Try it! is THE DECISION CLOUD. It offers a platform for development of Decision, Business Intelligence, Analytics, Search and Inference.

Decision Support

Decision Support is the art of selecting the best solution from a set of choices. While traditional business intelligence tools emphasize data retrieval and summary, they overlook a critical element of the decision process: tradeoffs between criteria. Auguri focuses on the decision process itself, offering an ideal tradeoff-based platform to optimize decisions. Auguri’s enterprise class solution spans a wide range of decisions, from simple to complex, from stand-alone to collaborative. Auguri’s users can quickly, confidently and successfully optimize their decisions by trading off decision criteria. By optimizing the decision, shortening the decision making process and reducing its cost, Auguri delivers a compelling ROI.Tradeoffs Paramount for Decisions

A decision is the selection of the optimal alternative(s) from a set of choices. The decision is typically made by trading off -or weighing the relative importance of- a set of factors or criteria. What makes tradeoffs necessary is that usually these factors are conflicting. For example, a consultant that often travels to deliver presentations is interested in a light weight laptop with a large screen. However, the criteria are conflicting; the larger the screen the heavier the laptop. Hence there is a need to tradeoff between conflicting criteria.
The objective of a decision, a selection, a prioritization, or a triage is to select the best solution(s) from a set of alternatives. To be able to make a selection, the first component required is a set of data (the choices). This is the database of alternatives which will be ranked according to their weighted proximity to the ideal.
In order to make a decision we typically use a set of criteria.

These criteria are encapsulated by their Criteria behavior (shown on the left side of Figure 1) and typically expressed by a unique function that can be thought of as a utility function, or the way we think about this specific criterion. For example a screen size is a criteria when choosing a laptop, and typically the behavior of this criterion is “the larger the better”. It is important to note that these criteria are often if not always conflicting. In the same laptop selection weight is another criterion. We typically seek lighter laptops. However, the weight of the laptop increases with the size of the screen. Hence we have a conflict between these criteria. Humans have only one way to mitigate these conflicts: TRADEOFFS – a technique our brains have mastered and computers have not been able to handle until the advent of Auguri-.

Collaborative Decisions

The purpose of collaborative decision software is to bring the experts in various areas of the business together into a decision that impacts each area of the operation. Because Auguri separates the various factors affecting a decision the system is ideally suited to facilitate the intervention of experts and others in the decision process.
This capability is designed to enable various members of the organization to leverage expert know-how, yet be able to customize the decision to their specific situation by modifying one of several components of the decision process: the ideal, the criteria behavior, or most often the relative importance of the criteria.

Participating in the process involves (i) Asking the experts for their input in each area (or criterion) that impacts the decision (contribution factor / domain ordering) (ii) Allowing various players to input the importance of the criterion that impacts them and (iii) Allowing a higher level authority that has at stake the overall interest of the project/company to override the weights of the various criteria
Sharing the experts’ know-how, raises the level of competence of the whole organization. Now novices can perform like experts. In addition, by capturing the experts’ knowledge in the system, the organization keeps this asset as the experts depart the organization achieving major savings.

Understanding and Justifying Decisions

Decisions rarely follow the same thought process. Sometimes they are made through a rigorous selection process. Occasionally they are made by reference i.e.” I like the car that Bob drives, I want something similar”. Often they are made intuitively. Auguri’s inference technology derives the selection criteria (and their importance) from the results or the selection. With its inference engine Auguri offers an ideal way to understand, rationalize or justify a decision.

Sharing Decisional Intelligence Across the Organization

Auguri’s tradeoff based metaphor makes it possible to rationalize, collect, and store insight into user and organizational profiles, preferences, and decision processes, which is not possible with traditional (SQL-based) tools. Auguri savvy applications can now share this knowledge throughout the organization. This raises every member’s competence to that of an expert. Furthermore, since knowledge is no longer kept with individuals, it is not lost when they leave. In essence, Auguri-based applications inaugurate the era of intelligence interchange. Another aspect of the intelligence interchange is the ability to capture customer behavior and preference. For example, if you were able to capture the customer preferences and tradeoffs, and download them from the brain of each salesperson to a centralized data-warehouse you would have tremendous intelligence on your customer needs.