S&P Global Commodity Insights (formerly known as Platts) provides benchmark price assessments for the physical commodities markets. As we have noted before, if you hear  on the news that Brent Crude is trading at $100 a barrel, it’s likely that S&P Global  determined this price. 

The price of a given commodity – be it coal, crude oil, or steel – depends on several factors,  and in large part it depends on the commodity’s chemical characteristics. Crude oil, for  example, comes in a variety of “grades.” A quick look at this periodic table of crude oil grades will give a sense of how much variety there really is. If you hover around and click on some of the boxes, you will see that each crude oil grade is associated with a technical specification related to characteristics like sulfur concentration and API Gravity. These characteristics determine whether crude oil is “sweet” or “sour” (depending on its sulfur concentration) and whether it is “heavy” or “light” (depending on its API Gravity). 

While crude oils have other characteristics, the market has determined sulfur concentration and API Gravity to be the most important for the purposes of price assessments. The same thing holds for other types of commodities—there are “key market characteristics” for any commodity that will be important for pricing purposes. 

While this knowledge about commodities is commonplace at S&P, pinning down the  precise meaning of “commodity grade” turned out to be a significant challenge internally to  define. This is one place where Semantic Arts was able to provide value. We identified and analyzed the various concepts underpinning the idea of a commodity and produced a clear definition. 

One might think of a commodity grade as a “leaf” in the hierarchy of commodity types.  Crude oil is the general type, while the Saharan Blend is a specific grade at the bottom of the hierarchy. This is a good first pass definition, but it fails to get at the essence of what a commodity grade really is. 

To make headway in clarifying the definition, our strategy was to learn from subject matter experts about how the concept is used in practice. A key question was: When people talk  about commodity grades at S&P, what are they truly talking about? 

It turns out that whatever else people might mean by a commodity grade, it always includes the specification having features that the market cares about. Additionally, it is specific enough to be priced in the marketplace (by S&P or anyone else). With the hindsight of this analysis, we were able to create a clear and formal definition of the concept of a commodity grade using semantic standards (W3C) to express. 

This was the first step. It provided the basis for deeper data quality checks. This becomes critical to Commodity Insights; whereby S&P’s core business is selling accurate data under constantly changing conditions. 

The second step was to create a means for validating S&P’s data that commodity grades had specifications for the relevant key market characteristics. For example, any crude oil  grade should have a specification indicating an acceptable range of sulfur concentration.  To achieve this, we developed a solution using SPARQL & SHACL that returned validation  

reports to indicate which grades might be missing specifications for certain characteristics.  This semantic method proved significantly more efficient than using Excel spreadsheets,  manual entry (mistake prone) processes. 

While we used crude oil as an example to illustrate this idea, the framework we developed can be applied to all commodities. Most importantly, it instilled automation, data interoperability and consistent meaning across multiple teams that previously interpreted commodity grade with a different lens. The solution offers easier value change implementation saving countless man hours in aggregating spreadsheets with greater data integrity and traceability. 

There were a few important takeaways. First, it can take years of working on a project to  determine what something means with confidence. Delivering a semantic layer brings data reusability and data centric principles is solving a core challenge of dissolving data silos. 

Second, it’s possible to live with an unclear definition but once you’ve cracked the semantic code puzzle across the enterprise … efficiency and accuracy of knowledge reaches new realization. A tangible benefit of being confident by knowing exactly what something means is that problems arising due to the persistence of ambiguity will be a thing of the past. It’s  now built into the standard S&P operating procedures inherently with a data-centric,  semantic driving solution. 

On a previous project we worked with Sallie Mae to build an enterprise ontology for their loan business. After the ontology was complete, they decided to outsource a new line of loans to a third-party SaaS vendor. Shortly after making that decision, they realized that the new system would have completely different screens, and completely different messages and APIs from their existing systems. 

Their existing loan servicing systems had, collectively, about 50,000 attributes. The enterprise ontology we had previously designed had 1,500 concepts. They decided to use their ontology as a unifying principle to conform the old and new messages such that their customer-facing systems would not look schizophrenic. They had mature service-oriented  architecture but had not done much to unify or rationalize their messages. 

We helped them select the DXSI toolkit from Progress Software. We created a set of programs that converted the ontology into a form that DXSI could consume. (There were  many issues around translating multiple inheritance to single and converting many fully expressed notions from the ontology into flatter representations.) 

Much of our work for the remainder of this project involved discovering at a very specific level of detail: exactly what each of the fields in each of the new system’s messages actually meant. In many cases this required extensions to the original ontology, but for the most part the extensions were consistent with the original design. In the end we extended the enterprise ontology by only about 10%. 

The new system was implemented on time with a set of conformed messages that allowed a single presentation to the customer.

Broadridge: Legacy Understanding

This firm processes some 70% of all the back office of Wall Street. They have three major systems, for different types of financial instruments and jurisdictions. The three systems are barely integrated. Bringing a client up on any of their systems is a multiyear endeavor.  Getting combined reporting from these three systems is nearly impossible. 

They have embarked on a major initiative to create a path toward integration, initially integrating the systems they have, ultimately delivering a fully integrated system. 

One of the barriers is the complexity of the existing systems. They are massively complex and built on completely different architectures. 

One of the systems was designed with an extremely table-driven design. While this made it  very flexible, it also created performance problems as well as understanding problems.  There are only two people in the world who understand all the intricacies of the system. 

We built an ontology of the functions that the system covered. We then took all the metadata in the tables that drive the processing and loaded them into a triple store. We  constructed a series of SPARQL queries that allowed relatively new personnel to pose and  get answers to complex questions regarding how the existing system works. This has become a key input into their project to understand and integrate their systems to create legacy understanding.

Teacher Retirement System: Enterprise Architecture

The Teacher Retirement Systems is one of the largest pension funds in the country, with 1  million active teachers and 250,000 retirees. They run the organization on a series of aging mainframe systems. In the late 90s, they attempted a major upgrade to their technology, but finally had to abandon that path. Since then, they have mostly been front-ending their existing systems with newer proxy systems to deal with web-based clients and the like. 

We worked with them to design a future enterprise architecture, featuring SOA and ontology-driven messages. They have begun work on some of the early projects in the plan.

Investment Bank Risk and Controls

We worked with a large investment bank who are embarking on a series of projects to further automate their back office. One of their first tasks was to understand in greater detail what all the 5,000 people in the back office were doing. They built an “Economic Architecture” that was essentially the equivalent of a continually running Activity Based Costing project. They asked managers to estimate the percentage of time each of their reports spent on a standard list of activities. However, the activity list was not stabilizing, and many managers had difficulty deciding which of the many activities they should use. As this was slated to eventually become part of the reporting and perhaps eventually the charge back to the front office for the activities performed to settle some of these very complex instruments.

We were called in to create a rational basis for the activity taxonomy. We ended up decomposing the working list of 800 or so activities into a set of orthogonal facets. What was fascinating was that the facets were far, far simpler than the long complex list of activities. Once someone knew the facets (such as financial product, market, as well as a simple set of verbs and modifiers) they would know what all the activities were, as they were just concatenations of the facets. More interestingly we discovered as we performed this that the facets provided a level of categorization that it would be possible to instrument in the workflow and source systems. (The list of 800 activities were too arbitrary to allow for automation, but he facets were closely aligned with primitive concepts found in most systems).

We completed the redefinition and got agreement on the new activities. The new activities are in production, and they are looking at applying this concept beyond the back office operations.

Email: [email protected]
Phone: (970) 490-2224

Investment Bank: Economic Architecture

We worked with a large investment bank who are embarking on a series of projects to further automate their back office. One of their first tasks was to understand in greater detail what all the 5,000 people in the back office were doing. They built an “Economic  Architecture” that was essentially the equivalent of a continually running Activity Based  Costing project.  

They asked managers to estimate the percentage of time each of their reports spent on a standard list of activities. However the activity list was not stabilizing, and many managers  had difficulty deciding which of the many activities they should use. As this was slated to  eventually become part of the reporting and perhaps eventually the charge back to the  front office for the activities performed to settle some of these very complex instruments. 

We were called in to create a rational basis for the activity taxonomy. We ended up  decomposing the working list of 800 or so activities into a set of orthogonal facets. What was fascinating was that the facets were far simpler than the long complex list of activities.  Once someone knew the facets (such as financial product, market, as well as a simple set of verbs and modifiers), they would know what all the activities were, as they were just concatenations of the facets.  

More interestingly we discovered as we performed this that the facets provided a level of categorization that it would be possible to instrument in the workflow and source systems.  The list of 800 activities was too arbitrary to allow for automation, but the facets were closely aligned with primitive concepts found in most systems. 

We completed the redefinition and got agreement on the new activities. The new activities are in production, and they are looking at applying this concept beyond the back-office operations.

Washington State: SOA Design and Ontology 

In our initial engagement, we did a rapid but detailed review of 200 applications, interfaces,  current initiatives, long-range plan, and a new system being proposed. We found several  areas where they could leverage work in progress to speed up their new project initiative,  and several areas where, with a slight change in scope and priority, the new initiatives  would actually reduce the amount of redundancy and inconsistency.  

We helped them build a high-fidelity depiction of their current “as-is” state. The content from an existing, unread 400-page report was rendered, and massively updated, to a very large graphic of the as-is condition. We then worked with them to define their long-term  SOA architecture with shared services.

Ontology Consultant Job Description

As a Semantic Arts ontologist, you will be essential in fixing the tangled mess of information in  client enterprise systems and promoting a world where enterprise information is widely  understood and easily accessed by all who have permission. Come work with the best in the  business on interesting projects with global leaders! 

Working together with other ontology consultants, you will take existing design artifacts and  work with subject matter experts to convert models to formal semantic expressions for clients.  We work with a diverse set of clients of all sizes and across industries. Therefore, you can expect a variety of work across many domains of knowledge. We have a strong sense of team, with no rigid hierarchy and place a high value on individual input. 

Requirements: 

• A passion for information and knowledge modeling 
• Must be trained in ontological development, either through formal training or on-the-job development. 
• Should have experience in data modeling or related analytical skills. 
• Strong interpersonal communication skills, experience managing client, stakeholder, and internal interactions. 
• Experience in OWL, RDF, SPARQL and the ability to program against triplestores
• A desire to learn new domains of knowledge in a fast-paced environment.
• Bachelor’s degree in Computer Science, Information Systems, Knowledge Management,  Engineering, Philosophy, Business, or similar. 

Nice to Have: 

• Prior use or understanding of W3C semantic web standards 
• Advanced academic degree preferred. 

About Us: 

Promoting a vision of Data-Centric Architecture for more than 20 years, people are catching on!  We have been awarded the 2022 ″Colorado Companies to Watch”, 2022 “Top 30 Innovators of the Year”, 2021 “30 Innovators to Watch”, and 2020 “30 Best Small Companies to Watch”.  Semantic Arts is growing quickly and expanding our domains, projects, and roles. We have assembled what might be the largest team of individuals passionately dedicated to this task,  making Semantic Arts a great place to develop skills and grow professionally in this exciting field. 

What We Offer: 

• Remote Position, with travel for onsite work with clients required (up to 3 days every 3  weeks) 
• Professional development fund to develop skills, attend conferences, and advance your career. 
• Medical, Dental, and Vision Benefits 
• SIMPLE IRA with company match 
• Student Loan Reimbursement
• Annual Bonus Potential 
• Equipment Purchase Assistance 
• Employee Assistance Program 

Employment Type: 

Full-time 

Authorization: 

Candidates must be authorized to work for any employer within the US, UK, or Canada. We are not currently able to sponsor visas or hire outside of those countries. 

Compensation: 

Compensation for this position varies based on experience, billable utilization, and other factors.  Entry-level ontologists start around $70,000 USD annually and generally rise quickly, with the overall average being approximately $150,000 USD, and about 1/3 of consultants averaging more than $175,000 USD. More details shared during the interview process. 

Semantic Arts is committed to the full inclusion of all qualified individuals. In keeping with our commitment, we will take steps to assure that people with disabilities are provided reasonable accommodations. Accordingly, if a reasonable accommodation is required to fully participate in  the job application or interview process, to perform the essential duties of the position, and/or to  receive all other benefits and privileges of employment, please contact our HR representative at  [email protected]. 

Semantic Arts is an Equal Opportunity Employer. We respect and seek to empower each  individual and support the diverse cultures, perspectives, skills, and experiences within our  workforce. We support an inclusive workplace where employees excel based on merit,  qualifications, experience, ability, and job performance.

Data-Centric Credentialling

In order to ensure that clients can get what they expect when they buy software or services that purport to be “data-centric” we are going to implement a credentialling program. The program will be available at three levels. 

Implementation Awards 

These are assessments and awards given to clients for projects or enterprises to recognize the milestones on their journey to becoming completely data-centric.  

It is a long journey. There is great benefit along the way, and these awards are meant to  recognize progress on the journey 

Software Certification 

The second area is in certify that software meets the goals of the data-centric approach.  There will be two major categories: 

• Middleware – databases, messaging systems, and non-application-specific tools that  might be used in a data-centric implementation will be evaluated on its consistency  with the approach 
• Applications – as described in the book “Real Time Financial Accounting, the Data Centric Way” we expect that vertical industries will be far easier and more consistent with the data-centric approach. Horizontal applications will be evaluated based on  their ease of being truly integrated with the rest of a data-centric enterprise.  Adhering to open models and avoiding proprietary structures will also improve the rating in this area. 

Professional Services  

There will be two levels of professional services credentialling, one based on what you know and the other on what you’ve done.  

The “what you know “will be based on studying and testing akin to the Project Management  Institute of the Data Management DMBOK. 

The “what you’ve done” recognizes that a great deal of the ability to deliver these types of projects is based on field experience.