Binding Information Layers to Business Strategy

Slicing information into layers is of course a simplification of real world situation using brutal force. However, if we don’t use conceptualization – we cannot communicate to each other – humans communicate with symbols. Each layer presented below probably manages to capture the essential and differentiating characteristics of each layer. First time I read about IM Layers in some Forrester’s study. They had defined an IA reference model containing: Conceptual, Logical and Physical layer. I have just renamed bottom one as Implementation, due to many people complained about the word “physical”.

The major enhancement I made by adding Contextual layer on top. Even though it sounds like hyper abstract on top of already abstract cake – I believe it deserves its place. Should we consider IM and IA  from strategic performance management (SPM) viewpoint, it becomes imperative to bind Information Big Picture to business goals and direction.

Some years ago – even before I added the top layer, I thought, smart heads have already developed concepts for SPM – how could I utilize that fine work as aligning attributes for Knowledge Maps (or any other conceptual representation of information)? Then I ran into the Strategy Maps (Kaplan&Norton) and did few prototypes. It looks pretty promising to loosely bind Knowledge Map to Business.

• Strategy Map provides a framework through which we can reflect strategy and different viewpoints to given big picture of information. Viewpoints are for example from Balance Score Card perspectives: customer, financial, learning & growth, internal business process. Views could also be your own special interests, say knowledge management or Time To Market as part your business transformation initiative.
• Knowledge Map is an intelligent big picture of your core business information. What data is needed in your daily business or in the future model are described: Business Objects are mapped to Information Entities, Relationships of entities are managed as well as Core Attributes. Special care is taken on attributes pointing to actual information resources (links). Sematic web technology provides the best standards and tools for this type of concept, they provide natural support for subject identity management and merging data etc. By default - The knowledge map spans across silo systems and adapts to Big Data!
• Logical types, structures and categories are managed at logical layer, which is pretty much the space we often understand as IM. At this level you can identify traditional system boundaries such as CMS, ERP, PDM or organizations: HR, Sales R&D and so one.
• Content and data of any kind in persistent form are stored and accessible in various systems at physical layer – including Cloud (Open Linked Data). Often the metadata is the only way of binding Info Entity to files in IM systems and further on to the Knowledge Map. It has proven to be very expensive (and slow) trying to harmonize information in legacy systems at this level. It is not even possible to comprehend a meaningful and useful abstraction on the data unless you elevate you thinking to Knowledge Map layer.

The next table tries to explain each layer in more details. I leave this for your review, since the design is under construction and still looking for a full scale business case . One previous Knowledge Map endeavor is described in slide set: Ontology driven portal for NSN.



Cheers, Heimo