LINQ allows sophisticated modelling of the value created through the reuse of information. The start of this process is for the LINQ user to assign a value to each information output – the point in an ISC where business value is supported. The value scale ranges from 0 (no value) to 10 (high value). Although this may seem arbitrary, it provides a consistent and normalised measure of value across the organisation and beyond, whatever the focus for value may be.
LINQ cascades this value ‘upstream’ against the information flow direction and then sums the values where reuse occurs. This can be seen below where the overlap area is scored at a combined 15 (5 + 10). LINQ groups all nodes of like pedigree so all nodes within the overlap area have this value of 15. What does this mean? For the first time, organisations can see how valuable a piece of information is, based empirically on how much it is used in the organisation.
LINQ automatically groups nodes of like value. These LINQsets provide a powerful visual cue to show where value is concentrated in any scale of enterprise. Above, these LINQsets are shown minimised (for example – ‘Things which Support both’ is a minimised LINQset containing nodes of value 12).
When considering how to value outputs, there are 3 approaches that can work;
1. Consider the relative importance of the outputs you are modelling. Output A is more important that Output B which is more important than Output C. Output A would then be valued as 10, Output B as 7 and Output C as 4. This approach works well when you are considering the Information Flows for a specific area of the business or a project. As value can be adjusted at any time, as more Outputs are considered, or more information regarding value becomes available, you can adapt this approach as necessary through the capture process.
2. Use a methodology such as MoSCoW to assign initial value scores. M (Must Have) = 10, S (Should Have) = 7, C (Could Have) = 5 and W (Won't Have/Would Like to Have/Isn't Needed) = 2. You can decide on the scores for the scale as long as you are consistent across all Outputs.
3. Use a weighted value approach which considers many influences of value to determine a more objective value score: