data-ed: metadata strategies

Metadata Management Strategies

Copyright 2014 by Data Blueprint

Good systems development often depends on multiple data management disciplines that provide a solid foundation. One of these is metadata. While much of the discussion around metadata focuses on understanding metadata itself along with its associated technologies, this perspective often represents a typical tool-and-technology focus, which has not achieved significant results to date. A more relevant question when considering pockets of metadata is whether to include them in the scope of organizational metadata practices. By understanding what it means to include items in the scope of your metadata practices, you can begin to build systems that allow you to practice sophisticated ways to advance data management and supported business initiatives with a demonstrable ROI. After a bit of practice in this manner you can position your organization to better exploit any and all metadata technologies in support of business strategy.

Date: November 11, 2014 Time: 2:00 PM ET/11:00 AM PT Presenter: Peter Aiken, Ph.D.

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PETER AIKEN WITH JUANITA BILLINGSFOREWORD BY JOHN BOTTEGA

MONETIZINGDATA MANAGEMENT

Unlocking the Value in Your Organization’s

Most Important Asset.

Peter Aiken, Ph.D.

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The Case for theChief Data OfficerRecasting the C-Suite to LeverageYour Most Valuable Asset

Peter Aiken andMichael Gorman

• 30+ years data management experience

• Multiple international awards/recognition

• Founder, Data Blueprint (datablueprint.com) • Associate Professor of IS, VCU (vcu.edu)

• (Past) President, DAMA Int. (dama.org) • 9 books and dozens of articles • Experienced w/ 500+ data

management practices in 20 countries • Multi-year immersions with

organizations as diverse as the US DoD, Nokia, Deutsche Bank, Wells Fargo, Walmart, and the Commonwealth of Virginia

Peter Aiken, Ph.D.


10124 W. Broad Street, Suite C Glen Allen, Virginia 23060

804.521.4056

Whither the "data dictionary?

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• The classic "data dictionary" pretty much died by the early '90s - ... they ever-so-kindly renamed "data dictionary" to "metadata repository" & then promptly

went belly up. Ask pretty much IBMer today if they've ever heard of AD/Cycle or RepositoryManager... guaranteed response will be a blank stare.

• My calculation says 5% survival rate from 1973 to 2003

• Metadata has morphed into a meaningless buzzword … - Yet organizations are suffering from unprecedented amounts of new forms of seriously

unmanaged metadata.

• I've essentially given up on trying to grok what metadata is other than "required buzzword" (Dave Eddy/[email protected])


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1. Data Management Overview

2. What is metadata and why is it important?

3. Major metadata types & subject areas

4. Metadata benefits, application & sources

5. Metadata strategies & implementation

6. Metadata building blocks

7. Guiding Principles

8. Specific teachable example

9. Take Aways, References and Q&A

Tweeting now: #dataed











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Uses

What is data management?

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Sources

Data Governance

Data Engineering

Data Delivery

DataStorage

Specialized Team Skills

• Data management practices connect data sources and uses in an organized and efficient manner – Storage – Engineering – Delivery – Governance

• When executed, engineering, storage, and delivery implement governance

DMM℠ Structure

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Five Integrated DM Practice AreasManage data coherently.

Share data across boundaries.

Assign responsibilities for data.Engineer data delivery systems.

Maintain data availability.

Data Program Coordination

Organizational Data Integration

Data Stewardship Data Development

Data Support Operations

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DMM℠ Structure

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Maslow's Hierarchiy of Needs

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You can accomplish Advanced Data Practices without becoming proficient in the Foundational Data Management Practices however this will: • Take longer • Cost more • Deliver less • Present

greaterrisk(with thanks to Tom DeMarco)

Data Management Practices Hierarchy

Advanced Data

Practices • MDM • Mining • Big Data • Analytics • Warehousing • SOA

Foundational Data Management Practices

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Data Platform/Architecture

Data Governance Data Quality

Data Operations

Data Management Strategy

Technologies

Capabilities

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Data Management

Functions

DAMA DM BoK & CDMP

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• Data Management Body of Knowledge (DMBOK) – Published by DAMA International, the

professional association for Data Managers (40 chapters worldwide)

– Organized around primary data management functions focused around data delivery to the organization and several environmental elements

• Certified Data Management Professional (CDMP) – Series of 3 exams by DAMA International and

ICCP – Membership in a distinct group of

fellow professionals – Recognition for specialized knowledge in a

choice of 17 specialty areas – For more information, please visit:

• www.dama.org, www.iccp.org

Metadata Management from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International

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What is a Strategy?

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• Current use derived from military • "a pattern in a stream of decisions" [Henry Mintzberg]

• "a system of finding, formulating, and developing a doctrine that will ensure long-term success if followed faithfully [Vladimir Kvint]

Meta data, Meta-data, or metadata

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• In the history of language, whenever two words are pasted together to form a combined concept initially, a hyphen links them

• With the passage of time, the hyphen is lost. The argument can be made that that time has passed

• There is a copyright on the term "metadata," but it has not been enforced

• So, term is "metadata"

Definitions

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• Metadata is – Everywhere in every data management activity and integral

to all IT systems and applications. – To data what data is to real life. Data reflects real life transactions, events,

objects, relationships, etc. Metadata reflects data transactions, events, objects, relations, etc.

– The data that describe the structure and workings of an organization’s use of information, and which describe the systems it uses to manage that information. [quote from David Hay's book, page 4]

• Data describing various facets of a data asset, for the purpose of improving its usability throughout its life cycle [Gartner 2010]

• Metadata unlocks the value of data, and therefore requires management attention [Gartner 2011]

• Metadata Management is – The set of processes that ensure proper creation, storage, integration, and

control to support associated use of metadata

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Analogy: a library card catalog

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• Identifies – What books are in the library, and – Where they are located

• Search by – Subject area – Author, or – Title

• Catalog shows – Author – Subject tags – Publication date and – Revision history

• Determine which books will meet the reader’s requirements

• Without the catalog, finding things is difficult, time consuming and frustratingfrom The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International

Definition (continued)

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• Metadata is the card catalog in a managed data environment

• Abstractly, Metadata is the descriptive tags or context on the data (the content) in a managed data environment

• Metadata shows business and technical users where to find information in data repositories

• Metadata provides details on where the data came from, how it got there, any transformations, and its level of quality

• Metadata provides assistance with what the data really means and how to interpret it

from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International

Defining Metadata

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Metadata is any combination of any circle and the data in the center that unlocks the value of the data!

Adapted from Brad Melton

Data

WhereWhy

What How

Who

When

Data

Library Metadata Example

Libraries can operate efficiently through careful use of metadata (Card Catalog)Who: Author What: Title Where: Shelf Location

When: Publication Date

A small amount of metadata (Card Catalog) unlocks the value of a large amount of data (the Library)

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Data

WhereWhy

What How

Who

When

Library Book

Outlook Example

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"Outlook" metadata is used to navigate/manage email

What: "Subject" How: "Priority" Where: "USERID/Inbox", "USERID/Personal" Why: "Body" When: "Sent" & "Received”

• Find the important stuff/weed out junk • Organize for future access/outlook rules • Imagine how managing e-mail (already non-trivial)

would change if Outlook did not make use of metadata Who: "To" & "From?"

Uses

What is the structure of metadata practices?

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Sources Metadata Governance

Metadata Engineering

Metadata Delivery

Metadata Practices

Metadata Storage


• Metadata practices connect data sources and uses in an organized and efficient manner – Storage: repository, glossary, models, lineage - often multiple

technologies – Engineering: identifying/harvesting/normalizing/administer

evolving metadata structures – Delivery: supply/access/portal/definition/lookup search

identify/ensure required metadata supplies to meet business needs

– Governance: ensure proper/creation/storage/integration/control to support effective use

• When executed, engineering and delivery implement governance

Polling Question #1

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• My organization began using or is planning to use a formal approach to metadata management

a) Last year (2013) b) This year (2014) c) Next year (2015) d) Not at all

Polling Question #1 (from last year)

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• My organization began using or is planning to use a formal approach to metadata management

a) Last year (2012) 38% b) This year (2014) 13% c) Next year (2015) 14% d) Not at all 15%


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Types of Metadata: Process Metadata

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• Process Metadata is... – Data that defines and describes the characteristics of other system

elements, e.g. processes, business rules, programs, jobs, tools, etc.

• Examples of Process metadata: – Data stores and data involved – Government/regulatory bodies – Organization owners and stakeholders – Process dependencies and decomposition – Process feedback loop and documentation – Process name


Business Process Metadata

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Who: Created the documentation?

What: Are the important dependencies among the processes?

How: Do the business processes interact with each other?

Data

WhereWhy

What How

Who

When

Email Messag

e

Types of Metadata: Business Metadata

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• Business Metadata describe to the end user what data are available, what they mean and how to retrieve them.

• Included are:

– Business names and definitions of subject and concept areas, entities, attributes

– Attribute data types and other attribute properties

– Range descriptions, calculations, algorithms and business rules

– Valid domain values and their definitions


Types of Metadata: Technical & Operational Metadata

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• Technical and operational metadata provides developers and technical users with information about their systems

• Technical metadata includes… – Physical database table and column names, column properties, other

properties, other database object properties and database storage • Operational metadata is targeted at IT operations users’

needs, including… – Information about data movement, source and target systems, batch

programs, job frequency, schedule anomalies, recovery and backup information, archive rules and usage

• Examples of Technical & Operational metadata: – Audit controls and balancing information – Data archiving and retention rules – Encoding/reference table conversions – History of extracts and results


Types of Metadata: Data Stewardship

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• Data stewardship metadata is about... – Data stewards, stewardship processes, and responsibility

assignments

• Data stewards… – Assure that data and Metadata are accurate, with high quality

across the enterprise. – Establish and monitor data sharing.

• Examples of Data stewardship metadata: – Business drivers/goals – Data CRUD rules – Data definitions – business and technical – Data owners – Data sharing rules and agreements/contracts – Data stewards, roles and responsibilities


Types of Metadata: Provenance

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• Provenance: – the history of ownership of a valued object or work of art or

literature" [Merriam Webster] – For each datum, this is the description of:

• Its source (system or person or department), • Any derivation used, and • The date it was created.

– Examples of Data Provenance: • The programs or

processes by which it was created

• Its owner • The steward responsible

for its quality • Other roles and

responsibilities • Rules for sharing it


Metadata Subject AreasSubject Areas Components

1) Business Analytics Data definitions, reports, users, usage, performance

2) Business Architecture Roles and organizations, goals and objectives

3) Business Definitions Business terms and explanations for a particular concept, fact, or other item found in an organization

4) Business Rules Standard calculations and derivation methods

5) Data Governance Policies, standards, procedures, programs, roles, organizations, stewardship assignments

6) Data Integration Sources, targets, transformations, lineage, ETL workflows, EAI, EII, migration/conversion

7) Data Quality Defects, metrics, ratings

8) Document Content Management

Unstructured data, documents, taxonomies, ontologies, name sets, legal discovery, search engine indexes

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Metadata Subject Areas, continuedSubject Areas Components

9) Information Technology Infrastructure Platforms, networks, configurations, licenses

10)Conceptual data models Entities, attributes, relationships and rules, business names and definitions.

11)Logical Data Models Files, tables, columns, views, business definitions, indexes, usage, performance, change management

12)Process Models Functions, activities, roles, inputs/outputs, workflow, timing, stores

13)Systems Portfolio and IT Governance

Databases, applications, projects, and programs, integration roadmap, change management

14)Service-oriented Architecture (SOA) information:

Components, services, messages, master data

15)System Design and Development Requirements, designs and test plans, impact

16)Systems Management Data security, licenses, configuration, reliability, service levels

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7 Metadata Benefits

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1. Increase the value of strategic information (e.g. data warehousing, CRM, SCM, etc.) by providing context for the data, thus aiding analysts in making more effective decisions.

2. Reduce training costs and lower the impact of staff turnover through thorough documentation of data context, history, and origin.

3. Reduce data-oriented research time by assisting business analysts in finding the information they need in a timely manner.

4. Improve communication by bridging the gap between business users and IT professionals, leveraging work done by other teams and increasing confidence in IT system data.

5. Increased speed of system development’s time-to-market by reducing system development life-cycle time.

6. Reduce risk of project failure through better impact analysis at various levels during change management.

7. Identify and reduce redundant data and processes, thereby reducing rework and use of redundant, out-of-data, or incorrect data.


Metadata for Semistructured Data

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• Unstructured data – Any data that is not in a database or data file, including documents or other

media data • Metadata describes both structured and unstructured data • Metadata for unstructured data exists in many formats,

responding to a variety of different requirements • Examples of Metadata repositories describing unstructured data:

– Content management applications – University websites – Company intranet sites – Data archives – Electronic journals collections – Community resource lists

• Common method for classifying Metadata in unstructured sources is to describe them as descriptive metadata, structural metadata, or administrative metadata


Metadata for Unstructured Data: Examples

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• Examples of descriptive metadata: – Catalog information – Thesauri keyword terms

• Examples of structural metadata – Dublin Core – Field structures – Format (audio/visual, booklet) – Thesauri keyword labels – XML schemas

• Examples of administrative metadata – Source(s) – Integration/update schedule – Access rights – Page relationships (e.g. site navigational design)

Specific Example

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• Four metadata sources:

1. Existing reference models (i.e., ADRM)

2. Conceptual model created two years ago

3. Existing systems (to be reverse engineered)

4. Enterprise data model

} from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International


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Metadata History 1990-2008

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• The history of Metadata management tools and products seems to be a metaphor for the lack of a methodological approach to enterprise information management:

• Lack of standards and proprietary nature of most managed Metadata solutions cause many organizations to avoid focusing on metadata

• This limits organizations’ ability to develop a true enterprise information management environment

• Increased attention given to information and its importance to an organization’s operations and decision-making will drive Metadata management products and solutions to become more standardized

• More recognition to the need for a methodological approach to managing information and metadata

Metadata History: The 1990s

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• Business managers began to recognize the value of Metadata repositories

• Newer tools expanded the scope

• Potential benefits identified during this period include:

– Providing semantic layer between company’s system and business users

– Reducing training costs

– Making strategic information more valuable as aid in decision making

– Creating actionable information

– Limiting incorrect decisions

Metadata History: Mid-to late 1990s

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• Metadata becomes more relevant to corporations who were struggling to understand their information resources caused by: – Y2K deadline – Emerging data warehousing initiatives – Growing focus around the World Wide Web

• Beginning of efforts to try to standardize Metadata definition and exchange between applications in the enterprise

• Examples of standardization: – 1995: CASE Definition Interchange Facility (CDIF) – 1995: Dublin Core Metadata Elements – 1994 – 1999: First parts of ISO 11179 standard for Specification and

Standardization of Data Elements were published – 1998: Common Warehouse Metadata Model (CWM) – 1995: Metadata Coalitions’ (MDC) Open Information Model – 2000: Both standards merged into CSM. Many Metadata repositories

began promising adoption of CWM standard

Metadata History: 21st Century

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• Update of existing Metadata repositories for deployment on the web

• Introduction of products to support CWM • Vendors begin focusing on Metadata as an additional product

offering • Few organizations purchase or develop Metadata repositories • Effective enterprise-wide Managed Metadata Environments

are rare due to: – Scarcity of people with real world skills – Difficulty of the effort – Less than stellar success of some of the initial efforts at some

companies – Stagnation of the tool market after the initial burst of interest in late 90s – Still less than universal understanding of the business benefits – Too heavy emphasis on legacy applications and technical metadata

Metadata History: Current Decade

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• Focus on need for and importance of metadata • Focus on how to incorporate Metadata beyond traditional

structured sources and include semistructured sources • Driving factors:

– Recent entry of larger vendors into the market – Challenges related to addressing regulatory requirements, e.g.

Sarbanes-Oxley, and privacy requirements with unsophisticated tools – Emergence of enterprise-wide initiatives, e.g. information

governance, compliance, enterprise architecture, automated software reuse

– Improvements to the existing Metadata standards, e.g. RFP release of new OMG standard Information Management Metamodel (IMM), which will replace CWM

– Recognition at the highest levels that information is an asset that must be actively and effectively managed

Why Metadata Matters

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• They know you rang a phone sex service at 2:24 am and spoke for 18 minutes. But they don't know what you talked about.

• They know you called the suicide prevention hotline from the Golden Gate Bridge. But the topic of the call remains a secret.

• They know you spoke with an HIV testing service, then your doctor, then your health insurance company in the same hour. But they don't know what was discussed.

• They know you received a call from the local NRA office while it was having a campaign against gun legislation, and then called your senators and congressional representatives immediately after. But the content of those calls remains safe from government intrusion.

• They know you called a gynecologist, spoke for a half hour, and then called the local Planned Parenthood's number later that day. But nobody knows what you spoke about. – https://www.eff.org/deeplinks/2013/06/why-metadata-matters

Metadata Strategy

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• Metadata Strategy is – A statement of direction in Metadata management by the enterprise – A statement of intend that acts as a reference framework for the development

teams – Driven by business objectives and prioritized by the business value they bring to

the organization

• Build a Metadata strategy from a set of defined components • Primary focus of Metadata strategy

– gain an understanding of and consensus on the organization’s key business drivers, issues, and information requirements for the enterprise Metadata program

• Need to understand how well the current environment meets these requirements now and in the future

• Metadata strategy objectives define the organization’s future enterprise metadata architecture and recommend logical progression of phased implementation steps

• Only 1 in 10 organizations has a documented, board approved data strategy

Metadata Strategy Implementation Phases

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Polling Question #2

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• Compliance laws have influenced my organization to pay more attention to and/or put more resources into:

a) Data quality improvement efforts 29% b) Metadata management efforts 6% c) Database management, in general 27% d) No impact 13%


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Goals and Principles

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• Provide organizational understanding of terms and usage

• Integrate Metadata from diverse sources

• Provide easy, integrated access to metadata

• Ensure Metadata quality and security

Activities

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• Understand Metadata requirements

• Define the Metadata architecture

• Develop and maintain Metadata standards

• Implement a managed Metadata environment

• Create and maintain metadata

• Integrate metadata

• Management Metadata repositories

• Distribute and deliver metadata

• Query, report and analyze metadata


Activities: Metadata Standards Types

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• Two major types: – Industry or

consensus standards

– International standards

• High level framework can show – How standards are

related – How they rely on

each other for context and usage


Activities: Noteworthy Metadata Standards Types

Warehouse Process Warehouse Operation

Transformation OLAP Data Mining

Information Visualization

Business Nomenclature

Object Model Relational Record Multidimensional XML

Business Information Data Types Expression Keys and

IndexesType

MappingSoftware

Deployment

Object Model

Management

Analysis

Resource

Foundation

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• Common Warehouse Metadata (CWM): • Specifies the interchange of Metadata among data

warehousing, BI, KM, and portal technologies. • Based on UML and depends on it to represent object-

oriented data constructs. • The CWM Metamodel

Information Management Metamodel (IMM)

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• Object Management Group Project to replace CWM

• Concerned with: – Business Modeling

• Entity/relationship metamodel

– Technology modeling • Relational Databases • XML • LDAP

– Model Management • Traceability

– Compatibility with related models

• Semantics of business vocabulary and business rules

• Ontology Definition Metamodel

• Based on Core model • Used to translate from

one model to another

Primary Deliverables

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• Metadata repositories

• Quality metadata

• Metadata analysis

• Data lineage

• Change impact analysis

• Metadata control procedures

• Metadata models and architecture

• Metadata management operational analysis


Roles and Responsibilities

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• Suppliers: – Data Stewards – Data Architects – Data Modelers – Database Administrators – Other Data Professionals – Data Brokers – Government and Industry Regulators

• Participants: – Metadata Specialists – Data Integration Architects – Data Stewards – Data Architects and Modelers – Database Administrators – Other DM Professionals – Other IT Professionals – DM Executives – Business Users

• Consumers: – Data Stewards – Data Professionals – Other IT Professionals – Knowledge Workers – Managers and Executives – Customers and Collaborators – Business Users


Technology

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• Metadata repositories • Data modeling tools • Database management systems • Data integration tools • Business intelligence tools • System management tools • Object modeling tools • Process modeling tools • Report generating tools • Data quality tools • Data development and administration tools • Reference and mater data management tools


Polling Question #3

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• Do you use metadata models and/or modeling tools to support your information quality efforts? a) Yes 49% b) No 39%


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15 Guiding Principles

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1. Establish and maintain a Metadata strategy and appropriate policies, especially clear goals and objectives for Metadata management and usage

2. Secure sustained commitment, funding, and vocal support from senior management concerning Metadata management for the enterprise

3. Take an enterprise perspective to ensure future extensibility, but implement through iterative and incremental delivery

4. Develop a Metadata strategy before evaluating, purchasing, and installing Metadata management products

5. Create or adopt Metadata standards to ensure interoperability of Metadata across the enterprise

6. Ensure effective Metadata acquisition for internal and external metadata

7. Maximize user access since a solution that is not accessed or is under-accessed will not show business value


15 Guiding Principles, continued

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8. Understand and communicate the necessity of Metadata and the purpose of each type of metadata; socialization of the value of Metadata will encourage business usage

9. Measure content and usage 10.Leverage XML, messaging and web services 11.Establish and maintain enterprise-wide business involvement

in data stewardship, assigning accountability for metadata 12.Define and monitor procedures and processes to ensure

correct policy implementation 13.Include a focus on roles, staffing,

standards, procedures, training, & metrics 14.Provide dedicated Metadata experts

to the project and beyond 15.Certify Metadata quality



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• Example: – iTunes Metadata

• Insert a recently purchased CD

• iTunes can: – Count the

number of tracks (25)

– Determine the length of each track

6609/10/12

Example: iTunes Metadata

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6709/10/12


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• When connected to the Internet iTunes connects to the Gracenote(.com) Media Database and retrieves: – CD Name – Artist – Track Names – Genre – Artwork

• Sure would be a pain to type in all this information

6809/10/12


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• To organize iTunes – I create a "New

Smart Playlist" for Artist's containing "Miles Davis"


6909/10/1279


• Notice I didn't get the desired results

• I already had another Miles Davis recording in iTunes

• Must fine-tune the request to get the desired results

– Album contains "The complete birth of the cool"

• Now I can move the playlist "Miles Davis" to a folder


7009/10/1280


• The same: –Interface –Processing –Data Structures

• are applied to –Podcasts –Movies –Books –.pdf files

• Economies of scale are enormous


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Uses

Metadata Take Aways

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• Metadata unlocks the value of data, and therefore requires management attention [Gartner 2011]

• Metadata is the language of data governance • Metadata defines the essence of integration challenges

Sources Metadata Governance

Metadata Engineering

Metadata Delivery

Metadata Practices

Metadata Storage


Data Management Body of Knowledge

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Data Management

Functions

Metadata Management Summary


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References & Recommended Reading

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References, cont’d

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Polling Question #4

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• My organization began using or is planning to use a metadata repository (purchased or homegrown)

a) Last year (2013) b) This year (2014) c) Next year (2015) d) Not applicable

Questions?

It’s your turn! Use the chat feature or Twitter (#dataed) to submit

your questions to Peter now.

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Upcoming Events

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Data Systems Integration & Business Value Pt. 2: Cloud August 13, 2013 @ 2:00 PM ET/11:00 AM PT

Data Systems Integration & Business Value Pt. 3: Warehousing September 10, 2013 @ 2:00 PM ET/11:00 AM PT

Sign up here: www.datablueprint.com/webinar-schedule or www.dataversity.net

data-ed: metadata strategies

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