Semantic Arts Admin, Author at Semantic Arts

How to Take Back 40-60% of Your IT Spend by Fixing Your Data

October 20, 2025November 6, 2023 by Semantic Arts Admin

Creating a semantic graph foundation helps your organization become data-driven while significantly reducing IT spend

Organizations that quickly adapt to changing market conditions have a competitive advantage over their peers. Achieving this advantage is dependent on their ability to capture, connect, integrate, and convert data into insight for business decisions and processes. This is the goal of a “data-driven” organization. However, in the race to become data-driven, most efforts have resulted in a tangled web of data integrations and reconciliations across a sea of data silos that add up to between 40% – 60% of an enterprise’s annual technology spend. We call this the “Bad Data Tax”. Not only is this expensive, but the results often don’t translate into the key insights needed to deliver better business decisions or more efficient processes.

This is partly because integrating and moving data is not the only problem. The data itself is stored in a way that is not optimal for extracting insight. Unlocking additional value from data requires context, relationships, and structure, none of which are present in the way most organizations store their data today.

Solution to the Data Dilemma

The good news is that the solution to this data dilemma is actually quite simple. It can be accomplished at a fraction of the cost of what organizations spend each year supporting the vast industry of data integration workarounds. The pathway forward doesn’t require ripping everything out but building a semantic “graph” layer across data to connect the dots and restore context. However, it will take effort to formalize a shared semantic model that can be mapped to data assets, and turn unstructured data into a format that can be mined for insight. This is the future of modern data and analytics and a critical enabler to getting more value and insight out of your data.

This shift from relational to graph approach has been well-documented by Gartner who advise that “using graph techniques at scale will form the foundation of modern data and analytics” and “graph technologies will be used in 80% of data and analytics innovations by 2025.” Most of the leading market research firms consider graph technologies to be a “critical enabler.” And while there is a great deal of experimentation underway, most organizations have only scratched the surface in a use-case-by-use-case fashion. While this may yield great benefits for the specific use case, it doesn’t fix the causes behind the “Bad Data Tax” that organizations are facing. Until executives begin to take a more strategic approach with graph technologies, they will continue to struggle to deliver the needed insights that will give them a competitive edge.

Modernizing Your Data Environment

Most organizations have come of age in a world dominated by technology. There have been multiple technology revolutions that have necessitated the creation of big organizational departments to make it all work. In spite of all the activity, the data paradigm hasn’t evolved much. Organizations are still managing data using relational technology invented in the 1970’s. While relational databases are the best fit for managing structured data workloads, they are not good for ad hoc inquiry and scenario-based analysis.

Data has become isolated and mismatched across repositories and silos due to technology fragmentation and the rigidity of the relational paradigm. Enterprises often have thousands of business and data silos–each based on proprietary data models that are hard to identify and even harder to change. This has become a liability that diverts resources from business goals, extends time-to-value for analysts, and leads to business frustration. The new task before leadership is now about fixing the data itself.

Fixing the data is possible with graph technologies and web standards that share data across federated environments and between interdependent systems. The approach has evolved for ensuring data precision, flexibility, and quality. Because these open standards are based on granular concepts, they become reusable building blocks for a solid data foundation. Adopting them removes ambiguity, facilitates automation, and reduces the need for data reconciliation.

Data Bill of Rights

Organizations need to remind themselves that data is simply a representation of real things (customers, products, people, and processes) where precision, context, semantics, and nuance matter as much as the data itself. For those who are tasked with extracting insight from data, there are several expectations that should be honored– that the data should be available and accessible when needed, stored in a format that is flexible and accurate, retains the context and intent of the original data, and is traceable as it flows through the organization.

This is what we call the “Data Bill of Rights”. Providing this Data Bill of Rights is achievable right now without a huge investment in technology or massive disruption to the way the organization operates.

Strategic Graph Deployment

Many organizations are already leveraging graph technologies and semantic standards for their ability to traverse relationships and connect the dots across data silos. These organizations are often doing so on a case-by-case basis covering one business area and focusing on an isolated application, such as fraud detection or supply chain analytics. While this can result in faster time-to-value for a singular use case, without addressing the foundational data layers, it results in another silo without gaining the key benefit of reusability.

The key to adopting a more strategic approach to semantic standards and knowledge graphs starts at the top with buy-in across the C-suite. Without this senior sponsorship, the program will face an uphill battle of overcoming the organizational inertia with little chance of broad success. However, with this level of support, the likelihood dramatically increases of getting sufficient buy-in across all the stakeholders involved in managing an organization’s data infrastructure.

While starting as an innovation project can be useful, forming a Graph Center of Excellence, will have an even greater impact. It can give the organization a dedicated team to evangelize and execute the strategy, score incremental wins to demonstrate value and leverage best practices and economies of scale along the way. They would be tasked with both building the foundation as well as prioritizing graph use cases against organizational focuses.

One key benefit from this approach is the ability to start small, deliver quick wins, and expand as value is demonstrated. There is no getting around the mandate to initially deliver something practical and useful. A framework for building a Graph Center of Excellence will be published in the coming weeks.

Scope of Investment Required

Knowledge graph advocates admit that a long tail of investment is necessary to realize its full potential. Enterprises need basic operational information including an inventory of the technology landscape and the roadmap of data and systems to be merged, consolidated, eliminated, or migrated. They need to have a clear vision of the systems of record, data flows, transformations, and provisioning points. They need to be aware of the costs associated with the acquisition of platforms, triplestore databases, pipeline tools, and other components needed to build the foundational layer of the knowledge graph.

In addition to the plumbing, organizations need to also understand the underlying content that supports business functionality. This includes the reference data about business entities, agents, and people. The taxonomies and data models about contract terms and parties, the meaning of ownership and control, notions of parties and roles, and so on. These concepts are the foundation of the semantic approach. These might not be exciting, but they are critical because it is the scaffolding for everything else.

Initial Approach

When thinking about the scope of investment, the first graph-enabled application can take anywhere from 6-12 months from conception to production. Much of the time needs to be invested in getting data teams aligned and mobilized – which underscores the essential nature of leadership and the importance of starting with the right set of use cases. It need to be operationally viable and solve a real business problem. The initial use case has to be important for the business.

With the right strategic approach in perspective, the first delivery is infrastructure plus pipeline management and people. This gets the organization the MVP including an incremental project plan and rollout. The second delivery should consist of the foundational building blocks for workflow and reusability. This will prove the viability of the approach.

Building Use Cases Incrementally

The next series of use cases should be based on matching functionality to capitalize on concept reusability. This will enable teams to shift their effort from building the technical components to adding incremental functionality. This translates to 30% of the original cost and a rollout that could be three times faster. These costs will continue to decrease as the enterprise expands reusable components – achieving full value around the third year.

The strategic play is not the $3-$5 million for the first few domains, but the core infrastructure required to run the organization moving forward. It is absolutely possible to continue to add use cases on an incremental level, but not necessarily the best way to capitalize on the digital future. The long-term cost efficiency of a foundational enterprise knowledge graph (EKG) should be compared to the costs of managing thousands of silos. For a big enterprise, this can be measured in hundreds of millions of dollars – before factoring in the value proposition of enhanced capabilities for data science and complying with regulatory obligations to manage risks.

Business Case Summary

Organizations are paying a “Bad Data Tax” of 40% – 60% of their annual IT spend on the tangled web of integrations across their data silos. To make matters worse, following this course does not help an organization achieve their goal of being data-driven. The data itself has a problem. This is due to the way data is traditionally stored in rows, columns, and tables that do not have the context, relationships, and structure needed to extract the needed insight.

Adding a semantic graph layer is a simple, non-intrusive solution to connect the dots, restore context, and provide what is needed for data teams to succeed. While the Bad Data Tax alone quantifiably justifies the cost of solving the problem, it scarcely scratches the surface of the full value delivered. The opportunity cost side, though more difficult to quantify, is no less significant with the graph enabling a host of new data and insight capabilities (better AI and data science outcomes, increased personalization and recommendations for driving increased revenue, more holistic views through data fabrics, high fidelity digital twins of assets, processes, and systems for what-if analysis, and more).

While most organizations have begun deploying graph technologies in isolated use cases, they have not yet applied them foundationally to solving the Bad Data Tax and fixing their underlying data problem. Success will require buy-in and sponsorship across the C-suite to overcome organizational inertia. For best outcomes, create a Graph Center of Excellence focused on strategically deploying both a semantic graph foundation and high-priority use cases. The key will be in starting small, delivering quick wins with incremental value and effectively communicating this across all stakeholders.

While initial investments can start small, expect initial projects to take from 6-12 months. To cover the first couple of projects, a budget between $1.5-$3 million should be sufficient. The outcomes will justify further investment in graph-based projects throughout the organization, each deploying 30% faster and cheaper than early projects through leveraging best practices and economies of scale.

Conclusion

The business case is compelling – the cost to develop a foundational graph capability is a fraction of the amount wasted each year on the Bad Data Tax alone. Addressing this problem is both easier and more urgent than ever. Failing to develop the data capabilities that graph technologies offer can put organizations at a significant disadvantage, especially in a world where AI capabilities are accelerating and critical insight is being delivered in near real time. The opportunity cost is significant. The solution is simple. Now is the time to act.

This article originally appeared at How to Take Back 40-60% of Your IT Spend by Fixing Your Data – Ontotext, and was reposted

DCA Forum Recap: US Homeland Security

October 20, 2025October 18, 2023 by Semantic Arts Admin

How US Homeland Security plans to use knowledge graphs in its border patrol efforts

During this summer’s Data Centric Architecture Forum, Ryan Riccucci, Division Chief for U.S. Border Patrol – Tucson (AZ) Sector, and his colleague Eugene Yockey gave a glimpse of what the data environment is like within the US Department of Homeland Security (DHS), as well as how transforming that data environment has been evolving.

The DHS celebrated its 20-year anniversary recently. The Federal department’s data challenges are substantial, considering the need to collect, store, retrieve and manage information associated with 500,000 daily border crossings, 160,000 vehicles, and $8 billion in imported goods processed daily by 65,000 personnel.

Riccucci is leading an ontology development effort within the Customs and Border Patrol (CBP) agency and the Department of Homeland Security more generally to support scalable, enterprise-wide data integration and knowledge sharing. It’s significant to note that a Division Chief has tackled the organization’s data integration challenge. Riccucci doesn’t let leading-edge, transformational technology and fundamental data architecture change intimidate him.

Riccucci described a typical use case for the transformed, integrated data sharing environment that DHS and its predecessor organizations have envisioned for decades.

The CBP has various sensor nets that monitor air traffic close to or crossing the borders between Mexico and the US, and Canada and the US. One such challenge on the Mexican border is Fentanyl smuggling into the US via drones. Fentanyl can be 50 times as powerful as morphine. Fentanyl overdoses caused 110,000 deaths in the US in 2022.

On the border with Canada, a major concern is gun smuggling via drone from the US. to Canada. Though legal in the US, Glock pistols, for instance, are illegal and in high demand in Canada.

The challenge in either case is to intercept the smugglers retrieving the drug or weapon drops while they are in the act. Drones may only be active for seven to 15 minutes at a time, so the opportunity window to detect and respond effectively is a narrow one.

Field agents ideally need to see enough visual real-time, mapped airspace information on the sensor activated, allowing them to move quickly and directly to the location. Specifics are important; verbally relayed information by contrast can often be less specific, causing confusion or misunderstanding.

The CBP’s successful proof of concept involved a basic Resource Description Framework (RDF) triple, semantic capabilities with just this kind of information:

Sensor → Act of sensing → drone (SUAS, SUAV, vehicle, etc.)

In a recent test scenario, CBP collected 17,000 records that met specified time/space requirements for a qualified drone interdiction over a 30-day period.

The overall impression that Riccucci and Yockey conveyed was that DHS has both the budget and the commitment to tackle this and many other use cases using a transformed data-centric architecture. By capturing information within an interoperability format, the DHS has been apprehending the bad guys with greater frequency and precision.

Contributed by Alan Morrison

HR Tech and The Kitchen Junk Drawer

October 20, 2025September 19, 2023 by Semantic Arts Admin

I often joke that when I started with Semantic Arts nearly two years ago, I had no idea a solution existed to a certain problem that I well understood. I had experienced many of the challenges and frustrations of an application-centric world but had always assumed it was just a reality of doing business. As an HR professional, I’ve heard over the years about companies having to pick the “best of the worst” technologies. Discussion boards are full of people dissatisfied with current solutions – and when they try new ones, they are usually dissatisfied with those too!

The more I have come to understand the data-centric paradigm, the more I have discovered its potential value in all areas of business, but especially in human resources. It came as no surprise to me when a recent podcast by Josh Bersin revealed that the average large company is using 80 to 100 different HR Technology systems (link). Depending on who you ask, HR is comprised of twelve to fifteen key functions – meaning that we have an average of six applications for each key function. Even more ridiculously, many HR leaders would admit that there are probably even more applications in use that they don’t know about. Looking beyond HR at all core business processes, larger companies are using more than two hundred applications, and the number is growing by 10% per year, according to research by Okta from earlier this year (link). From what we at Semantic Arts have seen, the problem is actually much greater than this research indicates.

Why Is This a Problem?

Most everyone has experienced the headaches of such application sprawl. Employees often have to crawl through multiple systems, wasting time and resources, either to find data they need or to recreate the analytics required for reporting. As more systems come online to try to address gaps, employees are growing weary of learning yet another system that carries big promises but usually fails to deliver (link). Let’s not forget the enormous amount of time spent by HR Tech and other IT resources to ensure everything is updated, patched and working properly. Then, there is the near daily barrage of emails and calls from yet another vendor promising some incremental improvement or ROI that you can’t afford to miss (“Can I have just 15 minutes of your time?”).

Bersin’s podcast used a great analogy for this: the kitchen drawer problem. We go out and procure some solution, but it gets thrown into the drawer with all the other legacy junk. When it comes time to look in the drawer, either it’s so disorganized or we are in such a hurry that it seems more worthwhile to just buy another app than to actually take the time to sort through the mess.

Traditional Solutions

When it comes to legacy applications, companies don’t even know where to start. We don’t know who is even using which system, so we don’t dare to shut off or replace anything. So we end up with a mess of piecemeal integrations that may solve the immediate issue, but just kicks the technical debt down the road. Sure, there are a few ETL and other integration tools out there that can be helpful, but without a unified data model and a broad plan, these initiatives usually end up in the drawer with all the other “flavor of the month” solutions.

Another route is to simply put a nice interface over the top of everything, such as ServiceNow or other similar solutions. This can enhance the employee experience by providing a “one stop shop” for information, but it does nothing to address the underlying issues. These systems have gotten quite expensive, and can run $50,000-$100,000 per year (link). The systems begin to look like ERPs in terms of price and upkeep, and eventually they become legacy systems themselves.

Others go out and acquire a “core” solution such as SAP, Oracle, or another ERP system. They hope that these solutions, together with the available extensions, will provide the same interface benefits. A company can then buy or build apps that integrate. Ultimately, these solutions are also expensive and become “black boxes” where data and its related insights are not visible to the user due to the complexity of the system. (Intentional? You decide…). So now you go out and either pay experts in the system to help you manipulate it or settle for whatever off-the-shelf capabilities and reporting you can find. (For one example of how this can go, see link).

A Better Path Forward

Many of the purveyors of these “solutions” would have you believe there is no better way forward; but those familiar with data-centricity know better. To be clear, I’m not a practioner or technologist. I joined Semantic Arts in an HR role, and the ensuing two years have reshaped the way I see HR and especially HR information systems. I’ll give you a decent snapshot as I understand it, along with an offer that if your interested in the ins and outs of these things I’d be happy to introduce you to someone that can answer them in greater detail.

Fundamentally, a true solution requires a mindset shift away from application silos and integration, towards a single, simple model that defines the core elements of the business, together with a few key applications that are bound to that core and speak the same language. This can be built incrementally, starting with specific use cases and expanding as it makes sense. This approach means you don’t need to have it “all figured out” from the start. With the adoption of an existing ontology, this is made even easier … but more on that later.

Once a core model is established, an organization can begin to deal methodically with legacy applications. You will find that over time many organizations go from legacy avoidance to legacy erosion, and eventually to legacy replacement. (See post on Incremental Stealth Legacy Modernization). This allows a business to slowly clean out that junk drawer and avoid filling it back up in the future (and what’s more satisfying than a clean junk drawer?).

Is this harder in the short term than traditional solutions? It may appear so on the surface, but really it isn’t. When a decision is made to start slowly, companies discover that the flexibility of semantic knowledge graphs allows for quick gains. Application development is less expensive and applications more easily modified as requirements change. Early steps help pay for future steps, and company buy-in becomes easier as stakeholders see their data come to life and find key business insights with ease.

For those who may be unfamiliar with semantic knowledge graphs, let me try to give a brief introduction. A graph database is a fundamental shift away from the traditional relational structure. When combined with formal semantics, a knowledge graph provides a method of storing and querying information that is more flexible and functional (more detail at link or link). Starting from scratch would be rather difficult, but luckily there are starter models (ontologies) available, including one we’ve developed in-house called gist, which is both free and freely available. By building on an established structure, you can avoid re-inventing the wheel.

HR departments looking to leverage AI and large language models in the future will find this data-centric transformation even more essential, but that’s a topic for another time.

Conclusion

HR departments face unique challenges. They deal with large amounts of information and must justifying their spending as non-revenue producing departments. The proliferation of systems and applications is a drain on employee morale and productivity and represents a major source of budget drain.

By adopting data-centric principles and applying them intentionally in future purchasing and application development, HR departments can realize greater strategic insights while saving money and providing a richer employee experience.

Taken all the way to completion, adoption of these technologies and principles would mean business data stored in a single, secured location. Small apps or dashboards can be rapidly built and deployed as the business evolves. No more legacy systems, no more hidden data, no more frustration with systems that simply don’t work.

Maybe, just maybe, this model will provide a success story that leads the rest of the organization to adopt similar principles.

JT Metcalf is the Chief Administrative Officer at Semantic Arts, managing HR functions along with many other hats.

Extending an upper-level ontology (like GIST)

March 24, 2023 by Semantic Arts Admin

Michael Sullivan is a Principle Cloud Solutions Architect at Oracle. Article reprinted with permission (original is here)

If you have been following my blogs over the past year or so they you will know I am a big fan of adopting an upper-level ontology to help bootstrap your own bespoke ontology project. Of the available upper-level ontologies I happen to like gist as it embraces a “less is more” philosophy.

Given that this is 3rd party software with its own lifecycle, how does one “merge” such an upper ontology with your own? Like most things in life, there are two primary ways.

CLONE MODEL

This approach is straightforward: simply clone the upper ontology and then modify/extend it directly as if it were your own (being sure to retain any copyright notice). The assumption here is that you will change the “gist” domain into something else like “mydomain”. The benefit is that you don’t have to risk any 3rd party updates affecting your project down the road. The downside is that you lose out on the latest enhancements/improvements over time, which if you wish to adopt, would require you to manually re-factor into your own ontology.

As the inventors of gist have many dozens of man-years of hands-on experience with developing and implementing ontologies for dozens of enterprise customers, this is not an approach I would recommend for most projects.

EXTEND MODEL

Just as when you extend any 3rd party software library you do so in your own namespace, you should also extend an upper-level ontology in your own namespace. This involves just a couple of simple steps:

First, declare your own namespace as an owl ontology, then import the 3rd party upper-level ontology (e.g. gist) into that ontology. Something along the lines of this:

<https://ont.mydomain.com/core> 
    a owl:Ontology ;
    owl:imports <https://ontologies.semanticarts.com/o/gistCore11.0.0> ;
    .

Second, define your “extended” classes and properties, referencing appropriate gist subclasses, subproperties, domains, and/or range assertions as needed. A few samples shown below (where “my” is the prefix for your ontology domain):

my:isFriendOf 
     a owl:ObjectProperty ;
     rdfs:domain gist:Person ;
     .
my:Parent 
    a owl:Class ;
    rdfs:subClassOf gist:Person ;
    .
my:firstName 
    a owl:DatatypeProperty ;
    rdfs:subPropertyOf gist:name ;
    .

The above definitions would allow you to update to new versions of the upper-level ontology* without losing any of your extensions. Simple right?

*When a 3rd party upgrades the upper-level ontology to a new major version — defined as non-backward compatible — you may find changes that need to be made to your extension ontology; as a hypothetical example, if Semantic Arts decided to remove the class gist:Person, the assertions made above would no longer be compatible. Fortunately, when it comes to major updates Semantic Arts has consistently provided a set of migration scripts which assist with updating your extended ontology as well as your instance data. Other 3rd parties may or may not follow suit.

Thanks to Rebecca Younes of Semantic Arts for providing insight and clarity into this.

Knowledge Graph Modeling: Time series micro-pattern using GIST

September 24, 2025February 27, 2023 by Semantic Arts Admin

Michael Sullivan is a Principle Cloud Solutions Architect at Oracle. Article reprinted with permission (original is here)

For any enterprise, being able to model time series is more than just important, in many cases it is critical. There are many examples but some trivial ones include “Person is employed By Employer” (Employment date-range), “Business has Business Address” (Established Location date-range), “Manager supervises Member Of Staff” (Supervision date-range), and so on. But many developers who dabble in RDF graph modeling end up scratching their heads — how can one pull that off if one can’t add attributes to an edge? While it is true that one can always model things using either reification or leveraging RDF Quads (see my previous blog semantic rdf properties) now might be a good time to take a step back and explore how the semantic gurus at Semantic Arts have neatly solved how to model time series starting with version 11 of GIST, their free upper-level ontology (link below).

First a little history. The core concept of RDF is to “connect” entities via predicates (a.k.a. “triples”) as shown below. Note that either predicate could be inferred from the other, bearing in mind that you need to maintain at least one explicit predicate between the two as there is no such thing in RDF as an subject without a predicate/object. Querying such data is also super simple.

Typical entity to entity relationships in RDF

So far so good. In fact, this is about as simple as it gets. But what if we wanted to later enrich the above simple semantic relationship with time-series? After all, it is common to want to know WHEN Mark supervised Emma. With out-of-the-box RDF you can’t just hang attributes on the predicates (I’d argue that this simplistic way of thinking is why property graphs tend to be much more comforting to developers). Further, we don’t want to throw out our existing model and go through the onerous task of re-modeling everything in the knowledge graph. Instead, what if we elevated the specific “supervises” relationship between Mark and Emma to become a first-class citizen? What would that look like? I would suggest that a “relation” entity that becomes a placeholder for the “Mark Supervises Emma” relationship would fit the bill. This entity would in turn reference Mark via a “supervision by” predicate while referencing Emma via a “supervision of” predicate.

Ok, now that we have a first-class relation entity, we are ready to add additional time attributes (i.e. triples), right? Well, not so fast! The key insight that in GIST, is that the “actual end date” and “actual start date” predicates as used here specify the precision of the data property (rather than letting the data value specifying the precision), which in our particular use case we want to be the overall date, not any specific time. Hence our use of gist:actualStartDate and gist:actualEndDate here instead of something more time-precise.

The rest is straightforward as depicted in the micro-pattern diagram shown immediately below. Note that in this case, BOTH the previous “supervised by” and “supervises” predicates connecting Mark to Emma directly can be — and probably should be — inferred! This will allow time-series to evolve and change over time while enabling queryable (inferred) predicates to always be up-to-date and in-sync. It also means that previous queries using the old model will continue to work. A win-win.

Time series micro-pattern using GIST

A clever ontological detail not shown here: A temporal relation such as “Mark supervises Emma” must be gist:isConnectedTo a minimum of two objects — this cardinality is defined in the GIST ontology itself and is thus inherited. The result is data integrity managed by the semantic database itself! Additionally, you can see the richness of the GIST “at date time” data properties most clearly in the expression of the hierarchical model in latest v11 ontology (see Protégé screenshot below). This allows the modeler to specify the precision of the start and end date times as well as distinguishing something that is “planned” vs. “actual”. Overall a very flexible and extensible upper ontology that will meet most enterprises’ requirements.

"at date time" data property hierarchy as defined in GIST v11

Further, this overall micro-pattern, wherein we elevate relationships to first-class status, is infinitely re-purposable in a whole host of other governance and provenance modeling use-cases that enterprises typically require. I urge you to explore and expand upon this simple yet powerful pattern and leverage it for things other than time-series!

One more thing…

Given that with this micro-pattern we’ve essentially elevated relations to be first class citizens — just like in classic Object Role Modeling (ORM) — we might want to consider also updating the namespaces of the subject/predicate/object domains to better reflect the objects and roles. After all, this type of notation is much more familiar to developers. For example, the common notation object.instance is much more intuitive than owner.instance. As such, I propose that the traditional/generic use of “ex:” as used previously should be replaced with self-descriptive prefixes that can represent both the owner as well as the object type. This is good for readability and is self-documenting. And ultimately doing so may help developers become more comfortable with RDF/SPARQL over time. For example:

ex:_MarkSupervisesEmma becomes rel:_MarkSupervisesEmma
ex:supervisionBy becomes role:supervisionBy
ex:_Mark becomes pers:_Mark

Where:

@prefix rel: <www.example.com/relation/>. @prefix role: <www.example.com/role/>. @prefix pers: <www.example.com/person/>.

Alan Morrison: Zero-Copy Integration and Radical Simplification

February 14, 2023February 14, 2023 by Semantic Arts Admin

Dave McComb’s book Software Wasteland underscored a fundamental problem: Enterprise software sometimes costs 1,000 times more than it ought to. The poster child for cost overruns was highlighted in the book was Healthcare.gov, a public registration system for the US Affordable Care Act, enacted in 2010. By 2018, the US Federal government had spent $2.1 billion to build and implement the system. Most of that money was wasted. The government ended up adopting many of the design principles embodied in an equivalent system called HealthSherpa, which cost $1 million to build and implement.

In an era where the data-centric architecture Semantic Arts advocates should be the norm, application-centric architecture still predominates. But data-centric architecture doesn’t just reduce the cost of applications. It also attacks the data duplication problem attributable to poor software design. This article explores how expensive data duplication has become, and how data-centric, zero-copy integration can put enterprises on a course to simplification.

Data sprawl and storage volumes

In 2021, Seagate became the first company to ship three zettabytes worth of hard disks. It took them 36 years to ship the first zettabyte. six years to ship the second zettabyte, and only one additional year to ship the third zettabyte.

The company’s first product, the ST-506, was released in 1980. The ST-506 hard disk, when formatted, stored five megabytes (10002). By comparison, an IBM RAMAC 305, introduced in 1956, stored five to ten megabytes. The RAMAC 305 weighed 10 US tons (the equivalent of nine metric tonnes). By contrast, the Seagate ST-506, 24 years later, weighed five US pounds (or 2.27 kilograms).

A zettabyte is the equivalent of 7.3 trillion MP3 files or 30 billion 4K movies, according to Seagate. When considering zettabytes:

1 zettabyte equals 1,000 exabytes.
1 exabyte equals 1,000 petabytes.
1 petabyte equals 1,000 terabytes.

IDC predicts that the world will generate 178 zettabytes of data by 2025. At that pace, “The Yottabyte Era” would succeed The Zettabyte Era by 2030, if not earlier.

The cost of copying

The question becomes, how much of the data generated will be “disposable” or unnecessary data? In other words, how much data do we actually need to generate, and how much do we really need to store? Aren’t we wasting energy and other resources by storing more than we need to?

Let’s put it this way: If we didn’t have to duplicate any data whatsoever, the world would only have to generate 11 percent of the data it currently does. In 2021 terms, we’d only need to generate 8.7 zettabytes of data, compared with the 78 zettabytes we actually generated worldwide over the course of that year.

Moreover, Statista estimates that the ratio of unique to replicated data stored worldwide will decline to 1:10 from 1:9 by 2024. In other words, the trend is toward more duplication, rather than less.

The cost of storing oodles of data is substantial. Computer hardware guru Nick Evanson, quoted by Gerry McGovern in CMSwire, estimated in 2020 that storing two yottabytes would cost $58 trillion. If the cost per byte stored stayed constant, 40 percent of the world’s economic output would be consumed in 2035 by just storing data.

Clearly, we should be incentivizing what graph platform Cinchy calls “zero-copy integration”–a way of radically reducing unnecessary data duplication. The one thing we don’t have is “zero-cost” storage. But first, let’s finish the cost story. More on the solution side and zero-copy integration later.

The cost of training and inferencing large language models

Model development and usage expenses are just as concerning. The cost of training machines to learn with the help of curated datasets is one thing, but the cost of inferencing–the use of the resulting model to make predictions using live data–is another.

“Machine learning is on track to consume all the energy being supplied, a model that is costly, inefficient, and unsustainable,” Brian Bailey in Semiconductor Engineering pointed out in 2022. AI model training expense has increased with the size of the datasets used, but more importantly, as the amount of parameters increases by four, the amount of energy consumed in the process increases by 18,000 times. Some AI models included as many as 150 billion parameters in 2022. The more recent ChatGPT LLM Training includes 180 billion parameters. Training can often be a continuous activity to keep models up to date.

But the applied model aspect of inferencing can be enormously costly. Consider the AI functions in self-driving cars, for example. Major car makers sell millions of cars a year, and each one they sell is utilizing the same carmaker’s model in a unique way. 70 percent of the energy consumed in self-driving car applications could be due to inference, says Godwin Maben, a scientist at electronic design automation (EDA) provider Synopsys.

Data Quality by Design

Transfer learning is a machine learning term that refers to how machines can be taught to generalize better. It’s a form of knowledge transfer. Semantic knowledge graphs can be a valuable means of knowledge transfer because they describe contexts and causality well with the help of relationships.

Well-described knowledge graphs provide the context in contextual computing. Contextual computing, according to the US Defense Advanced Research Projects Agency (DARPA), is essential to artificial general intelligence.

A substantial percentage of training set data used in large language models is more or less duplicate data, precisely because of poorly described context that leads to a lack of generalization ability. Thus the reason why the only AI we have is narrow AI. And thus the reason large language models are so inefficient.

But what about the storage cost problem associated with data duplication? Knowledge graphs can help with that problem also, by serving as a means for logic sharing. As Dave has pointed out, knowledge graphs facilitate model-driven development when applications are written to use the description or relationship logic the graph describes. Ontologies provide the logical connections that allow reuse and thereby reduce the need for duplication.

FAIR data and Zero-Copy Integration

How do you get others who are concerned about data duplication on board with semantics and knowledge graphs? By encouraging data and coding discipline that’s guided by FAIR principles. As Dave pointed out in a December 2022 blogpost, semantic graphs and FAIR principles go hand in hand. https://www.semanticarts.com/the-data-centric-revolution-detour-shortcut-to-fair/

Adhering to the FAIR principles, formulated by a group of scientists in 2016, promotes reusability by “enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals.” When it comes to data, FAIR stands for Findable, Accessible, Interoperable, and Reusable. FAIR data is easily found, easily shared, easily reused quality data, in other words.

FAIR data implies the data quality needed to do zero-copy integration.

Bottom line: When companies move to contextual computing by using knowledge graphs to create FAIR data and do model-driven development, it’s a win-win. More reusable data and logic means less duplication, less energy, less labor waste, and lower cost. The term “zero-copy integration” underscores those benefits.

Alan Morrison is an independent consultant and freelance writer on data tech and enterprise transformation. He is a contributor to Data Science Central and TechTarget sites with over 35 years of experience as an analyst, researcher, writer, editor and technology trends forecaster, including 20 years in emerging tech R&D at PwC.

The Data-Centric Revolution: An Interview with Dave McComb

December 5, 2022 by Semantic Arts Admin

Are today’s economics of software projects and support inevitable? No.

They are a product of the fact that the industry has collectively chosen the application-centric route to implementing new functionality. When every business problem calls for a new application and every new application comes with its own database, what you really get is runaway complexity. Many clients have thousands of applications. But it isn’t inevitable. A few firms have shown the way out: data-centric development.

In this ground-breaking interview with Business Rules Community, Dave McComb explains what being ‘data-centric’ is about and how it can be made to work.

Six Enterprise Knowledge Graph Anti-Patterns

November 10, 2022 by Semantic Arts Admin

The truth is that despite the outsider’s perception that the world of technology just keeps getting better, faster and less expensive, over 70% of enterprise digital transformations fail to achieve their objectives (McKinsey, 2019). Most commentary on the subject points the finger at executive priorities, strategic goal-setting and organizational management. But the seldom discussed root cause of this failure is complexity, more specifically integration debt. Integration debt is the ongoing cost of integrating data from a large number of enterprise applications that each have their own local incompatible data models.

Leading organizations are starting to address this proliferation of local data models by adopting a more “data-centric” architecture. A data-centric architecture models a singular representation of the organization’s core data in a knowledge graph. The knowledge graph simplifies and harmonizes data from enterprise applications. Organizations can see a 1000-fold decrease in complexity across the enterprise data landscape.

As organizations make this move, they stumble falter and fail. This leads to what Gartner’s calls the trough of disillusionment. But it doesn’t have to. The technology is ready to deliver with the right skills and approach. With careful execution and the right Sherpa, you can make it up the mountain. The ascent is worth it. Knowledge graphs are changing the game for those who succeed. We are sharing six common anti-patterns for enterprise knowledge graphs to keep you heading in the right direction.

Anti-pattern #1 — Agreeing with the Status Quo

“This is how enterprise architecture is done”

The most progress deadening perspective you can hold is that the future is going to be just like the present. The most challenging aspect of moving to an enterprise knowledge graph is not a technical one, it is the mindset that embraces the status quo. In order to break through to those who are entrenched is to educate them and show them a better way preferably with their own data.

People who make decisions in your organization must view this technology for the radical departure that it is from relational databases, data warehouses or data lakes. If you don’t secure this distinction, you will be in danger of creating yet another silo of data, in this case a semantic silo.

You must personally step boldly enough into this mindset and educate yourself to the point that you can see a coherent vision of this next generation architecture. Your vision has to be nearly white-hot, because in the beginning no one will understand you and they will disbelieve that such a revolutionary approach is possible. Convince yourself it is possible.

But it is not just the boldness of vision but its broadness as well. The vision is a transformation for the entire organization, and you must keep this in mind. If you can find a few advocates across functional boundaries, you may be able to demonstrate what data harmonization is capable of: astounding flexibility, intricate insights, a broad purview of your enterprise, holistic yet simple.

Anti-pattern #2 — Fad Surfing

“This is cool, lets buy it and see what it does”

The tech world moves quickly from one favored technology to the next. Fad surfing refers to plucking technologies from the latest trend and trying them out. We have seen NoSQL, Hadoop, Kafka, Data Lakes, Data Warehouses, AI and Snowflake all fly by and get picked up by organizations struggling to get a handle on their data. The problem with fad surfing is that organizations get used to trying and quickly discarding technologies. This might work if you were trying out an application or a shirt but with enterprise architecture it’s a little like trying on a new foundation for your house to see how it works.

Data-centric thinking is not strictly speaking a technology. Is it an approach that uses a knowledge graph, semantic modeling and model driven everything. The move needs to square with corporate strategic planning and have people dedicated to making a longer-term investment in design, execution and integration of the core ontology. Impulse buying is not a recipe for success. There is too great a maturation phase than is sustainable by someone trying a technology out and then showing it off to gain support. With this mindset you will be forced to move on quickly perhaps feeling like you have exercised due diligence but rejecting it because either your organization or the technology was “not ready”.

Anti-pattern #3 — Too Small

“We can get that from the old system”

Have you ever done a proof of concept successfully only to have it shelfed because there was no enthusiasm for a larger implementation. It happens, a lot. The same thing will happen with your enterprise knowledge graph proof of concept if the scope is too small. If you show someone a graph solution that embodies a single domain, they feel they could have gotten the answer using the status-quo relational system.

The way to avoid this frustration is by selecting 2–3 domains that are currently difficult to reconcile. This demonstrates that even if queries could be answered before, they would have been time consuming and difficult one-offs and required a great deal of specialized knowledge to complete. This demonstrates the knowledge graph’s serviceability, its ability to answer questions easily that span difficult to reconcile data sources with different levels of abstraction or other domains that do not share a common nomenclature. In addition, it demonstrates the simplification and logical grounding of semantic concepts. By simplifying the entire structure non-experts can understand and query the data. A working example of data democratization.

Anti-pattern #4 — Too Big

“Ok, now what?”

It is possible to develop an enterprise knowledge graph that encompasses nearly the entire organization within nine months to a year if you have a team of ontologists ready to go. The problem is that when presented with an all-encompassing enterprise knowledge graph, few technical leaders know what to do with it, or even if they want to deploy this model at scale. The prospect of making a change of this magnitude drains the energy and enthusiasm of the people who contemplate it. You may have to swallow an elephant but perhaps the waiter could bring just the ear to start with.

The paradox is that you must include enough of the enterprise perspective so that you do not create an overall design that locks you into design (ontological) commitments that are structurally incompatible with other areas of your business. The key is to survey enough of the rest of the enterprise at a sufficient but not exhaustive level such that you can future proof the design to a large degree and rework is minimal. One good thing is that with a graph data structure even rework is less destructive than relational models that have rigid schemas and deep structural commitments.

The use of a minimalist upper-level ontology designed for such a purpose can help by establishing reusable patterns that can be applied in one domain today and somewhere else a year from now with consistent results. We are biased but Gist, the ontology from Semantic Arts, has over 100 enterprise implementations under its belt and is both simple and comprehensive in scope. It provides a set of covering concepts, semantic primitives, that can be combined to describe your organization and sub domains with minimal extension. So think big, looking broadly at the enterprise, but start small with a couple of sub-domains.

Anti-pattern #5 — Data Governance

“We’ve got this covered”

Data governance initiatives are a growth industry, with good reason. Organizations know they face an overwhelming challenge to make sense of all of their data. They also have the obligations to secure personally identifiable PII data, comply with domestic and international data regulations and be able to provide audit support. This is all before enhancing the ability to find and reuse data for the purposes of analysis and operational improvements.

So called master data management initiatives designate certain data in the organization as “golden records” then establish a practice around managing and proliferating the most up-to-date version of those golden records. This is seen as a solution in today’s enterprise environment. What it really is is the overgrowth of a set of technologies and practices that are built to correct for the shortcomings of the application-centric world we live in. We are becoming expert at solving the wrong problem and waste effort paying the wages of integration debt. The solution is data-centric. In a data-centric world there would not be literally thousands of instances of the same social security number (a real example).

The anti-pattern here is to believe that since you have a master data management initiative you are solving the problem of organizing your data and making it maximally findable and usable. You are not. In some ways it makes the problem worse by entrenching systems of records and adding additional layers of automation to ship updated data around at greater cost and effort.

Anti-pattern #6 — Data Hoarding

“It’s too valuable to share”

Some organizations are better than others at sharing data among internal groups. There are good reasons why data should be kept secure. We have all heard of incidents of data breeches that have cost companies lots of money and prestige. Data hording though, is a practice of going beyond protecting data to preventing its use by others with legitimate needs. This is a pathological behavior that is typically borne from the perception that the data is valuable and sharing it with others would reduce me or my team’s value to the organization. Currently, people must come and have permission to get summary level data from the data hoarders, and they like it.

Two lessons here, first if you or someone you know is a hoarder try to get help. If data hoarders cannot be reasoned with, and some cannot, you should identify them early in the process and not count on them to participate. Instead, develop the system elsewhere until it has enough demonstrable value and political support to perhaps persuade them to come along. The process can be a positive one. Sometimes data hoarders can be made to see the value of the harmonization of data. They themselves can see how combining other’s data with their own can provide value to them. Seeing this, it may be possible that they come around to seeing value in living in harmony in the graph.

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Originally posted at Medium.com

The Greatest Sin of Tabular Data

September 9, 2022September 6, 2022 by Semantic Arts Admin

We recently came across this great article titled “The greatest sin of tabular data”. It is an excellent summary of the kind of work we do for our clients and how they benefit.

You can read it at The greatest sin of tabular data · A blog @ nonodename.com

The journey of capturing the meaning to data is an elusive process. If 80% of data science is simply data wrangling, how can we do better actually providing value by making sense of that data?

With a disciplined approach and levering RDF capabilities, Semantic Arts can help to create clear, defined data, saving time and money and driving true value instead of getting bogged down in simply trying to understand data.

As stated by the author, “We can do better!”

Reach out to Semantic Arts today to see how we can help.

Original article at nonodename.com/Dan Bennett via LinkedIn Post.

Get the gist: start building simplicity now

January 20, 2025July 21, 2022 by Semantic Arts Admin

While organizing data has always been important, a noticeably profound interest in optimizing information models with Semantic Knowledge graphs has arisen. LinkedIn, AirBnB, in addition to giants Google and Amazon use graphs, but without a model for connecting concepts with rules for membership buyer recommendations and enhanced searchability (follow your nose) capabilities would lack accuracy.

Drum roll please … Introduce the ontology.

It is a model that supports semantic knowledge graph reasoning, inference, and provenance enablement. Think of an ontology as the brain giving messages to the nervous systems (the knowledge graph). An ontology organizes data into well-defined categories with clearly defined relationships. This model represents a foundational starting point that allows humans and machines to read, understand, and infer knowledge based on its classification. In short, this automatically figures out what is similar and what is different.

We’re asked often, where do I start?

Enter ‘gist’ a minimalist business ontology (model) to springboard transitioning information into knowledge. With more than a decade of refinement grounded in simplicity, ‘gist’ is designed to have the maximum coverage of typical business ontology concepts with the fewest number of primitives and least amount of ambiguity. ‘gist’ is available for free under a Creative Commons license and is being applied and extended within a number of business use cases and utilized by countless industries.

Recently, senior Ontologist Michael Uschold has been sharing an introductory overview of ‘gist’, maintained by Semantic Arts.

One compelling difference from most publicly available ontologies, ‘gist’ has an active governance and best practices community, called the gist Council. The council meets virtually on the first Thursday of every month to discuss how to use ‘gist’ and make suggestions on its evolution.

See Part I of Michael’s introduction here:

See Part II of Michael’s introduction here:

Stay tuned for the final installment!

Interested in gist? Visit Semantic Arts – gist

See more informative videos on Semantic Arts – YouTube