost … Read more