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Baseline Findings EPA Enterprise Data Architecture / Data Management Metadata. Mike Fleckenstein, Practice Leader, MDM, Project Performance Corp. mfleckenstein@ppc.com 571-527-6453. Types of Data. Transactional data Measurements at a point in time Dollars earned or units sold

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baseline findings epa enterprise data architecture data management metadata

Baseline FindingsEPA Enterprise Data Architecture / Data Management Metadata

Mike Fleckenstein, Practice Leader, MDM, Project Performance Corp.mfleckenstein@ppc.com571-527-6453

types of data
Types of Data

Transactional data

  • Measurements at a point in time
  • Dollars earned or units sold
  • Used for trend analysis

Reference data

  • Entity by which transactions measured
  • ‘Country’, ‘Prefix’ and ‘Industry
  • Often inconsistently and redundantly stored within an organization

Master data

  • Single version of the truth
  • Key corporate reference entities like ‘Customer’, ‘Location’ and ‘Product’

Metadata

  • Describes objects by connecting objects to the subjects they are about
types of metadata
Types of Metadata

Technical- data sources, access protocol (ODBC, JDBC, SQL*NET, etc.), physical schema (database definition, table definition, column definition, etc), logical data source (ER models, object models, etc.)

Example: people within IT supporting financial reporting know that the financial data mart resides on machine "XPT001;" the data mart is refreshed, "12 a.m. every Saturday night;" data is sourced from "Hyperion GL" and period data was captured in "AP column.”

Business- contextual data about the information retrieved; taxonomies that define business organizations and product hierarchies; controlled vocabulary or reference data that are used to define business terms such as a medical dictionary, financial terminology and such.

Example: people in the finance department know performance reports come "once a month;" "GPR" stands for "Global Performance Report;" "AP7" means "Accounting Period Number 7;" and accounting period starts in "February." These descriptions are business meta data.

metadata related terms
Metadata & Related Terms
  • Metadata describes objects, and one of the ways in which it does that is by connecting objects to the subjects they are about
  • Controlled vocabularyis a closed list of subjects, that can be used for classification
  • Taxonomy is a subject-based classification that arranges the terms in the controlled vocabulary into a hierarchy
  • Thesauri take taxonomies and extend them to make them better able to describe the world by not only allowing subjects to be arranged in a hierarchy
taxonomy
Taxonomy
  • Metadata can be organized using a taxonomy
  • Helps an audience find information more easily
  • Blue lines reflect metadata; black lines reflect taxonomy
  • Blue lines – metadata about the paper
  • Black lines – subject-based taxonomy
taxonomy core characteristics
Taxonomy Core Characteristics
  • Simple terminology
  • Looser, flatter and more intuitive than traditional taxonomies
  • E.g. Eight top levels, three levels deep each
  • Usability in favor of detail
  • Fewer ‘clicks’
  • Must be easy to alter
  • Don’t overanalyze with too many ‘what ifs’
  • United understanding
thesauri e g iso2788
Thesauri (e.g. ISO2788)
  • BT ( Broader Term) - refers to the term above this one in the hierarchy
  • SN (Scope Node) - a string attached to the term explaining its meaning
  • USE - refers to another term that is preferred to this term
  • TT (Top Term) - refers to the topmost ancestor
  • RT (Related Term)- refers to a term, related to this term, without being a synonym
metadata maturity model
Metadata Maturity Model

WITH NO METADATA MGMT

  • Information is lost or hidden
  • Data integration is costly
  • Cannot support everyday business
  • Information is difficult to find
  • Partial& dated information
  • Loss of trust in data

METADATA MANAGEMENT

The organization of technical and business metadata with the goal to advance the sharing, retrieving and understanding of enterprise information assets.

metadata maturity model phase i
Metadata Maturity Model – Phase I

PROCESS

  • Changes are locally acquired, made and consumed
  • Sharing through conversations with ‘incumbents’
  • Infrequent changes

TECHNOLOGY

  • Spreadsheets and unstructured tools
  • Application specific metadata components

PEOPLE

  • Small group of rouge metadata warriors
  • Knowledge is in people’s heads
  • Sharing of metadata is ad-hoc
metadata maturity model phase ii
Metadata Maturity Model – Phase II

PROCESS

  • Limited sharing of metadata
  • Local or semi-local repositories
  • Local attempts at managing metadata
  • Exploration of core metadata and metadata tools

TECHNOLOGY

  • Modeling tools
  • Application specific metadata components
  • Some metadata management tools
  • Mix

PEOPLE

  • Management awareness
  • Sporadic adding to various repositories
  • ‘Talk’ about importance of sharing metadata
metadata maturity model phase iii
Metadata Maturity Model – Phase III

PROCESS

  • Governance process is created and enforced
  • Workflows
  • Communication with ‘outside’ departments
  • Beginnings of real-time integration

TECHNOLOGY

  • Metadata management tools with governance process
  • Workflow engine
  • Business rule engine
  • Data integration tools

PEOPLE

  • Data stewards
  • Data governance body
  • Management understands importance of administering metadata
metadata maturity model phase iv
Metadata Maturity Model – Phase IV

PEOPLE

  • Constantly seeking optimization
  • Metadata administrators – centralized validation

PROCESS

  • Enterprise-level standards
  • Taxonomy, Ontologies, etc.
  • Authoritative data sources for entities

TECHNOLOGY

  • Collaboration tools
  • Enterprise data modeling tool
  • Vocabulary and taxonomy management tool
metadata maturity model phase v
Metadata Maturity Model – Phase V

PEOPLE

  • Start managing metadata as part of business
  • Critical, ubiquitous, invisible part of the organization

TECHNOLOGY

  • Ontology management
  • Reasoning technology
  • Data mediation

PROCESS

  • Automated real-time integration
  • Domain ontologies & topic maps
  • Seamless integration at low cost
data governance components
Data Governance Components
  • Data Stewards
    • Principle – ‘Guardians’ of Data
    • Business – Help define data and stewardship standards
  • Data Architects
    • Part of EA; Understand EA
    • Broker requests for new data and data changes
    • Responsible for enterprise-wide taxonomy
  • Data Advisory Committee (DAC)
    • Strategic
    • Managers & Execs
    • Broad representation
  • Infrastructure Team
    • Responsible for physical architecture and data provision
    • DBA’s & Developers
    • Systems & Network Administrators
value vs cost of metadata
Value vs. Cost of Metadata

High awareness but no governance

  • ROI point
  • Start of governance
  • Right of Phase III

Sharp rise in cost for unmanaged metadata

the dublin core standard
The Dublin Core Standard
  • Created in 1995 to aid internet searches
  • Most common standard
  • Primarily for 'document-like objects' (DLOs)
  • Example: 'Author = Ronald Snijder‘
  • Qualifier: 'Author (type=personalName) = Ronald Snijder‘
  • Each element can be repeated (e.g. 'Author (type=personalName) = Seargent Pepper‘
  • Every metadata description should describe just one information resource
  • 15 Elements
slide19

Not syntax specific

Each element is optional

Intrinsicality

Many Standards bodies exist

Extensions can be used & registered

DC content is modifiable

dublin core framework extensions
Dublin Core Framework & Extensions

Domain specific metadata extensions (e.g. geospatial)

Dublin Core adopted as standard

Metadata extensions for managing information through its lifecycle

Mandatory set of Common Look and Feel elements

Extensions for clusters and gateways