The examples you will see below are real cases in which you can see the difference of a knowledge layer running in parallel to the operation of the company.

traditional data layer

The difference between data and knowledge

Knowledge is related data within a context.  It takes key entities and relates them so, even if the Data is completely disassociated or dissimilar, or too deep in the nuts and bolts, it will increase the chance of being found and is being presented in a format for immediate consumption. 


By giving context to your data, you can yield impressive analytics. Context may include time, location, related events, nearby entities, business rules, hierarchies, among others.  


You can live the power of “contextualized data” in all modern apps such as social media, recommenders, search engines, and of course, in virtual assistants as well as in predictive analytics.  The context helps to put the dots together. 


Let’s give an example:

Data only approach: (DATA is black bolded)


Business Scenario:


A high-end supermarket has ordered 1,000 cash register displays for a new product.  The order is to be delivered to the fulfillment center by 9:45am tomorrow.  


The product will be finished on the specialty folder gluer.   

The order is scheduled to run during the second shift.


During production, the machine operator, David Overstreet, has had some stops due to fish-tailing.  At this moment, he is facing his third stop.


This order is for a new customer.  The customer, a high-end supermarket, will have this new luxury brand product exhibit at the POS.  The customer is proud to offer only the best and to ensure compliance therefore the quality of the product, as well as any item in contact with the customer, is well specified for the suppliers.


The customer is considering including this box plant as part of its strategic supply chain.  

Example Production order ERP .png

with smart knowledge layer