Translating Back

Interesting article on how BI applications have to be traced back through the Systems Development Life Cycle to become business information again.  Here is a diagram from the article:

http://www.b-eye-network.com/view/index.php?cid=7566

Mark

Turning Data into Information is Just the Beginning

Final day for me at the Semantic Technology conference in San Jose. I ended this trip with a renewed appreciation for the bigger picture of knowledge management to create and leverage true Intelligence for an organization. In some organizations we are so focused on turning raw data into usable information, we sometimes lose sight of how we can continue to add value to our information assets to turn them into a true competitive advantage.

The following picture shows the full story on the "Intelligence Pyramid" that will add exponentially more value to an organization. I won't take credit for this pyramid, sometimes referred to the "Wisdom Pyramid", but I will share my personal recreation of it.



Lets walk through these 4 stages starting at the bottom, and provide some additional context:

• Data - This can be described as the raw data that would typically support a company's operational processes. For all you ETL buffs, the "Source System". - "What is happening?"


• Information - Transformed information that is not in IT terms, but in business terms and is consumable by the business for reporting and analytical needs. - "What does it mean?"


• Knowledge - Taking the Information we have prepared in the previous step and enriching it with relationships and correlations that start to "tell the story" of what an organization's data contains. - "What do we already know and why?"


• Intelligence - The holy grail, we have leveraged all the previous stages to put all this data to work to develop action plans to resolve the business problem or challenge. - "What do we do?"

This is the true power of data! This goal can not be accomplished by traditional BI tools and techniques alone. We need to add a mix of other technologies that are better at representing relationships and data (i.e. semantic technology), and most importantly to bring together the right people to look at all this data from their different perspectives. With the right mix, we can deliver value to the organization that will pay back the investment in data many, many times over.

Mark

Ontologies Applied to Business Intelligence

After a full day of seminars at the Semantic Technology conference, I ran into an interesting tool that is "new to me". This tool is called Ontology.

An ontology is a formal representation of a set of concepts within a domain and the relationships between those concepts.

OK, I have to admit this is a pretty high level and cerebral topic, lets put this in the context of an example to make it real. Lets say we have a business problem (i.e. the domain) that involves displaying a customer's invoice online. If you think about it, this problem typically has the following entities involved:

• Customer
• Product
• Marketing Strategy
• Person
• Invoice

Does this seem like an exercise in Entity Relationship (ER) modelling? Well to a certain extent it is, but the value an ontology adds on top of this is how these entities are related. This still seems like ER modelling, but lets see how this plays out...

• A Customer subscribes to Products
• A Customer is a Person
• A Marketing Strategy acquires Customers
• A Marketing Strategy sells Products
• An Invoice belongs to a Customer
• An Invoice has Products
• An Invoice includes Marketing Strategies (Bill Messages)
• A Person creates a Marketing Strategy

Are all you data modellers out there feeling confused by all these relationships? 8) Real world relationships seldom fall into the typical hierarchical relationships so common in ER modelling. To fully describe the richness of all relationships we have to step back from the physical data structure and build out a separate meta data store that does not care about the structure, but does care about the context of the data and its relationships. This is typically stored within a database as a Triplestore, which breaks down relationships into "subject" "predicate" and "object".

A Person (subject) creates (predicate) a Marketing Strategy (object)

If each of these 3 pieces had a Uniform Resource Identifier (URI) that uniquely identified each piece, a deceptively simple data model can be created to handle any object and any relationship. In this way any object in the system can be related to any other object using 1 data model.

Once we have this ontology, the next logical step is to map this ontology to the physical data. This not only helps business users navigate data, it is a great tool to facilitate data integration efforts as we can map any data source against the business focused ontology.

What is the value of this for Business Intelligence? In the BI field we are constantly striving to take data and turn it into actionable intelligence. If we had a rich meta data layer that contained a validated ontology we could use this data to uncover correlations in data that would not be uncovered through a simple ER model. As the ontology is in business language it serves as a great tool to bridge the gap between physical data and business entities. This facilitates communication of the organization's data assets between business and IT to ensure no bit or byte goes un-leveraged.

This is but one tool to allow us to bring our users into the development of their applications, better yet lets get the business to own this "layer" since they know it best!

Mark

Semantic Technology Conference 2008 - Questions

Well, I’m in sunny San Jose now, and am preparing for my first day at the Semantic Technology Conference. Before I dive into the seminars, I think it is important to step back and understand what specific questions I am trying to answer by attending this conference.

Here we go:

· How can we bridge the gap between information and human understanding to make information actionable?

· How do we build a business model that represents how the organization perceives it’s products, service and customers? How can we put such a business model in place to help us organize information in a way that it is accessible and takes us to the next level of comprehension?

· How can we leverage user-generated semantic data to enhance applications to add context?

· How can we create a data discovery process that is intuitive and returns results?

· How does Semantic Technology apply to both Business Intelligence and a Service Oriented Architecture?

I’ll throw these questions out for now, and will look to answer them over the next 3 days. I’ll be posting updates to this blog as I have time between sessions. If you have any input on the above, please comment away!

Mark