In a dramatic development, the new release of SBVR (1.1) has replaced the term “fact type” with “verb concept”, and the term “fact model” with “concept model”, for all business-facing use.[1] Why the problems with “fact type” and “fact model”? Let me see if I can explain. First some background: Since its inception in the early 2000s, the OMG standard SBVR[2] has focused on “fact type” and “fact model”. That’s no accident – the underpinning of SBVR in formal logic is based on the work of Terry Halpin, who in turn based his work on Sjir Nijssen’s. Sjir Nijssen was using the terms for database models as early as the 1970s. By the way, both bodies of work are world-class. Now to the problems: If someone gives you an example or instance of a customer, where is that customer? In a database? No, of course not. The customer is out there in the real world. Similarly, suppose someone gives you an example of some customer visiting some retail store. Where did that visitation take place? In a database? Again, of course not. The visitation also happened out there in the real world. The bottom line is that when most people talk about things, those things exist or happen in the real world. But not if those people happen to be logicians or database gurus. Then instances of the things they talk about formally are likely to be in some database – i.e., data. The formal terminology is usually more refined – e.g., “population of facts” – but it is what it is. And it’s not the same stuff as is in the real world. Where does that lead you? If you’re a logician or database guru, you need to classify all the facts – hence “fact type”. You also need a model of all the fact types – hence “fact model”. If you’re not a logician or database guru, however, you’re clearly going to need something else. What exactly fits the bill? Here’s a clue: Databases hold data; those data represents facts. Those facts have meaning, but to understand that meaning you need to understand the concepts that are used. In business basically all we have is words to refer to things in the real world. What do those words communicate? The words communicate what you mean; that is, the ideas or concepts you have in your head when you say or write them. So what we need – or more precisely, what we need to share – is a model of what you mean by those words. In short we need a concept model. More on SBVR The world might or might not need another information modeling standard. The point is debatable. The soul of SBVR, however, lies in meaning[3] and language – what concepts we mean by the words we use in business communications (especially but not exclusively business rules). In the standards landscape that focus sets SBVR apart. What kind of language concepts do we need in organizing and expressing meaning? The answer is really quite simple (once you see it) – you need nouns and verbs. Those nouns and verbs stand for concepts – noun concepts and verb concepts, respectively. For example:

• The noun “customer” might stand for what is meant by the definition “one that purchases some commodity or service”. 
• The verb “visits” (as in “some customer visits a retail outlet”) might stand for what is meant by the definition “customer physically appears at retail outlet”

There is no other practical way to communicate business concepts and establish relationships among them. You need nouns and verbs to write sentences and convey meaning – it’s as simple as that. Once you look at the problem this way, forcing “fact model” and “fact type” on business people is unnatural and unnecessary. It commits a cardinal sin in business analysis – using unnatural terms for natural business concepts. The most natural terms for the concepts meant by SBVR are “concept model” and “verb concept”. About Business Rules For my part, I didn’t arrive at this understanding through the path above – or for that matter any other path you are likely to guess. The need for the shift dawned on me when I saw business rules being included in populations of facts. Hold on, how could a rule be treated as a fact?! Well, exactly! To a logician or database guru, however, treating a rule as a fact makes perfect sense. Formal logic is all about propositions. A business rule is a proposition taken to be true – in other words, a fact. So of course business rules belong in populations of facts and therefore in fact models. It couldn’t be any other way. And I agree. The only problem is that in SBVR we want to talk directly about the real world. The Bottom Line So a concept model in SBVR is a ‘map’ of noun concepts and their relationships based largely on verb concepts.[4] Actually, it’s more than that. By ‘map’ I don’t mean either of the following on its own:

• A set of concepts and definitions loosely related (e.g., a glossary) – although definitions are clearly essential. 
• Some diagram(s) – although often quite useful.

Rather, I mean a non-redundant, integrated, anomaly-free structure of concepts based on interlocking definitions – a blueprint of meanings. How is an SBVR-style concept model different from (and better than) a traditional “conceptual data model” (or entity-relationship diagram)? Instead of mere lines to represent relationships between noun concepts, with an SBVR-style concept model we have verbs. These verbs reveal the intended meanings of the relationships. With verbs I can verbalize – literally communicate (and demonstrate) what is meant. No more hidden meaning!

A practitioner recently said this: “Even if we assume that a technical methodology might exist to generate a complete and correct data model from a set of articulated business rules / facts, in my opinion this approach just moves the target from the data modeling area to the need to verify the articulation of business facts / rules for completeness and correctness.” Exactly! And why would that be a problem?! Here’s an additional advantage: The core concepts (concept model or fact model) of an operational business area are very, very stable. Don’t believe it? As proof see http://www.brcommunity.com/b594.php (short case study, well worth a quick read). The problem with current data modeling practices is that a large gap often exists between the business view of things (operational business things) and the ‘data’ view of those things – even when trying to keep to a ‘conceptual’ view. This gap gives business-side people a ready excuse to drop out. But data modelers alone can’t provide the necessary business know-how for a robust, complete model. The problem is so big, it’s hard to see. Current data model practices evolved bottom-up, from database designs. (I believe I can speak with some authority here. I was editor of the Data Base Newsletter, 1977-1999, and wrote three books related to the matter in the late ‘70s and early ‘80s.) It’s time for us to approach the problem top-down. There is a natural way to build concept systems – it starts with basic business communication. The standard for such an approach already exists – SBVR. (For discussion see BRCommunity’s SBVR Insider section.) We’re already living and working in a knowledge economy … We need to start acting like it!

A practitioner recently commented: “Everyone has their biased view of what a data model is. Data modeling is art – not science. Give 6 data modelers one set of requirements and you’ll get 7 solutions all distinctively different.” My response: To me that’s a huge problem. No, ‘data’ modeling is not a science, but nor should it be an art. Actually, it should be engineering. Engineered solutions have to stand up to rigorous tests. But we lack that in ‘data’ modeling. Why? Because ‘data’ modeling is divorced from its initial business context, which is operational business communication, including business rules. You need nouns and verbs for that, and those nouns and verbs should stand for well-structured concepts. Give me a model of well-structured concepts that has been ‘proven’ by verbalizing business rules and other formal business communications and I guarantee I can come up with the best data model. I’m talking of course about concept models (sometimes called fact models).

As part of a continuing dialog, I recently asked these questions: What does the term “conceptual data model” really mean? Is it the best term for what is meant? To me, it sounds like “conceptual data model” might be about “conceptual data”. Surely not(?). What exactly then? (Some of my thoughts on the matter: http://goo.gl/8GX5o.) A practitioner responded with the following 3-part challenge below. With each challenge is my response. Challenge part 1: Which of the following, if any, would you see in a conceptual data model: primary keys, foreign keys, abstract keys, constraints, nulls, non-nulls, 1:1s, 1:Ms, optionality, cardinality, etc.? My response: Wrong approach. Bad definition practice one three counts. 1. Things should be defined in terms of their essence, what they are, not in terms of what are allowed or disallowed. 2. Suppose none of the above were allowed. You’d end up simply with a description of what the thing is not, not what it is. Since you can’t possibly list everything it’s not, the definition is woefully incomplete. 3. The list attempts to describe something in terms of an implementation or design of the thing, not in the thing’s own terms. That’s simply backwards. Challenge part 2: So let’s turn the question around. What would be the minimum types of objects you need to explore a concept, produce a mini model, and convey knowledge about the concept and its relationship to other concepts? My response: To develop a concept you need: 1. A concise definition for the concept, which distinguishes the concept from all other concepts and conveys its essence to the business. 2. A business-friendly signifier (term) for the concept that has only that one meaning (or is properly disambiguated by context). 3. A set of facts that indicates the place of the concept within a structure of other concepts (e.g., classification, categorization). 4. A set of verbs that permits the business to communicate unambiguously about how the concept relates to other concepts (e.g., customer places order). 5. A set of structural rules for the concept and its relations to other concepts, as needed. Why the verbs? Because you can’t write sentences without verbs and business rules can always be written in sentences. ‘Requirements’ should be too(!). Challenge part 3: When you are finished do you call that a ‘conceptual data model’? My response: You notice that I didn’t say anything, directly or indirectly, about “data”. That term is irrelevant. Taking away “data” leaves “conceptual model”. But that term suggests what the model is made of (concepts), not what it is about. Of course the kind of model we’re talking about is made up of concepts – it’s certainly not made of physical material (e.g., wood, plaster, etc.) or through use of math (some formula). That leaves “concept model” (also sometimes called “fact model”). Bingo. Is there a standard for this kind of model? Indeed there is: SBVR (Semantics of Business Vocabulary and Business Rules). See the SBVR Insider section on www.BRCommunity.com for more information.

Business Process Models: A completed transform often achieves a business milestone and a new state for some operational business thing(s). Example: claimant notified. Fact Models: In fact models (structured business vocabularies) such states are represented by fact types, for example, claimant is notified (or claimant has been notified if you prefer). A fact model literally represents what things the business can know (remember) about completed transforms and other operational business events. Business Rules: Business rules indicate which states are allowed or required. They should not reference business processes or business tasks by name, just the states they try to achieve. For example, a business rule might be: A claimant may be notified that a claim has been denied only if the specific reason(s) for denial have been determined.  ~~~~~~~~~~~~~~~~~~~~~~~~~~  This post excerpted from our new book (Oct, 2011) Building Business Solutions: Business Analysis with Business Rules. See:  http://www.brsolutions.com/b_building_business_solutions.php  

It almost goes without saying (but I’ll say it anyway) that you must know exactly what the words mean in all parts of your business requirements. In running a complex business (and what business isn’t complex these days?!), the meaning of the words can simply never be taken as a ‘given’. Some IT professionals believe that if they can model the behavior of a business capability (or more likely, some information system to support it), structural components of the know-how will somehow fall into place. That’s naïve and simply wrong. Business can no longer afford such thinking. A single, unified business vocabulary (fact model) is a prerequisite for creating a scalable, multi-use body of business rules – not to mention good business requirements. It’s what you need to express what you know precisely, consistently, and without ambiguity. Certainly no form of business rule expression or representation, including decision tables, is viable or complete if not based on one. And I pretty certain that’s true for most other forms of business communication about day-to-day business activity too. What am I missing something here?  ~~~~~~~~~~~~~~~~~~~~~~~~~~ This post excerpted from our new book (Oct, 2011) Building Business Solutions: Business Analysis with Business Rules. See:  http://www.brsolutions.com/b_building_business_solutions.php

One of the first rules of business analysis should be never waste business people’s time. One of the fastest ways to waste their time is not knowing what they are talking about … literally … and do nothing about it. So you end up just wasting their time over and over again. Unacceptable. Is there a way to avoid it? Yes, by taking the time to understand exactly what concepts the business people mean when they use the words they use.  I believe business vocabulary should be job one for Business Analysts. If you don’t know (and can’t agree about) what the concepts mean, then (excuse me here for being blunt) you simply don’t know what you’re talking about. (And sometimes, unfortunately, neither do the business people … which is something important BAs should find out as early as possible.) So structured business vocabularies (fact models) are a critical business analysis tool. How else is there to analyze and communicate about complex know-how in a process-independent way?! Looking at the issue the other way around, you can make yourself look really smart about a complex area in a relatively short time by having and following a blueprint. We’ve had that experience many, many times in a wide variety of industries and problem areas. (Try jumping between insurance, pharmaceuticals, electricity markets, eCommerce, race care equipment, credit card fraud, trucking, taxation, healthcare, banking, mortgages, pension administration, ship inspections, and more! We do.) There’s no magic to it – like contractors for the construction of buildings, you must have or create structural blueprints. For operational business know-how, that means bringing an architect’s view to structure the concept system of the problem space …  just a fancy way of saying develop a well-structured business vocabulary. Then a whole lot of things will fall right into place for you. P.S. By the way, I’m not talking about any form of data modeling here. Also, there’s no real need to use the ‘S’ word (semantics) for it.  

Difficulties in communicating within organizations are by no means limited to communications among business workers, Business Analysts, and IT professionals. In many organizations, business workers from different areas or departments often have trouble communicating, even with each other. The business workers seem to live in what we might call semantic silos (reinforced by legacy systems).  A well-managed, well-structured business vocabulary (fact model) should be a central fixture of business operations. We believe it should be as accessible and as interactive as (say) spellcheck in Microsoft Word. Accessible business vocabulary should be a basic element in your plan for rulebook management, requirements development, and managing know-how.  ~~~~~~~~~~~~~~~~~~~~~~~~~~  This post excerpted from our new book (Oct, 2011) Building Business Solutions: Business Analysis with Business Rules. See:  http://www.brsolutions.com/b_building_business_solutions.php    

Is there any proven way to demonstrate data models are correct, complete, and stable with respect to the operational business and its needs? No. That’s distressing.  Is there an alternative that does? Yes, fact modeling, which is to say structured business vocabularies (concept systems). The core concepts (fact model) of an operational business area are very, very stable. I have outstanding proof (short case study).  See: http://www.brcommunity.com/b594.php. Definitely worth a quick read. I recently made these statements in a data modeling forum, and a practitioner came back with this: “Even if we assume that a technical methodology might exist to generate a complete and correct data model from a set of articulated business rules / facts, IMO this approach just moves the target from the data modeling area to the need to verify the articulation of business facts / rules for completeness and correctness.” Missed the point. Concept anaysis is brain work. You’ll never generate a ‘complete and correct data model’ … you must create it … ith business people and SMEs. The problem with data modeling as practiced today is that there is a large gap between the business view of things and the data model. It gives business-side people a ready excuse to drop out. And its an art rather than a science. You really have no justification building a system until you have a concept blueprint. Currrent data models evolved bottom up, from database design. (I know, I watched it happen. I was editor of the Data Base Newsletter, 1977-1999.) It’s time to approach the problem top-down. There are natural ways to build concept systems. The standard for the new approach is SBVR. (For more, see BRCommunity’s “SBVR Insider”), which in turn is based on ISO 1087 and 704.  We (all of us) need to start practicing like we’re living in a knowledge economy … which in fact we actually already are.