In my book, Information-Driven Business, I introduce a four layer model for information. You can also read more about this model in the MIKE2.0 article: Four Layers of Information.
The four layer model provides a way of describing information in every organisation. The model explains how information is consumed (layer 1: metrics), navigated (layer 2: dimensional), held (layer 3: atomic) and created (layer 4: operational). Using this model helps to organisation to understand where it is overly dependent on staff or customer knowledge to manage information at any of these layers (such as summarising to report, or slicing-up in spreadsheets to answer questions).
Some people have commented that the descriptions I use in the book, and are used in the MIKE2.0 article, are geared towards structured data. To help readers understand how the model equally applies to both structured and unstructured data, the following definitions of each layer may help
Layer 1: Metrics
For information to be used for management decision making, it ultimately needs to be summarised into a score or metrics against which “good” or “bad” can be defined. This is the same regardless of whether we are talking about structured data or summarising a collection of unstructured content. The metric for documents could be as simple as a count (for example, the number of policies) or a combination of factors such as the number of processes covered by a particular type of policy.
Layer 2: Dimensional or Navigational
While formally described as the dimensional layer, it is perhaps better described as the way that the organisation can be navigated. At this layer we are talking about structuring the content in way that we can find in a systematic way (via a taxonomy). It is from here that metrics, such as a count of policies, can be derived. It is also from here that we go to find content in its general form (“get me all procedures associated with disaster recovery”). For instance, in this layer policies can be cross referenced against each other.
Layer 3: Normalised or Atomic
In the unstructured sense it is better to use the term “atomic” for this layer which contains the content in its original form reference by the event that created it rather than a business taxonomy. This layer is often handled badly in organisations but can be as simple as recording the time, author and organisational hierarchy. It can also be aligned to business processes. For instance, in this layer, policies and procedures should be fully formed but only associated with the scope that they are covering.
Layer 4: Operational
The fourth layer is the front line and refers to the situation and technology context in which the content is created or updated. Examples include: social media, documents on network drives and email within the inbox of the conversation participants. For instance, in this layer, policies are created (maybe in many parts) but have no context.
Information-Driven Business 
Good explaination Rob. A good example of such applicaiton would be sentiment analysis of social media contents which are largely unstructured. At the high level, there are Positive or Negative sentiment (Metric), A number of taxonomy can be built to organise the contents such product lines or business lines (Dimensional), There are clear atomic layer contents such as Tweats or status updates (Atomic). Finally, Such applications can be used by front line for real time interaction both in terms of new content creation or updating.
Hi Felix, thank-you for your comment and I agree with you that sentiment analysis is a very good example and one that is poorly understood by many organizations. While many are looking for a one-off score of the perception of their company or product, this is both shallow and ultimately useless as there is no effective way of making comparisons over time. Moving to a taxonomy at the navigation (second) layer allows different measures of sentiment to be compared over time, including internal attitudes of the staff who are responsible of taking the products or services to market. Understanding the atomic form (third layer) of internal and external social media allows businesses to go further and start designing new products and services that will dramatically change perceptions of the company.
Your example for Metrics (count of documents) is structured content. A count of documents is structured data detailing how many unstructured documents there are hence it is a structured and not unstructured example.
Under navigation, a count of policies for e.g. is also structured data. If we use student load (number of students enrolled) as typical structured data reporting from a warehouse as an example this illustrates that a count of things is unstructured data.
To use your triangle (which I love BTW) unstructured metrics are thoughts or ideas (e.g. goals) and navigation could relate to a set of english sentences that describe the objectives of a company hence you can navigate through the underlying goals of an organisation by reading their set objectives.
working your way further down the triangle it can be used for unstructured content but with very different content in each level – goals, objectives, policies and standards and finally blog, email,video,PDF, etc etc. IMHO these are unstructured examples of how to use the triangle.
cheers,
james
Hi James, I really appreciate your comments – I love the challenge! I agree with you that the examples of the metrics are in and of themselves structured. While I also agree with lifting “thoughts and ideas” to this level, I believe that metrics are largely scores and hence numeric or structured – even when applied to content that is in itself unstructured. Similarly, a taxonomy is a form of metadata that is generally rule-based and structured. Both, however, are applied to unstructured content. Many organizations would be infinitely better able to use their information assets if they took the time to prioritize through scores and navigate through structured taxonomies.
errata
my bad
“Under navigation, a count of policies for e.g. is also structured data. If we use student load (number of students enrolled) as typical structured data reporting from a warehouse as an example this illustrates that a count of things is structured data.”