The key factor in considering how rule platforms could better support business governance is to remember how business governance is formally communicated. It's expressed in natural language in governance documents, starting with policies and directives.

On top of that is an array of legal documents, almost all pure text as well, including:

Alongside are authoritative sources of science and engineering knowledge, documents which are also always extensively framed in text.

Like it or not, policy makers and knowledge workers by and large will never learn a diagrammatic or tabular way to represent or revise the rules already expressed in these artifacts. Why should they?!

Business governance needs a path to automating such rules that features interpretation and disambiguation of natural language and definitions. Translation into decision tables (which is seldom even possible for governance artifacts) or into some other foreign form (such as a programming language) is not an effective solution. A rule platform that doesn't support natural language at least in some controlled or structured form will always find itself at odds with core needs and practices of business governance.

Lack of such support leaves a huge gap right at the heart of organizations. And it's not just a technological gap, it's also a traceability gap. Why?

Without support for some form of natural language, automating governance sources requires translation, not just interpretation. Under the very best of circumstances, this translation is into specific parameter settings for some automated tool(s). Far more common is translation into procedural coding languages — a.k.a. programming.

Impact for Business Governance

It's hard to believe this is still a fact of life in the 21st century. Yet by and large it is. Figure 1 illustrates the resulting gap from a governance perspective.

Figure 1. The Business Governance Gap

What impacts does this have for the day-to-day needs and practices of business governance?

• Loss of traceability. Top-down, it's hard to determine what pieces of code implement which piece of governance so as to prove that obligations have been met correctly. Just ask compliance people. They're often the ones who feel the pain most acutely.

Loss of traceability, however, works both ways. Bottom-up, it's hard to determine the immediate business motivation for crucial pieces of code so as to understand what results should be considered authentic.

So, impact assessment is difficult and time-consuming in both directions. Nobody enjoys it or feels very productive doing it.

• Diminished business agility. Change is far harder than it needs to be. It's not possible simply to re-state and re-interpret relevant governing artifacts through to implementation, watching for conflicts, gaps, and inconsistency — including, of course, inter-artifact clashes. That's hard enough on its own. Instead, change almost always requires programmer intervention.

That software work, in turn, is not simply a matter of translating modifications into computer code. First, developers must do fine-grained detective work to determine what code needs to be revised or replaced where, work that is often very time-consuming and error-prone in its own regard. It's not a happy overall mix whatsoever!

• Poor corporate memory. In an ever-more-fluid digital workplace, who can possibly remember that some new bit of code implemented some revised piece of governance in a particular way for a given reason?!

This is hardly a productive way to conduct Knowledge-Age work in a digital environment. Why is it still happening? With regard to rule platforms, there are two fundamental reasons:

1. Most rule platforms to date have assiduously avoided natural language in any form, as well as its natural cohort, business semantics.


2. The industry seems to have a fixation on perfect disambiguation (impossible), rather than on disambiguation beyond a reasonable doubt in a well-defined context.

It would be hugely productive to reach a middle ground where humans contribute glossaries and concept models (structured business vocabularies), and machines exploit those specifications to assist with automated disambiguation. Yes, it's a hard problem but I believe by no means impossible.

In a day and age when you can google almost anything, it's amazing to me that it hasn't already happened. We simply need to envision a new partnership between humans and machines, one more directly suitable to business governance.

A New Vision for Business Governance

That brings me to a new vision for business governance in the Knowledge Age. First, let me define what I mean by business governance. The definition we've used for well over a decade is:

a process, organizational function, set of techniques, and systematic approach for creating and deploying policy and rules into day-to-day business operations

What criteria should be used to assess how well 'creating and deploying policy and rules into day-to-day business operations' is accomplished? Such creation and deployment should be:

• timely
• effective
• selective
• pervasive
• traceable
• repeatable
• retractable

We also want the activity to be transparent (to those authorized by position or statute), and the ability to hold accountable those parties responsible for specific actions.

Current approaches to automating governing artifacts are neither smart nor agile! Addressing this challenge is by far the most important business opportunity remaining for rule platforms.

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