Operational business intelligence (BI) has a focus on day-to-day operations and so requires low-latency or real-time data to be integrated with historical data. It also requires BI systems that are integrated with operational business processes. However, while operational BI might be part
and parcel of operational processes and systems, the focus is still on changing how people make decisions in an operational context. To compete on decisions, however, you must recognize that your customers react to the choices made by you, your staff and your systems, and that you must manage all the decisions you (or your systems) make – even the
very small ones. This is the basis for enterprise decision management or EDM. Five main areas of difference exist between operational BI and EDM – a focus on decisions (especially operational
ones), organizational integration, analytic technology change, adoption of additional technology and adaptive control.
In this article, I want to outline some steps organizations can take as they move from “traditional” BI towards operational BI and enterprise decision management. Some of these steps would be a good idea if operational BI was your goal. But hopefully you are more ambitious than that and want to really begin to compete on decisions.
Unlike traditional BI, an operational BI system should be focused on influencing the interaction with your customer to provide benefit to both the customer and your business. Traditional BI, while often seen as a tool with a very fuzzy ROI, is nonetheless necessary for conducting business. Operational BI, on the other hand, provides a much clearer benefit because it directly addresses your business.
It is from this operational asymmetry that complexity in event processing is required. In other words, as distributed networks grow in complexity, it is difficult to determine causal dependence when trying to diagnosis a distributed networked system. Most who work in a large distributed network ecosystem (cyberspace) understand this. The CEP notion of “the event cloud” was an attempt to express this complexity and uncertainly (in cyberspace).
The power of Decision Management in this kind of scenario is threefold.
Firstly it focuses on the decisions themselves - what decisions matter to the customer interaction. This ensures that the data being collected and used is that which will make a difference. Beginning with the decision in mind in this way focuses analytics and data gathering.
Secondly it allows the decision to be made consistently across channels so that customers get the same service from the agent at the gate, the call center, the service center or the kiosk. Operational BI assumes there is a person to make the decision and so cannot deliver this true cross-channel consistency.
Thirdly, Decision Management recognizes that policies and regulations matter as much, sometimes more, than data. Presenting the data and even its analysis to someone who then fails to follow procedure is not helpful. Decision Management combines the policy aspects of a decision with the analytic aspects in a way Operational BI does not.
business processes and business rules capturing the operational logic and decisioning logic respectively.
To study this analysis, we first need to understand theory which is the basis of their analysis i.e. BWW. Representational analysis is basically comparing constructs of representation theory with the constructs of the modeling grammar. The two evaluation criteria used are ontological completeness which determines the extent of lack of constructs in modeling grammar and ontological clarity. Now BWW is the representational theory to represent real world and has been earlier used to benchmark many languages. SRML and SBVR are compared to BWW to benchmark their representational power.
I believe that the general consensus among those who study this kind of thing, is that any decision made wholly by a computer is an operational decision, even if it affects the behavior/tasks of many people or sub-components. Online decisions, being a subset of automated decisions, would then be operational in nature.
Business intelligence has “invaded” the operational space in a big way, offering in-line analytics, real-time or near real-time decision-making support for all employees in the enterprise.
A key component of a company's IT framework is a business intelligence (BI) system. Traditional BI systems were designed for senior management and business analysts to report on, analyze and optimize business operations to reduce costs and increase revenues. Organizations use BI for strategic and tactical decision making where the decision-making cycle may span a time period of several weeks or months. Competitive pressures coming from a very dynamic business environment are forcing companies to react faster to changing business conditions and customer requirements. As a result, there is now a need to use BI to help drive and optimize business operations on a daily basis, and, in some cases, even for intraday decision making. This type of BI is called operational business intelligence and real-time business intelligence and it is used not only by senior management and analysts (as in traditional BI) but also by line of business managers and operational users. In other words this is BI for everyone.
This article discusses why business intelligence is often too closely associated with data warehousing and should be replaced by a concept such as decision intelligence, which could be considered a modern version of earlier decision support systems.
Embedded operational analytics help applications and business users take close to real-time action. However, there is another class of applications where even close to real-time analytics are not
sufficient.