On the Road out of Metadata Chaos Toward Maturity: Strategic Listening

Cloud applications development, NoSQL database environments, and hybrid platforms, such as MongoDB, treat metadata as data encoded in scripts produced by developers.  The development environment is challenged by the need for speed, flexibility, and functional and user experience requirements.  Application development may reference or assume the existence of metadata or standards but is not suited to generating organization metadata standards.  Indeed, the developer's view of metadata is necessarily limited to resources with which a particular body of scripts is required to interact. 

Complexity and enterprise maturity

Pragmatism produces complexity.  Living with complex data and multiple semantic standards means coping with organizational messiness in which the limited system interoperability becomes yet another issue for development someday, maybe, if it is not too hard, if there is enough of a business case, if...  The effective result is costly organizational stagnation and an inefficient pattern of lurching toward technology adoption and innovation.

To achieve maturity enterprises continuously optimize the use of capital, people, technology, and information to achieve strategic goals.  Without metadata standards pragmatically implemented through a domain governance process, the maturity process is hindered as analysts and business experts struggle to understand, model, and represent knowledge, construct meaningful queries, and reconcile semantic inconsistencies in an effort to answer business questions and visualize customer interactions.  Too often, the results of these efforts do not inspire confidence in their accuracy, currency, or reliability they deserve.

In the struggle to survive, a tactical plan is easily confused with a strategy for success.  The difference between a strategy and a tactical plan is expressible in one word: goal.  Tactics focus on achieving specific task based results.  Tactical plans are sequences of tasks and results.  This is not a strategy or strategic plan.  Strategies are clusters of approaches organized around achieving a goal onto which specific tactics maybe mapped as the process of moving toward the goal is implemented. 

"Organizations fall into permanent immaturity when the goal is self-contained – technology for the sake of technology, knowledge organization for the sake of knowledge organization, thrilling user experiences for the sake of thrilling user experiences."

Governance and modeling

To escape the traps that lead to complexity, confusion, and organizational immaturity a return to fundamentals is required.  Units of information (data) need to be examined closely within their operational context.  The enterprise requires, and must have, an explicit model of the sources (system architecture), context (services and network architecture), and meaning (metadata architecture) of the information produced and used (business value) of the data.  These fundamental understandings describe the knowledge framework, or logical structure, which, taken in conjunction with an enterprise maturity goal, that allow a strategy to be formulated, with tactics grounded in one's current state to be mapped and planned.

In linguistics, syntax is the set of rules that allow words to be organized into speech that can be understood by others.  For a business, this means developing a clear process for implementing and managing metadata as organizational standards to enable information integration and system interoperability.  Broadly, within the realm of information technology this can be called a "governance" process.  For those working to produce and structure content it is sometimes called "information architecture" management.

Just as syntax by itself only describes the rules for recognizing a well-formed utterance and says nothing about meaning, having governance or architectural control does not result in either enterprise maturity or success.  Governance of standards and architectures is effective only to the extent that it aligns organizational semantics and systems with organizational goals and use cases.  One of the reasons why organizations frequently find themselves dissatisfied with generically produced taxonomies, metadata standards, and technology platform standards is that that these are only generally aligned with enterprise market niche and business-specific use cases.  Discerning how to align enterprise knowledge needs, especially within the context of knowledge acquisition plans and application development environments is facilitated by contextualizing use cases, knowledge sets, and business activities within a domain model that will enable managing complexity around strategic goals.  

At the risk of being technical for a moment, domain models specifying the valid relationships between all the enterprise pieces and distinguish classes of things (is-ness) from the attributes (aboutness).  These relationships are express in terms of identities (isA) and of functionalities (hasA) that are themselves able to generate additional information (metadata and value sets).  In a very real sense, domain models, systems and services architectures, and multiple, integrated metadata taxonomies establish, or ground, enterprise knowledge in the business of the enterprise.

Acquiring this level of organizational self-understanding and knowledge and making it usable by all enterprise stakeholders, applications developers, data administrators, knowledge workers, marketers and executives, and outside users is a serious investment.  As with other serious investments, specialized skills and approaches are needed, while outside resources can step back from institutional politics to provide a healthy perspective that can facilitate moving forward.

Listening and multichannel knowledge

Discovery of enterprise knowledge grounded in business activity is meaningful, defines the semantic goal.  Management of complex organizational semantics therefore requires strategic as well as tactical governance processes involving all facets of the enterprise. Governance is strategic when it is focused on providing a cultural framework for achieving measurable outcomes, integrating business objectives, clarifying data management tasks.  As such, is a core element in transforming an enterprise into an integrated activity, with the expertise of each business unit an effective contributor to an overall, multi-dimensional knowledge-based social organization.  Efficient cooperation relies on communication, structural management policies and decision-making authority, and a negotiation matrix that pragmatically decides semantic values and implements data hygiene activities.

One especially important challenge to be considered is constant churn in third-party systems essential to the organization's activities.  These systems inject external standards into an enterprise, requiring – and requiring it to make – implementation decisions which results in conflicts with internal systems.  Once again, the analysis appears to be shifting the source of systemic chaos to the governance process itself, bringing into question its effectiveness and legitimacy.

In the logic of linguistics, a particular instance of a topic not included in the topical domain cannot be expressed in the language of the main topic.  When understood, this is an extraordinarily powerful principle, it means that without discovering what is not being said, one does not have a complete understanding of a domain.  By changing perspective, however, it is possible for missing elements to be given a voice and a way out of chaos can be found.  Facilitating the identification of where perspective changes can be made represents another value proposition of using a taxonomy consultant.  To work successfully with a taxonomy consultant, tactical and strategic components will define the work as a genuine engagement, and the organization needs to be willing to commit the resources necessary for the taxonomist to develop authentic insights into the endeavor.  Blind to an enterprise, a taxonomist can be neither Athena springing from the head of Zeus nor Homer able to write panegyrics to the gods.

The business activity of listening produces data.  Semantics, standards governance are products of transforming what is heard into actions.  Learning to listen and learn requires paying attention to many sources of information and to use multiple channels of communication. 

The assumption producing the chaos is thinking that metadata needs to integrate into a monolithic system when the reality is that diversity abounds.  The challenge then is how to represent that diversity respectfully.  Completeness is more important for understanding than absolute consistency.  What that means is the organizations that approach metadata and taxonomy with the strategic objective of optimizing its resources as knowledge are those most likely to be able to effectively respond in a dynamic age of knowledge exploration and discovery.   



Taxonomy provides a fundamental model for responding to the challenges of navigating multiple vocabularies and metadata sets.  The process of discovering semantic richness starts with creating concept-oriented taxonomies organized to give structure to the diversity of topics (facets) while preserving the integrity of the information.  The next step is to understand and define the relationships both in and across associative relationships allowing faceted taxonomy structures to give pragmatically granular representation to information sources from multiple perspectives and contexts.  However, the approach does not solve the problem of disambiguating vocabulary when that concept or vocabulary element is not ambiguous in context.  To those who have followed the development of taxonomy, this is little more than the old discussion of whether it is best to use key-words in context or key-words out of context as the basis for information retrieval.

"New" network technologies provide a powerful implementation platform for the interoperable implementation of complex taxonomies.  A common name for this new technology is - the Internet.  The internet is possible through standards that are created and managed by the World Wide-Web Consortium (W3C).   

Two W3C standards are relevant to the resolution of data and semantic complexity.  These are the Resource Descriptor Framework (RDF) and the Simplified Knowledge Organization System (SKOS).  Taken together, these are central elements to a concept/semantics driven approach to information access and organization.  What makes these technologies exciting from an enterprise perspective is that they may actually be easier to use on an enterprise network and cloud architectures than on the open-ended universe of the Internet.  That an increasing number of organizations are turning to them to solve issues is seen in the increasing number of taxonomy management and systems architecture tools that support these technologies.  Another W3C standard, a semantic query language (SPARQL), adds to the excitement by providing a rigorous formal language for asking questions of, and making inferences about, the data and the structural relationships between the data.

These technology standards can be used to enable organizations to listen to meaning by connecting data to unify a diversity of data sources, not just databases, and metadata values into a system of concepts that the enterprise itself can define, understand, and incorporate into its overall governance processes.  This can allow the data governance, network governance, and taxonomy governance, and systems management processes and teams to work together to create a genuinely mature and more stable enterprise knowledge management framework.

Out of chaos

The way out of chaos is the acquisition of maturity.  Maturity is a product of hard work that has sustained and managed over time.  Metadata governance is a core process for acquiring maturity.  Governance is required because contexts are dynamic.  One of the most dynamic elements in the current business environment is technology.  Mature metadata governance expands its understanding of taxonomy as a model from a metadata standard for data, content/asset management systems to include new technologies and opportunities for data capture within an ever-changing organization.

A  fundamental take-away from my foregoing discussions is that because language is a social activity the way out of chaos requires carefully representing how each of enterprise domains understands and expresses the meaning of what is heard and known by the many speakers of that language.  Capturing the rich meaning speakers invest in their language requires active listening and the setting of pragmatic standards though a governance process that is able to conceptually normalize  the language for use by the organization.  Finally, that a well-designed enterprise taxonomy engagement is a strategic opportunity to enhance business processes, all levels of metadata management, and to provide assistance in increasing organizational capacity to consistently identify, listen, capture and use information from the widest spectrum of voices.

To learn more about how we can help you with this process contact us.

Meet the Author
Seth Earley

Seth Earley is the Founder & CEO of Earley Information Science and the author of the award winning book The AI-Powered Enterprise: Harness the Power of Ontologies to Make Your Business Smarter, Faster, and More Profitable. An expert with 20+ years experience in Knowledge Strategy, Data and Information Architecture, Search-based Applications and Information Findability solutions. He has worked with a diverse roster of Fortune 1000 companies helping them to achieve higher levels of operating performance.