Let’s put you on the hot spot. You are forced to agree or disagree with the following statement, and defend your answer. What would you say?
Deep subject matter expertise is irrelevant for a business analyst, especially in agile business analysis.
Here’s how I answer: I disagree. How about you?
My reasoning: Actually I agree that deep subject matter expertise is generally irrelevant for a business analyst. Where I disagree (strongly) is that it’s especially true for agile business analysis.
Agile business analysis is no silver bullet. It doesn’t imbue you with magical powers to learn faster. Failing faster to learn faster is simply churn. There’s no end to it.
We’re missing the big picture. Exploring ‘deep subject matter expertise’ you’re not familiar with is a matter of having the right architectural tools to probe knowledge. It’s that deep operational business knowledge that makes subject areas hard.
So you simply need the right architectural techniques to map the knowledge of a domain explicitly. You need to be able to ask probing questions about the domain intelligently without wasting SMEs’ time.
The architectural techniques you need are true business rules and concept modeling (structured business vocabulary). By the way, business rules and concept models can be made to work equally well for both agile and waterfall – and anything in between.
Mark Your Calendar: The annual Building Business Capability (BBC) conference is November 6-10, 2017 at the Loews Royal Pacific Resort, Orlando, FL. The BBC is the place to be for professional excellence!
John Zachman says you can (and probably should) develop each of the following three kinds of artifacts to “excruciating level of detail”.
1. For the business management’s perspective (row 2), a conceptual model (roughly CIM in OMG terms).
2. For the architect’s perspective (row 3), a business logic design (roughly PIM in OMG terms).
3. For the engineer’s perspective (row 4), a class-of-platform design (roughly PSM in OMG terms).
Because each is a different kind of model, there is a transform from one to the next. One implication is that it is possible to make a clear distinction between analysis (CIM) and design (PIM). Another implication is that concept models and logical data models are clearly distinct. Unfortunately, many people blur the line between them. That’s wrong.
A concept model is about the meaning of the words you use, and the business statements you make assuming those meanings. It’s about communication.
A logical data model is about how you organize what you think you know about the world so it can be recorded and logically manipulated in a systematic way.
I started my career in data. It took me as much as 15 years of intense work on business rule statements (1990-2005) to fully appreciate the difference. But now I am very clear that concept models do need to be developed to excruciating level of detail in order to disambiguate the intended business communication. Most businesses don’t do that today. They jump in at data design (conceptual, logical or even physical). And they unknowingly pay a big price for it. By the way, a good concept model can be readily transformed into a first-cut logical data model – just as Zachman says.~~~~~~~~~~~~~~~www.BRSolutions.com
Guest Post by Markus Schacher
We should first agree on the semantics of underlying concepts and only then start to think about the best terms for those concepts.
One particular technique I often apply in such cases is the following:
1. Name controversial concepts with proxy names such as “Greg”, “Mike” or “John” (or whatever name you prefer) to get potentially misleading names and their implicit connotations out of the way of progress.
2. Draw a concept diagram showing those concepts as well as important semantic relationships among them.
3. Formulate intensional definitions for each concept – still using the proxy names. Ensure that those definitions are consistent with the relationships shown on the concept diagram.
4. Identify one or more communities that “baptize” those concepts by giving them better names.
If synonyms and/or homonyms appear among those communities, that’s just how the world is; we simply have to live with it. This is why SBVR formally supports semantic communities as well as speech communities.
To understand the future of processes, you must dig a little deeper than many people do.
Process thinking goes back well over a 100 years, to the origin of modern iron and automobile production. The raw materials and finished goods of such manufacturing and production processes are literally spatial – 3-dimensional. What can you do to significantly improve productivity in a 3-dimensional world? The answer these days is simple: You build robots. Robotization has literally changed the world during the past 30-40 years.
Rather than manufacturing and production processes, however, the world is now increasingly focused on white-collar and digital processes. What 3-dimensional presence do the raw materials and finished goods of these processes have?
Well, exactly none. The raw materials and finished goods of these processes aren’t physical and simply have no spatial presence whatsoever (except maybe for paper artifacts). Robots (at least physical ones) aren’t an option. That fact of life makes a huge difference in how you have to think about automation for such processes.
Instead, the raw materials and finished goods of such processes are all about your operational business knowledge – your intellectual capital – and your capacity to express and apply it. That capability, in turn, depends directly on your business terminology and business language. For white-collar processes you have no choice – the world is semantic. So you must deal with the subject matter semantically.
That takes us in a very different direction than most professionals currently foresee. For one thing it takes us toward natural language and away from diagrams-for-everything. That’s a huge shift in mindset. Imagine having a business conversation with your smart phone about gaps and ambiguities in business policies and in the meanings of the vocabulary you use to talk about subject matter knowledge. Don’t think that’s possible? Have you watched your kids talking to their smart phones lately?
Sooner or later businesses will realize that operational business knowledge differentiates their product/services and enables their ever-more-automated processes to function. Capturing, managing and re-using that intellectual capital puts a premium on structured business vocabulary (concept models) and on business rules expressed in structured natural language. Those business rules are the only way you have to ensure quality from white-collar and digital processes.
Read more on this topic:
Are Processes and BPM Relevant in the Digital Economy? http://www.brsolutions.com/2015/10/19/are-processes-and-bpm-relevant-in-the-digital-economy/
Measuring Quality and Defects in the Knowledge Economy: http://www.brsolutions.com/2015/10/27/measuring-quality-and-defects-in-the-knowledge-economy/
Quality and Tolerances in the Knowledge Economy: http://www.brsolutions.com/2015/10/29/quality-and-tolerances-in-the-knowledge-economy/
Building Business Solutions: Business Analysis with Business Rules (2nd Edition) … Just Out! http://www.brsolutions.com/b_building_business_solutions.php
Get it on Amazon: http://goo.gl/HXxN1fWhat It’s About: How to develop business solutions working directly with business leads, create blueprints of the solutions, and then use those blueprints for developing system requirements. Engineering business solutions, not just requirements.We have applied the techniques described in this book successfully in hundreds of companies worldwide.
Kind Words from a Practitioner: “We have based our whole business rules analysis practice on the methodology and techniques developed by the Business Rules Solution team. This book is an integral part of our practice. It’s an easy to read, useful guide with real life examples – we use it daily and couldn’t do without it!” – Michelle Murray, Inland Revenue Department NZ
New in this Edition: How Business Architecture corresponds with your projects and requirements work. Developing a Concept Model and how it will help you. How business rules align with the new terminology in the recently released IIBA® BABOK® Guide version 3.
OMG released version 1.3 of SBVR (Semantics of Business Vocabulary and Business Rules) last month – comprehensively reorganized for approachability, but not changed. Some thoughts …
Ever hear the conversation that three baseball umpires once had? If you don’t live in a baseball country, it’s an archetypical story, so you’ve probably heard some variant. By the way, in American English a ‘pitch’ is a throw of the ball for the batter to try to hit, not the field of play. Meanings matter!
The first umpire says, “Some pitches are balls and some are strikes. I call them as they are.”
The second umpire says, “Some pitches are balls and some are strikes. I call them as I see them.”
The third umpire says, “Some pitches are balls and some are strikes. But they aren’t nothing till I call them.”
Most modeling techniques primarily focus on modeling the real world as it ‘really is’. They essentially take a first-umpire point of view or maybe second-umpire. I come from that tradition too. My 1987 book Entity Modeling was about doing that … modeling the real world as it ‘really is’. Pretty much all professionals with some IT background come from that world.
Working with business people and business rules for the last 25 years, however, has taught me that business is really more of a third-umpire world. Think of laws, regulations, statutes, contracts, agreements, terms & conditions, policies, deals, bids, deeds of sale, warranties, prospectuses, citations, complaints, receipts, notices … and business policies.
Even businesses that deal with tangible stuff (e.g., railroads, electrical transmission, infectious diseases, etc.) live in a third-umpire world. And many of the most automated organizations around have no tangible product at all (e.g., finance, insurance, government, etc.). They really exist only in a world of words (between people).
It’s humbling to realize that the way the business world ‘really is’ is more directly the product of words exchanged by the players in a conversation game than anything IT professionals can model directly. But why would it be otherwise? Do IT professionals really know better than business owners, business managers, lawyers, engineers, subject matter experts, etc.? Really?!
SBVR, in contrast to almost all other standards, doesn’t try to model the way the real world ‘really is’. Instead, its focus is on modeling what is said about the way the world really is. It’s fundamentally a third-umpire standard. You simply have to understand what the words mean – and that’s a human-communication issue.
Yes, the SBVR world view is a game changer. It also happens to align closer with some of the most exciting new work in computerization today including cognitive computing and machine learning.
I stand accused by peers in the standardization community of wanting to go beyond the ‘capture, exchange and production of information’. Sure, I can live with that.
Professionals should always focus on business solutions first, then and only then on designing systems. Not just lip service, I mean applying the power techniques of true business architecture. The first two of these techniques are:
The third technique is structured business vocabulary – a concept model.
The value-add companies produce today is based on rich operational business knowledge. No business solution can prove truly effective if business people (and the tools they use) are unable to communicate about that knowledge clearly. Who profits from operating in a Tower of Babel?
A concept model is about identifying the correct choice of terms to use in business communications (including statements of business rules) especially where subtle distinctions need to be made. A concept model starts with a glossary of business terms and definitions. It puts a premium on high-quality, design-independent definitions, free of data or implementation biases. It also gives structure to business vocabulary.
Essential for any true architecture is stability over time. Are the core concepts of an operational business stable over time? Yes. Did you know that?!
When you show business people class diagrams or data models, you’re not really talking business. Class diagrams and data models are design artifacts that inevitably focus on how knowledge about real-world things is to be represented (and manipulated) as data in machines. If we want business people to talk business to us, we must talk business back. That means talking about their words, concepts and meanings. To do that you need to develop business vocabulary in a structured way – i.e., develop a concept model.Practitioners are slowly starting to ‘get’ this. Examples:
IIBA’s recently released Business Analysis Body of Knowledge (BABOK) version 3 devotes a new section to concept models.
A solid standard exists for concept models – OMG’s Semantics of Business Vocabulary and Business Rules (SBVR). Version 1.3 of the standard, comprehensively reorganized – but not changed – is due out this week.
Do people in your company always mean the same thing when they use the same terms? Almost certainly not, right?! So ask yourself, how good are your business communications and requirements likely to be if people don’t mean the same things by the terms they use? And how good is your automation likely to be?Gurus talk about application or functional silos in organizations. I believe the problem is even more basic than that – organizations today essentially have semantic silos. Look under the covers of any broken process or poor set of requirements and you inevitably find poor communication practices. These days you don’t have the time not to define, structure and manage your business vocabulary. These days a concept model is no luxury. ~~~~~~~~~~~~~~~~~~~~~~~~~www.BRSolutions.com
A concept model organizes the business vocabulary needed to communicate consistently and thoroughly about the know-how of a problem domain.A concept model starts with a glossary of business terms and definitions. It puts a very high premium on high-quality, design-independent definitions, free of data or implementation biases. It also emphasizes rich vocabulary.A concept model is always about identifying the correct choice of terms to use in communications, including statements of business rules and requirements, especially where high precision and subtle distinctions need to be made. The core concepts of a business problem domain are typically quite stable over time.Concept models are can be especially effective where:
The organization seeks to organize, retain, build on, manage, and communicate core knowledge or know-how.
The project or initiative needs to capture 100s or 1,000s of business rules.
There significant push-back from business stakeholders about the perceived technical nature of data models, class diagrams, or data element nomenclature and definition.
Outside-the-box solutions are sought when reengineering business processes or other aspects of business capability.
The organization faces regulatory or compliance challenges.
Definition of Concept Model
a model that develops the meaning of core concepts for a problem domain, defines their collective structure, and specifies the appropriate vocabulary needed to communicate about it consistently
The standard for concept models is the OMG standard Semantics of Business Vocabulary and Business Rules (SBVR).Concept Model vs. Data ModelA concept model differs from a data model in important ways. The goal of a concept model is to support the expression of natural-language statements, and supply their semantics – not unify, codify (and sometimes simplify) data. Therefore the vocabulary included in a concept model is far richer, as suits knowledge-intensive problem domains. In short, concept models are concept-centric; data models are thing-entity-or-class-centric. Data models can usually be rather easily derived from concept models; the reverse is much harder (or impossible). Like data models, concept models are often rendered graphically, but free of such distractions to business stakeholders as cardinalities. The Components of Concept ModelsNoun Concepts. The most basic concepts in a concept model are the noun concepts of the problem domain, which are simply ‘givens’ for the problem space.
For BACCM these basic noun concepts are: need, stakeholder, value, change, context, and solution.
In finance, basic noun concepts might include financial institution,real-estate property, party, mortgage application, lien, asset, loan, etc.
Verb Concepts. Verb concepts provide basic structural connections between noun concepts. These verb concepts are given standard wordings, so they can be referenced unambiguously.
In BACCM some basic wordings for verb concepts include: Value is measured relative to Context, Change is made to implement Solution, Stakeholder has Need.
In a financial business, some basic wordings for verb concepts include: Lien is held against Real Estate Property, Party requests Loan, Asset is included in Mortgage Application.
Note that these wordings are not sentences per se; they are the building blocks of sentences (such as business rule statements). Sometimes verb concepts are derived, inferred or computed by definitional rules. This is how new knowledge or information is built up from more basic facts.Other Connections. Since concept models must support rich meaning (semantics), other types of standard connections are used besides verb concepts. These include but are not limited to:
Categorizations – e.g., Person and Organization are two categories of Party.
Classifications – e.g., ‘Toronto Dominion Bank’ is an instance of Financial Institution.
Partitive (Whole-Part) Connections – e.g., Dwelling and Land are two Parts of a Real Estate Property.
Roles – e.g., Applicant is the role that Party plays in the verb concept Party requests Loan.
StrengthsA concept model:
Provides a business-friendly way to communicate with stakeholders about precise meanings and subtle distinctions.
Is independent of data design biases and the often limited business vocabulary coverage of data models.
Proves highly useful for white-collar, knowledge-rich, decision-laden business processes.
Helps ensure that large numbers of business rules and complex decision tables are free of ambiguity and fit together cohesively.
LimitationsA concept model:
May set expectations too high about how much integration based on business semantics can be achieved on relatively short notice.
Requires a specialized skill set based on the ability to think abstractly and non-procedurally about know-how and knowledge.
Involves a knowledge-and-rule focus that may be foreign to stakeholders.
Requires tooling to actively support real-time use of standard business terminology in writing business rules, requirements, and other forms on business communication.
For more information about Concept Models, refer to Business Rule Concepts 4th ed, by Ronald G. Ross, 2013, Part 2.www.BRSolutions.com
 This discussion is consistent with that standard, but explains concept models from a business point of view. See the SBVR Insider section of www.BRCommunity.com for more information about SBVR.
 The first set of examples in each of the following two subsections is from the Business Analysis Core Concepts Model (BACCM), a part of the IIBA’s Business Analysis Body of Knowledge (BABOK), version 3.
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