Good metadata is critical to helping people find information. Metadata can be used to enhance search tools, drive navigation and relate documents to one another. Unfortunately, manually adding metadata to content is cumbersome for small batches of content and impractical or impossible for large content sets. Enterprise Knowledge understands the difficulty and importance of maintaining metadata. In this session, we will share 6 different ways to simplify and/or automate metadata management even on extremely large content sets. We will share the tools and techniques we have used with our clients to make metadata management possible and provide real world examples as to how these techniques can be applied to your content.

1. Six ways to Simplify Metadata Management
Presentation for 2014 KM World Conference
© Enterprise Knowledge, LLC

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The Importance of Metadata
• Makes content/information findable
• Explains structure or provides context
• Informs document retention policies
• Aids in the securing of content
• Drives workflow
• Allows for reporting on trends in unstructured
data
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met·a·da·ta
ˈmedəˌdādə,ˈmedəˌdadə/
noun
noun: metadata; noun: meta-data
a set of data that describes and gives information about other data.

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Content without Metadata Leads to Chaos
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6 Ways to Simplify Metadata Management
1. Implied Metadata
2. Linked Metadata
3. Entity Extraction
4. Auto-categorization
5. Pattern Matching
6. Batch Metadata Management
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Implied Metadata
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Definition: Metadata that is derived from a pre-existing
attribute of the content.
Examples include:
• Information based on the folder
in which a file resides.
• Information based on the author
of the file.
• Information based on the file
name or document type.
Success story:
• A large government agency
needed to track building and
project plans for buildings across
the country.
• They setup a folder hierarchy
that organized content by state,
building number and project
number.
• The location of the content
could then be used for the
metadata.

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Linked Metadata
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Definition: Data or information that is related to, but not
directly associated with a piece of content.
Examples include:
• A topic based on the author of a
piece of content.
• The business owner of a piece of
content based on the location
where it is stored.
• The state of a business based on
the city it resides in.
Success story:
• A publisher wanted to improve
their article search. Authors on
their staff were responsible for
specific topics.
• The topic that the author wrote
about was added as metadata
for each piece of content so that
it could be offered as a search
facet.

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Entity Enrichment
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Definition: The automatic identification of people, places,
and things in a textual document. The entities are
typically tagged in-line.
Examples include:
• The name of a famous person in
an article.
• The location described in an
article or report.
• A business listed in a financial
report.
• Identify an industry mentioned
in a report.
Success story:
• A large rating agency needed
new products.
• The agency used entity
enrichment to identify locations
and industries in financial
reports.
• They used this information to
develop a product that allowed
registered users to select and
group sections of financial
reports based on industry or
company location.

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Auto-categorization
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Definition: Systems that automatically group related
pieces of content to general categories typically defined by
a taxonomy.
Examples include:
• Tools that assign content to
folders based on predefined
queries or rules.
• Tools that assign content to
folders based on concepts based
on statistical analysis.
• Tools that assign content to
folders based on entity
enrichment.
Success story:
• A large financial institution
needed to improve their
workflow management.
• They used auto-categorization
of the incoming forms to route
them to the appropriate team.
• This increased the speed with
which forms could be processed.

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Pattern Matching
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Definition: Automatically extracting information based on
a consistent structure or pattern of text within a file.
Typically used on forms.
Examples include:
• Extracting company names out of
contracts.
• Extracting names out of forms.
• Identifying attendees of a
meeting from standard meeting
minutes.
Success story:
• A large government agency
wanted to find better ways to
share their content with the
public.
• We identified patterns in the
way hearings were stored. We
used the patterns to identify
Senators that did not attend
hearings and provide reports on
attendance by topic.

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Batch Metadata Management
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Definition: A tool for managing topical metadata on large
sets of related content based on search.
Examples include:
• Manually adding topics to news
items in batch.
• Adding metadata to prioritize
content for audit purposes.
• Identifying and grouping related
content.
Success story:
• A news site wanted to group
similar content together under
subject pages. The process of
tagging each piece of content
individually was too time
consuming.
• They implemented batch
metadata management and
were able to tag the content in
20% of the time it used to take.

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Summary Best Practices
• Do not accept a poor search because it is too hard to
manage metadata or you lack budget.
• Consider automated metadata management to
improved findability and increase adoption of
content management tools.
• Work with experts to identify a business taxonomy
for your content and ways to automate the
management of the metadata.
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12. For More Information Please Contact:
Joe Hilger
(c) 571.436.0271
jhilger@enterprise-knowledge.com
@EKConsulting
© Enterprise Knowledge, LLC
Questions?
Comments?
Thank you!