That is a very dangerous question for a leader to ask when evaluating options. Yet it is one I hear far too often in the healthcare realm. It encapsulates a rejection of innovation, evolution and learning all in one terse, often rhetorical, question.

A common context for this question, often prefixed by, “If this is so great…,” is when discussing semantics and semantic technology. Although these concepts are not new to some industries, such as media, they are foreign in many healthcare organizations. Yet we know that healthcare payers and providers alike struggle with massive data integration and data analytics challenges just like media conglomerates.

The needs to: combine siloed information; drive an analytics mindset throughout an organization; and support the flexibility of a constantly changing IT environment are common in large healthcare organizations. Repeated attempts by organizations to meet these needs betray a lack of consensus around how to best achieve a valuable result.

Further, the implication that how most organizations solve a problem is optimal ignores the fact that best practices must change over time. The best way to solve a problem last year may not be the same this year. The healthcare industry is changing, the physical world of servers, networks, disk drives, memory is changing, and the expectations of members are changing. What was infeasible years ago becomes commonplace. Relational databases were all but unworkable in the 1970s due to a lack of experienced DBAs, slow disk drives, slow processors and limited memory.

In the same way, semantic formalization and graph databases were too new and limited to deal with large data sets until people gained expertise with ontologies while system hardware benefitted from another generation of Moore’s law. In the face of ongoing innovation, the question leaders should ask when approaching a challenge is, “What advancements have been made since the last time we looked at this problem?”

Leadership requires leading, not following. Leaders mentor their organizations through change in order to reach new levels of success. Leadership is based on learning, open-mindedness, creativity and risk-taking. The question, “Why isn’t everybody doing it?” is the antithesis of leadership and has no place there. In fact, if everybody is doing something, a leader would be better off asking, “How do we get ahead of what everybody is doing?”

Leaders must be on the forefront of pushing for better, faster, cheaper. Questioning the status quo, looking for new opportunities, seeking to leapfrog the competition, those are key foci for leadership.

As a leader, the next time you find yourself limiting your willingness to explore an option because everybody isn’t doing it, keep in mind that calculators, computers, automobiles, elevators, white boards, LED light bulbs, Google maps, telephones, the Internet, 3-D printing, open heart surgery, and many more concepts that are accepted or gaining traction, had a day when only one person or organization was “doing it.” Challenge yourself and your organization to find new options, new best practices and new paradigms for advancing your strategy and goals.

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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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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The Cognitive Corporation™ is a framework introduced in an earlier posting.  The framework is meant to outline a set of general capabilities that work together in order to support a growing and thinking organization.  For this post I will drill into one of the least mature of those capabilities in terms of enterprise solution adoption – Learn.

Business rules, decision engines, BPM, complex event processing (CEP), these all invoke images of computers making speedy decisions to the benefit of our businesses.  The infrastructure, technologies and software that provide these solutions (SOA, XML schemas, rule engines, workflow engines, etc.) support the decision automation process.  However, they don’t know what decisions to make.

The BPM-related components we acquire provide the how of decision making (send an email, route a claim, suggest an offer).  Learning, supported by data analytics, provides a powerful path to the what and why of automated decisions (send this email to that person because they are at risk of defecting, route this claim to that underwriter because it looks suspicious, suggest this product to that customer because they appear to be buying these types of items).

I’ll start by outlining the high level journey from data to rules and the cyclic nature of that journey.  Data leads to rules, rules beget responses, responses manifest as more data, new data leads to new rules, and so on.  Therefore, the journey does not end with the definition of a set of processes and rules.  This link between updated data and the determination of new processes and rules is the essence of any learning process, providing a key function for the cognitive corporation.

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Given my role as an enterprise architect, I’ve had the opportunity to work with many different business leaders, each focused on leveraging IT to drive improved efficiencies, lower costs, increase quality, and broaden market share throughout their businesses.  The improvements might involve any subset of data, processes, business rules, infrastructure, software, hardware, etc.  A common thread is that each project seeks to make the corporation smarter through the use of information technology.

As I’ve placed these separate projects into a common context of my own, I’ve concluded that the long term goal of leveraging information technology must be for it to support cognitive processes.  I don’t mean that the computers will think for us, rather that IT solutions must work together to allow a business to learn, corporately.

The individual tools that we utilize each play a part.  However, we tend to utilize them in a manner that focuses on isolated and directed operation rather than incorporating them into an overall learning loop.  In other words, we install tools that we direct without asking them to help us find better directions to give.

Let me start with a definition: similar to thinking beings, a cognitive corporation™ leverages a feedback loop of information and experiences to inform future processes and rules.  Fundamentally, learning is a process and it involves taking known facts and experiences and combining them to create new hypothesis which are tested in order to derive new facts, processes and rules.  Unfortunately, we don’t often leverage our enterprise applications in this way.

We have many tools available to us in the enterprise IT realm.  These include database management systems, business process management environments, rule engines, reporting tools, content management applications, data analytics tools, complex event processing environments, enterprise service buses, and ETL tools.  Individually, these components are used to solve specific, predefined issues with the operation of a business.  However, this is not an optimal way to leverage them.

If we consider that these tools mimic aspects of an intelligent being, then we need to leverage them in a fashion that manifests the cognitive capability in preference to simply deploying a point-solution.  This involves thinking about the tools somewhat differently.

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