Data Unleashed™. The name expresses a vision of data freed from its shackles so that it can be quickly and iteratively accessed, related, studied and expanded. In order to achieve that vision, the process of combining, or federating, the data must be lightweight. That is, the approach must facilitate rapid data set expansion and on-the-fly relationship changes so that we may quickly derive insights. Conversely, the process must not include a significant investment in data structure design since agility requires that we avoid a rigid structure.

Over the past year Blue Slate Solutions has been advancing its processes and technology to support this vision, which comprises the integration between components in our Cognitive Corporation® framework. More recently we have invested in an innovation development project to take our data integration experiences and semantic technology expertise and create a service offering backed by a lightweight data federation platform. Our platform, Data Unleashed™, enables us to partner with customers who are seeking an agile, lightweight enhancement to traditional data warehousing.

I want to emphasize that we believe that the Data Unleashed™ approach to data federation works in tandem with traditional Data Warehouses (DW) and other well-defined data federation options. It offers agility around data federation, benefiting focused data needs for which warehouses are overkill while supporting a process for iteratively deriving value using a lightweight data warehouse™ approach that informs a broader warehousing solution.

At a couple of points below I emphasize differences between Data Unleashed™ and a traditional DW. This is not meant to disparage the value of a DW but to explain why we feel that Data Unleashed™ adds a set of data federation capabilities to those of the DW.

As an aside, Blue Slate is producing a set of videos specifically about semantic technology, which is a core component of Data Unleashed™. The video series, “Semantic Technology, An Enterprise Introduction,” will be organized in two tracks, business-centric and technology-centric. Our purpose in creating these is to promote a holistic understanding of the value that semantics brings to an organization. The initial video provides an overview of the series.

What is Data Unleashed™ All About?

Data Unleashed™ is based on four key premises:

1. the variety of data and data sources that are valuable to a business continue to grow;
2. only a subset of the available data is valuable for a specific reporting or analytic need;
3. integration and federation of data must be based on meaning in order to support new insights and understanding; and
4. lightweight data federation, which supports rapid feedback regarding data value, quality and relationships speeds the process of developing a valuable data set.

I’ll briefly describe our thinking around each of these points. Future posts will go into more depth about Data Unleashed™ as well. In addition, several Blue Slate leaders will be posting their thoughts about this offering and platform.

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At this year’s Semantic Technology and Business Conference in San Francisco, Mike Delaney and I presented a session discussing Semantic Technology adoption in the enterprise entitled, When to Consider Semantic Technology for Your Enterprise. Our focus in the talk was centered on 3 key messages: 1) describe semantic technology as it relates to enterprise data and applications; 2) discuss where semantic technology augments current data persistence and access technologies; and 3) highlight situations that should lead an enterprise to begin using semantic technology as part of their enterprise architecture.

In order to allow a broader audience to benefit from our session we are creating a set of videos based on our original presentation. These are being released as part of Blue Slate Solutions’ Experts Exchange Series.  Each video will be 5 to 10 minutes in length and will focus on one of the sub-topics from the presentation.

Here is the overall agenda for the video series:

We’ll release a couple of videos every other week so be on the lookout during July and August for this series to be completed. We would appreciate your feedback on the information as well as hearing about your experiences deploying semantic technology as part of an enterprise’s application architecture.

The playlist for the series is located at: http://www.youtube.com/playlist?list=PLyQYGnkKpiugIl0Tz0_ZlmeFhbWQ4XE1I The playlist will be updated with the new videos as they are released.

When depicting the Cognitive Corporation™ as a graphic, the use of semantic technology is not highlighted.  Semantic technology serves two key roles in the Cognitive Corporation™ – data storage (part of Know) and data integration, which connects all of the concepts.  I’ll explore the integration role since it is a vital part of supporting a learning organization.

In my last post I talked about the fact that integration between components has to be based on the meaning of the data, not simply passing compatible data types between systems.  Semantic technology supports this need through its design.  What key capabilities does semantic technology offer in support of integration?  Here I’ll highlight a few.

Logical and Physical Structures are (largely) Separate

Semantic technology reduces the tie between the logical and physical structures of the data versus a relational database.  In a relational database it is the physical structure (columns and tables) along with the foreign keys that maintain the relationships in the data.  Just think back to relational database design class, in a normalized database all of the column values are related to the table’s key.

This tight tie between data relationships (logical) and data structure (physical) imposes a steep cost if a different set of logical data relationships is desired.  Traditionally, we create data marts and data warehouses to allow us to represent multiple logical data relationships.  These are copies of the data with differing physical structures and foreign key relationships.  We may need these new structures to allow us to report differently on our data or to integrate with different systems which need the altered logical representations.

With semantic data we can take a physical representation of the data (our triples) and apply different logical representations in the form of ontologies.  To be fair, the physical structure (subject->predicate->object) forces certain constrains on the ontology but a logical transformation is far simpler than a physical one even with such constraints.

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I am excited to share the news that Blue Slate Solutions has kicked off a formal innovation program, creating a lab environment which will leverage the Cognitive Corporation™ framework and apply it to a suite of processes, tools and techniques.  The lab will use a broad set of enterprise technologies, applying the learning organization concepts implicit in the Cognitive Corporation’s™ feedback loop.

I’ve blogged a couple of times (see references at the end of this blog entry) about the Cognitive Corporation™.  The depiction has changed slightly but the fundamentals of the framework are unchanged.

The focus is to create a learning enterprise, where the learning is built into the system integrations and interactions. Enterprises have been investing in these individual components for several years; however they have not truly been integrating them in a way to promote learning.

By “integrating” I mean allowing the system to understand the meaning of the data being passed between them.  Creating a screen in a workflow (BPM) system that presents data from a database to a user is not “integration” in my opinion.  It is simply passing data around.  This prevents the enterprise ecosystem (all the components) from working together and collectively learning.

I liken such connections to my taking a hand-written note in a foreign language, which I don’t understand, and typing the text into an email for someone who does understand the original language.  Sure, the recipient can read it, but I, representing the workflow tool passing the information from database (note) to screen (email) in this case, have no idea what the data means and cannot possibly participate in learning from it.  Integration requires understanding.  Understanding requires defined and agreed-upon semantics.

This is just one of the Cognitive Corporation™ concepts that we will be exploring in the lab environment.  We will also be looking at the value of these technologies within different horizontal and vertical domains.  Given our expertise in healthcare, finance and insurance, our team is well positioned to use the lab to explore the use of learning BPM in many contexts.

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I had the pleasure of attending the Semantic Technology and Business Conference in Washington, DC last week.  I have a strong interest in semantic technology and its capabilities to enhance the way in which we leverage information systems.  There was a good selection of topics discussed by people with a variety of  backgrounds working in different verticals.

To begin the conference I attended the half day “Ontology 101” presented by Elisa Kendall and Deborah McGuinness.  They indicated that this presentation has been given at each semantic technology conference and the interest is still strong.  The implication being that new people continue to want to understand this art.

Their material was very useful and if you are someone looking to get a grounding in ontologies (what are they?  how do you go about creating them?) I recommend attending this session the next time it is offered.  Both leaders clearly have deep experience and expertise in this field.  Also, the discussion was not tied to a technology (e.g. RDF) so it was applicable regardless of underlying implementation details.

I wrapped up the first day with Richard Ordowich who discussed the process of reverse engineering semantics (meaning) from legacy data.  The goal of such projects being to achieve a data harmonization of information across the enterprise.

A point he stressed was that a business really needs to be ready to start such a journey.  This type of work is very hard and very time consuming.  It requires an enterprise wide discipline.  He suggests that before working with a company on such an initiative one should ask for examples of prior enterprise program success (e.g. something like BPM, SDLC).

Fundamentally, a project that seeks to harmonize the meaning of data across an enterprise requires organization readiness to go beyond project execution.  The enterprise must put effective governance in place to operate and maintain the resulting ontologies, taxonomies and metadata.

The full conference kicked off the following day.  One aspect that jumped out for me was that a lot of the presentations dealt with government-related projects.  This could have been a side-effect of the conference being held in Washington, DC but I think it is more indicative that spending in this technology is more heavily weighted to public rather than private industry.

Being government-centric I found any claims of “value” suspect.  A project can be valuable, or show value, without being cost effective.  Commercial businesses have gone bankrupt even though they delivered value to their customers.  More exposure of positive-ROI commercial projects will be important to help accelerate the adoption of these technologies.

Other than the financial aspect, the presentations were incredibly valuable in terms of presenting lessons learned, best practices and in-depth tool discussions.  I’ll highlight a few of the sessions and key thoughts that I believe will assist as we continue to apply semantic technology to business system challenges.

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