27 Jun Enterprise Meta-Model Governance

Joe Roushar – June 2016

Is knowledge important to you or your organization? What value does it bring? Are there types of knowledge that are more valuable than others? Do you know where your knowledge is right now, and what it’s doing? Business Knowledge comes in multiple flavors. Business Information systems are used to process data or “content” that is structured or unstructured, across different flavors of knowledge. “Metadata is structured information that describes, explains, locates, or otherwise makes it easier to retrieve, use, or manage an information resource. Metadata is often called data about data or information about information” (NISO 2004). Models can help improve the consistency for understanding and accessing the contents of different systems, so having a standard metadata model is a big plus.

Metadata can describe both the structured content in databases, tables and columns, and unstructured content in images, videos, audio files and documents. Meta-content governance brings “enterprise” level discipline to the metadata creation and management processes in each area of an organization. This can help achieve horizontal alignment across disparate divisions and stakeholder groups.

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Section 8 #26

Table of Context

The National Information Standards Organization (NISO) describes three main types of metadata:

• Descriptive metadata describes a category (glossary) or single instance of a digital resource for discovery and identification with elements such as subject, title, abstract, author, and keywords. A taxonomical model is descriptive.
• Structural metadata indicates file or data type, formal link structures or how compound objects, such as pages and chapters of a book, are put together.
• Administrative metadata provides information to help manage a resource, such as when, by what system or how it was created, data lineage, file type and other technical information, and who can access it. Types of administrative meta-content may include:
  • Rights management metadata describing intellectual property rights, and
  • Preservation metadata describing policies and procedures governing resource archival and preservation (ibid).

These types also apply to meta-knowledge about structured and unstructured content needed for meta-content governance. Meta-knowledge, or semantic knowledge about content, provides insight into what is represented by the tables, columns, attributes, objects, dimensions, files and documents. These are the things knowledge workers gather and use to make better business decisions. Semantic insights include:

• Data Element and Category definitions
• Formulas for combining data into results and performance indicators
• Source and lineage information (Where it came from and who manipulated it)
• Sensitivity and other business policy-related information
• The nature of associations between related data elements
• The types of people who would find the contents or presentation most useful
• The people responsible for managing the metadata

Decision makers often use the term “insight” to refer to information that describes or predicts customer or market behaviors and trends. The introspective insights in meta-knowledge facilitate and help deliver customer and market insights that can enhance success and competitiveness.

When establishing metadata management strategies for large organizations, it helps to define a minimal set of attributes to capture in metadata. The overall process sometimes makes a distinction between global processes, such as defining the minimum attributes to always capture, and local processes that apply to each individual asset in the knowledge inventory. In the illustration below, what NISO describes as “Structural Metadata” may be included in the Knowledge Asset or the Packaging Metadata.

In the new knowledge enterprise, business users will have more ability to identify critical content from documents, audio, video and image files as well as databases using advanced search. With the use of advanced models and model management tools, including content models (canonical and metadata) and process models (BPM) they will be able to define and redefine their own enabling tools. Citizen developers will be able to customize dashboards and reports, and even the workflows and rules that feed the repositories, warehouses, marts and lakes from which they draw meaningful information. The outputs of some of the more intelligent systems will be in the form of actionable knowledge. The better the meta-model – the more actionable knowledge can be delivered.

Automated processes for creating metadata, in bulk through mining, or for each transaction, are needed to feed the model and the users with important semantic information. The model without the instrumentation is not enough. Value-chain instrumentation is one way the lineage and transaction metadata can be captured. Ideally, services that form part of every CRUD transaction go beyond logging the transaction (logs are hard to access and often out of reach for most users) to provide a compact statement of the current step in the lineage. Along with automatically mined data, this becomes a permanent part of the historical record of that data element and its successors, aggregates and KPIs. When needed, manual input can be used to augment and enrich metadata for content of any type.

Retrofitting existing systems, especially commercial software with value-chain instrumentation is naturally more difficult, and sometimes requires mining logs to extract lineage data. But the instrumentation delivers such rich and helpful historical data, especially needed when researching downstream data quality problems, that the effort is almost always worth the cost. (For further information on value-chain instrumentation, see Joe’s post at https://understandingcontext.com/2015/04/knowledge-value-chain-instrumentation/).

What problem are we solving?

Meta-knowledge of any sort is used to clarify ambiguities in data and expose implications of change. In an enterprise, inspecting the metadata can resolve ambiguities when users or auditors ask “Where did this data come from and how was it calculated?” Well implemented canonical models combined with up-to-date metadata can help technicians answer “How will this change affect connected systems or downstream data consumers and reports?” Both are served by the transparency provided by good metadata. In some cases, good semantic metadata can interpret requests and provide more complete answers or collateral information that can make the results more actionable.

These are significant benefits of metadata for meta-content management, but how do you govern its creation and management? The same stewardship and governance strategies that improve the consistency and quality of enterprise data today can be used in the future “knowledge enterprise” with a few additional considerations: especially reducing the stewards’ burdens with automated content and data discovery and automated metamodel generation. More to come…

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