One of my primary goals is for business processes and data to be both human understandable and machine understandable.  For example, a person could define a business process in manner that the person would understand and modify as the business changes.  Then, by taking this process and making it machine understandable would mean that a computer can reason, infer, and even execute the process.  For the financial industry, this would provide Straight-through processing (STP), enabling the entire trade process for capital markets and payment transactions to be conducted electronically without manual intervention.   For insurance processes, this can be employed for automated case management.

Similarly with data, the business user can define and acesss a clear, unambiguous understanding of the enterprise's data.  When terminology differs between two parties, a mechanism must exist to translate and transform the data – so that exchange is meaningful.  This is particularly prevalent in the health insurance and financial industries.  By enabling the data to be machine understandable, areas such as terminology, metadata (and even formal approaches such as ontology) can be automated and used for reasoning, inference, and the guidance of autimated processing in realtime.  Hospitals can exchange health information in a way that would not be possible without machine-understandable data.  Financial institutions benefit in a similar manner in a global context.

Machine understandability of data also enables reference/master data (MDM) to be managed and its integrity guarded.  Complicated master data can also be re-engineered by using a machine-understandable approach.

Complex event processing (CEP) is enabled when the process, data, and rules are machine understandable.   Using this approach, billions of global financial transactions can be analyzed and appropriate action taken in a timely manner – because its processing employs small busines-defined and machine-understandable processors, instead of being constrained to the bottleneck of a mammoth centralized processing.

When more and more of the business’ process, data, and rules become machine understandable, the automation can become adaptable.  In other words, it can learn and evolve – and change how it executes and the business works.

Businesses that employ both a human-understandable and machine-understandable approach with be ready for the next generation of global systems.